Annual/Termination Reports:

[04/27/2012] [05/30/2013] [09/18/2014] [10/02/2015]

Report Information

Participants

Brief Summary of Minutes

Minutes of NCERA-180 committee meeting
(Precision Agriculture Technologies for Food, Fiber, and Energy Production)
March 28  30th, 2012
Grace Inn hotel, Maricopa, Arizona.

March 28 (Field Tour summary provided by Lei Tian)
A field tour was organized as per the following schedule:

First stop: Maricopa-Stanfield Irrigation and Drainage District (in Maricopa).
Leave Grace Inn hotel in Phoenix by 8:00am.

Presentation by Mr. Brian Betcher, General Manager of the Maricopa-Stanfield Irrigation and Drainage District. Maricopa Stanfield Irrigation & Drainage District (MSIDD) formed in 1962 for the purpose of providing irrigation water for agricultural use. The District is located in Pinal County. The irrigation system involves over 200 miles of distribution facilities including concrete-lined canals, pipelines, pumping plants and related works. MSIDD receives its surface water from the Central Arizona Project (CAP). The Districts CAP allocation is 110,000, to 120,000 AF per year, this is supplemented with groundwater provided by wells operated and maintained by the District since 1989. The group toured the controller room and went to the field, looked at the distribution facilities.

Second stop: Biosphere 2 (near Tucson, 2 hr drive from Phoenix). Tour guided by Dr. Kevin Fitzsimmons - University of Arizona.
The current Biosphere 2 functions as a department of the University of Arizona College of Science. Within Biosphere 2 there are two divisions, B2 Earthscience and B2 Institute. B2 Earthscience is utilizing the unique attributes of Biosphere 2 to conduct research that cannot be accomplished anywhere else, by anyone else. Another important component of Biosphere 2 is Education and Outreach. This overlaps both B2 Earthscience and B2 Institute to provide educational experiences and active involvement for students and community members.

Return to hotel in Phoenix around 6:00pm


March 29 (Presentation and field demonstration of advanced technologies)

Pedro Andrade hosted the seconds meeting at Cardon Bldng, Maricopa Agricultural Center.

First, a welcome address delivered by Dr. Ron Allen, Director of Experiment Station, University of Arizona

The meeting then moved to presentation of technical topics:

1. A group Presentation given by Doug Hunsaker, Andrew French, Kelly Thorp, Eduardo Bautista from USDA-ARS Arid Land Agricultural Research Center and Peter Waller from University of Arizona: Decision support for managing spatial and temporal variations of crop water use, soil, and irrigation systems: Linking real-time remote sensing observations and ground-based data with crop and irrigation models.

Declining water tables, drought, and rapid urban growth in the arid regions of the southwestern United States are causing excessive water demand. Thus, developing improved agricultural water-saving practices and technologies is more important than ever to meet the water availability challenge. A multidisciplinary team effort on real-time sensors, geo-spatial data processing/modleing, and decision support water management system development was presented.

2. Randy Norton - University of Arizona: Sensor-based nematode control in irrigated cotton.

Effective control of Southern root knot nematode looks promising with the use of GPS-controlled, variable-rate applications of soil fumigants. This technology illustrates that nematicide applications can be applied sparingly in some cases while maintaining good nematode control and trimming chemical costs.

3. Yufeng Ge  Texas A&M University: Ground-based technologies for control of cotton root rot

Early detection could facilitate a more economical solution than those that might be used after plant infection had become more severe and widespread. Three cotton fields around CRR-prone areas of Texas have been the sites for two years of data collection.

4. Tom Mueller - University of Kentucky: Mobile app: Kentucky Land Use Data Cloud

The underutilization of publicly available land-use assessment datasets has led to long-term negative economic and environmental consequences. The objective of this project is to develop methods for making public land-use assessment data more easily available over the internet for viewing in Google Maps and Google Earth.

5. Howard Wuertz - Sundance Farms (Coolidge, AZ): Grower perspectives on adoption of precision agriculture technologies in AZ.

6. Mark Siemens, Yuma Ag Center, University of Arizona: Advanced Technologies in Vegetable Production - Precision lettuce thinner

A machine vision guided automatic thinning machine was developed and demonstrated.

7. Kurt Nolte, Yuma Ag Center, University of Arizona: Advanced Technologies in Vegetable Production - Lettuce yield monitoring and traceability

A practical lettuce yield monitoring system was proposed and tested in the real production environment.

The afternoon meeting included several field demonstrations:

1. soil and plant sensors. Dual wavelength-EC-force probe, and EC-OpticMapper. Eric Lund - Veris Technologies.

2. Display of field deployment of spectral and plant height sensors in sensor platform. Pedro Andrade - University of Arizona.

3. Equipment demonstrations: a) HemisphereGPS  New generation of on-board computers
b) CNH/Trimble  Telemetry applications in agricultural machines

4. Industry updates  Software solutions for crop management:
a) CDMS Advisor , b) HemisphereGPS AgJunction



March 30th, Business Meeting: Sahuaro Room in the Grace Inn hotel

Meeting started with Raj Khoslas (Colorado State University) report on ISPA and the 11th ICPA conference in Indianapolis.

Raj Koshla: Report on ISPA (upcoming election of new officers). Precision ag exploding worldwide (12 international divisions of ISPA). 26 countries represented (2 reps per country. Membership in the 200s

Report on ICPA. Volunteers needed for ICPA meeting in Indianapolis to serve as moderators and judges. Grad students nominations are encouraged (online applications with support letter.

Then, Richard Ferguson of University of Nebraska-Lincoln given an update on IUSS PSS workgroup activities. Discussion on the first results related to vertically integrated precision agriculture initiative for a large-scale crop production system.


Fran Pierce: Report on ASA Symposia (October 24) that will cover the topics: Understanding yield variability across spatial and temporal scales; sensor-based water management; and robotic weed control.

The meeting then move to the NIFA Report given by Dan Schmoldt from USDA Washington office through Remote connection. Van C. Kelley is also participated the business meeting through remote connection.


Combined with the selection of next hosting institution, the next session was the discussion on the future of NCERA-180: Academic Advisor, review of research/education objectives, membership promotion, future meetings, working groups, collaboration, etc.

Session moderated by Fran Pierce:

Discussion of NCERA-180 meeting location in 2014:

Raj Koshla: second the proposal of Washington State (Manoj Karkee). All members present voted yes. Remote vote from Dan Schmoldt (yes) and emails from Tim Stombaugh, Ken Sudduth, Van C. Kelley, and Slava Adamchuck.

Fran Pierce moderated discussion on the future of NCERA-180. Some comments:

Ken Sudduth: Agree with the discussion that turning data into valid management recommendations is the bottleneck. How can we help with this? Regional analysis of data? Global soil spectroscopy data analysis by Raphael and regional sensor N project are examples.

Viacheslav Adamchuk: Unique features of NCERA-180  diversification (multidisciplinary, national/international, academic/agencies/agribusiness), size that enables steering function, great history. Main agenda  analysis of limitations and formulation of strategic initiatives

Bruce Erickson: I will be glad to contribute. With my new position with ASA, we have an excellent webinar delivery system and would be pleased to work with this group to deliver educational opportunities


Fran Pierce: 18 years ago we started with the idea of talking with the each others. The industry changes a lot, more complexes and evolved. Is there a reason for NCERA-180 to continue on? We get some structures &

Raj Koshla: (We got a) good skeleton to start with. We just need more participants. Move the business to the first day.

Shrini Upadhyaya: Come up with some topics, for the first day discussions. 1) Is science for the precision Farming there yet?

Tom Mueller: 2) the role Information technologies

Lei Tian: 3) Data standardization in industry to help decision making process

Shrini Upadhyaya: Someone take these topics, definition of the topics, the same thing like PM 54 in ASABE & We are a more diverse group. We can brain storming & Illinois starts to do something and start the in depth discussion: are we relevant to the industry &


March 30th, After the business meeting there was an Extra activity:

Visit to Arizona State University, GeoDa Center for Geospatial Analysis and Computation.

Accomplishments

NCERA-180 Summary of Accomplishments 2011-2012<br /> <br /> Research<br /> <br /> NCERA-180 activities and the relationships formed through these activities have facilitated research and extension accomplishments. The following is a brief summary of some of these activities as reported by participants from different states:<br /> <br /> Research Projects reported from Arizona:<br /> Precision canopy and water management of specialty crops through sensor-based decision making. This project uses proximal sensors mounted on a mobile platform to provide the information desired by stakeholders. These include information on canopy architecture and light interception using Pohtosynthetically Active Radiation (PAR) sensors, plant-soil water status using a sensor suite consisting of a thermal IR gun, ambient temperature, humidity and wind speed sensors. Moreover, this project aims to develop data visualization software and a decision support system to assist with management decisions.<br /> <br /> Non-destructive estimation of cotton plant growth and Nitrogen status. Research funded by Cotton Inc. Description: In this project we are carrying out a series of research activities that combine plant parameters with existing sensor technology. ultispectral radiometer data can be generated to test the performance of spectral-based indices in their ability to estimate leaf area of upland cotton. Moreover, we are using ultrasonic displacement sensors to estimate the height of plants on-the-go. Plant height is coupled with the weather-based estimation of the number of nodes to compute cotton height: node ratio.<br /> <br /> Assessment of hail damage in cotton using active-light spectral sensors. Research funded by National Crop Insurance Services. Description: This project is about using sensor technology to make a quick assessment of the amount of canopy and rate of recovery after a simulated hail event. Impact: This project will give information on sensor-based spectral indices that can represent the extent of damage of cotton plants and generate savings to the crop insurance industry.<br /> <br /> Sensor-based Management of Mid-season N Fertilizer in Durum Wheat. Research funded by Arizona Grain Research and Promotion Council. Description: The purpose of this study is to relate soil and plant Nitrate measurements with the response of spectral sensors during the growing season. The overall objective is to determine if it is possible to capture the response to in-season Nitrogen with spectral and plant-height sensors. Yield and grain quality will be analyzed as a function of input application.<br /> <br /> Characterization of spatial variation in wheat yield and protein using soil and plant sensors. Research funded by Arizona Grain Research and Promotion Council. Description: An improved scheme of field-level research will be carried out with particular attention to capturing the dynamics of soil/plant Nitrogen. This will be achieved with soil/plant sampling for laboratory analysis of N status at tillering, jointing, booting, and flowering of durum wheat; along with spectral measurements of the crop using hand-held instruments.<br /> <br /> Improving Arizona tree crop weed management. Description: This project will evaluate newly registered pre-emergence herbicides to determine how many post-emergence herbicide sprays can be eliminated annually and to develop herbicide programs that minimize the risk of developing herbicide resistant weeds by measuring the light reflectance characteristics of the orchard floor and obtain the technical data needed to develop a more robust automatic spot spraying system.<br /> <br /> Soil compaction reduction of date yields. Research funded by Arizona Department of Agriculture - Specialty Crop Grant Block Program. Description: Date palms have a shallow root system that differs from most tree crops. This projects aims at characterizing the dynamics of soil strength and root growth through the growing season, and establish the nature of the relationship between compaction levels and yield components, especially date quality.<br /> <br /> <br /> Research Projects reported from Florida<br /> Three different machine vision algorithms were developed to detect and count immature green citrus fruits in natural canopies using regular digital color images: (1) Fast Fourier Transform (FFT) leakage, (2) color, circular Gabor texture analysis and eigenfruit approach, and (3) shape analysis, support vector machine, and scale invariant feature transform. Average detection accuracies were approximately 80%. <br /> <br /> Spectral characteristics of blueberry fruit and leaves were investigated. Different fruit detection algorithms (classification tree, principal component analysis, and multinomial logistic regression) were developed and yielded detection accuracies of 98%-100% for fruit and leaf. <br /> <br /> In 2011, we continued to develop different detection algorithms for the citrus greening disease or Huanglongbing (HLB) using aerial multispectral and hyperspectral images acquired in 2007 and 2010. We observed that healthy canopy had higher reflectance in the visible range, and lower reflectance in the NIR range than HLB infected canopy. Red edge position yielded a detection accuracy of over 90% for infected ground spectra, however did not work well for aerial hyperspectral images due to low spatial resolution. Several detection methods were applied, and their accuracies were over 60% for most of them. Disease density maps were created, and most of the methods were able to identify severely infected areas. These maps could provide an effective way to manage the citrus greening disease.<br /> <br /> For citrus mechanical harvesting with a continuous canopy shake and catch harvester, a machine vision system was installed in a citrus debris cleaning machine to estimate citrus fruit mass, fruit count, and fruit size during postharvesting towards the development of an advanced citrus yield mapping system. Different image processing algorithms were developed to identify fruit from images of the postharvest citrus using logistic regression and the H-minima transform based Watershed algorithm. A special algorithm, named a highly saturated area recovering algorithm, was developed to avoid misclassification due to highly saturated area in fruit regions. A coefficient of determination of 0.945 was obtained between the actual and the estimated fruit mass with a root mean square error of 116 kg. Fruit sizes were also estimated after applying the Watershed algorithm. <br /> <br /> A machine vision algorithm was developed for automatically estimating mass of debris in a citrus canopy shake and catch harvester. Debris materials are non-citrus objects such as leaves, twigs and branches which are mechanically harvested along with citrus fruit. The algorithm included a special step for removing undesired debris on the ground using a novel Parse and Add algorithm. The estimation algorithm yielded coefficients of determination between the pixel area and debris mass of 0.815 and 0.78 for test bench experiments and field testing, respectively. A geo-referenced map of the debris mass was created, which could play an important role in solving the problem of safe and economical disposal of diseased leaves and twigs. <br /> <br /> <br /> Research Projects reported from North Dakota<br /> Use of the Greenseeker and Holland Crop Circle Sensors to predict in-season N needs of field corn.<br /> <br /> Correlation of Greenseeker and Holland Crop Circle Sensors with Rapid-Eye satellite data to be predictive of sugarbeet leaf N content, sugarbeet yield and sugar content; to be predictive of post-anthesis foliar N application for protein enhancement in spring wheat; to be predictive of side-dress N needs of field corn and sunflower.<br /> <br /> Incorporation of plant height data in predictive N requirements of sugarbeet, field corn and sunflower.<br /> <br /> Estimation of energy savings using GPS and Auto-guidance systems in farm vehicles and development of a Crop height sensor for integration into active optical sensor readings for corn<br /> <br /> In class conducted a project to explore the spatial distribution of phosphorus and other nutrients in two Fargo dog parks. The results are being submitted to an urban horticulture journal.<br /> <br /> There is also a new three-year NSF grant to explore the correlation of active optical sensors with satellite imagery with sunflower, spring wheat, corn and sugarbeet.<br /> <br /> Research Projects reported from Illinois<br /> Precision agriculture for biomass production: Adapt precision agriculture technologies specifically for computerized planning, sensing, decision support/making, and managing of energy crop field production. This requires the establishment of an informatics platform for storing, analyzing (including modeling and simulation), and delivering biological and engineering information. Multi-platform sensing systems were developed for biomass production monitoring: stand-alone image tower system, the UAV imaging system and the close proximity crop field sensing-gantry. Two years of remote sensing data have been collected. Data processing results shown that the in season biomass yield is closely related to real-time remote sensing data.<br /> <br /> Data processing and DSS for precision farming: In this study, we are optimizing both the sensing process and data to knowledge (D2K) conversion process. Automatic and supervised learning processes have been applied on a large database of agricultural crop systems. The objectives are to eventually understand the complicated system by means of processing a massive database with the state-of-the-art high performance computing systems. <br /> <br /> Extension<br /> <br /> Extension Education for Ag Professionals: Pilot Project . University of Arizona - Extension Office. Participation: Providing training to crop consultants in the use of GPS technology. <br /> <br /> Audio-visual extension shorts for field crop clients in Arizona. University of Arizona - Extension Office. Participation: Preparation of short videos on spraying technology (calibration, controller operation) and use the multi-function displays. <br /> <br /> Characterizing plant height, canopy temperature and reflectance for high-thruput phenotyping. Extensive testing of proximal sensing techniques with high-clearance ground systems in a field-based approach. Collaboration with scientists of the USDA-ARS ALARC in Maricopa AZ.<br /> <br /> Cotton yield monitoring in commercial fields- 2011. Installation, training and data analysis of cotton yield data. Systems included John Deere and Case-IH. These systems required interfacing GPS to collect geo-referenced yield data in several fields in Buckeye, Paloma, Maricopa and Marana AZ.<br /> <br /> Yield measurement of forage crops in small-scale plots. Installed load sensors in round-baler, along with GPS receiver and data logger. Recorded switch grass yield in both static and dynamic conditions in a variety trial in Maricopa AZ.<br /> <br />

Publications

Andrade-Sanchez P. and Heun J.T. 2012. From GPS to GNSS: Enhanced functionality of GPS-integrated systems in agricultural machines. Bulletin AZ1558. The University of Arizona - Cooperative Extension. Tucson, Arizona 85721<br /> <br /> Andrade-Sanchez P. and Heun J.T. 2011. A general guide to Global Positioning Systems (GPS)  Understanding operational factors for agricultural applications in Arizona. Bulletin AZ1553. The University of Arizona - Cooperative Extension. Tucson, Arizona 85721<br /> <br /> Andrade-Sanchez P. and Heun J.T. 2010. Things to know about applying precision agriculture technologies in Arizona. Bulletin AZ1535. The University of Arizona - Cooperative Extension. Tucson, Arizona 85721<br /> <br /> Bansal, R., W. S. Lee, R. Shankar, and R. Ehsani. 2010. Automated trash estimation in a citrus canopy shake and catch harvester using machine vision. ASABE Paper No. FL10-123. St. Joseph, Mich.: ASABE.<br /> <br /> Bansal, R., W. S. Lee and S. Satish. 2011. Green citrus detection using Fast Fourier Transform (FFT) leakage. 8th European Conference on Precision Agriculture (ECPA), July 11-14, 2011, Prague, Czech Republic.<br /> <br /> Bansal, R., W. S. Lee, R. Shankar, and R. Ehsani. 2011. Automated debris mass estimation for citrus mechanical harvesting systems using machine vision. Applied Engineering in Agriculture 27(5): 673-685.<br /> <br /> Han, Y., W. S. Lee, C. Lee, S. Park, K. Kim, and S. Kim. 2011. Entrapment of Mg-Al layered double hydroxide in calcium alginate beads for phosphate removal from aqueous solution. Desalination and Water Treatment. 36: 178-186. <br /> <br /> Jeffrey W. White; P. Andrade-Sanchez; M. A Gore; K. F Bronson; T A Coffelt; M M Conley; K A Feldmann; A N French; J T Heun; D J Hunsaker; M A Jenks; B A Kimball; R L Roth; R J Strand; K R Thorp; G W Wall; G. Wang. 2012. Field-based phenomics for plant genetics research. Field Crops Research (in press).<br /> <br /> Jeon H., L. Tian and H. Zhu. 2011. Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination. Sensors 2011, 11, 6270-6283; doi: 10.3390 /s110606270<br /> <br /> Jones, C. D., J. B. Jones, and W. S. Lee. 2010. Diagnosis of bacterial spot of tomato using spectral signatures. Computers and Electronics in Agriculture 74(2):329-335.<br /> <br /> Kumar, A., W. S. Lee, R. Ehsani, L. G. Albrigo, C. Yang, and R. L. Mangan. 2010. Citrus greening disease detection using airborne multispectral and hyperspectral imaging. 10th International Conference on Precision Agriculture. July 18-21, 2010, Hyatt Regency Tech Center, Denver, Colorado.<br /> <br /> Kurtulmus, F., W. S. Lee, and A. Vardar. 2011. Green citrus detection using eigenfruit, color and circular Gabor texture features under natural outdoor conditions. Computers and Electronics in Agriculture 78(2): 140-149.<br /> <br /> Kurtulmus, F., W. S. Lee, and A. Vardar. 2011. An advanced green citrus detection algorithm using color images and neural networks. 11th International Congress on Mechanization and Energy in Agriculture, Sep. 21-23, 2011, Istanbul, Turkey. Journal of Agricultural Machinery Science, 7(2): 145-151. <br /> <br /> Lee, W. S. 2011. Research on auto-guidance system and their commercialization for U. S. agricultural production. Dec. 16, 2011. Seoul National University, Seoul, Korea.<br /> <br /> Lee, W. S., V. Alchanatis, C. Yang, M. Hirafuji, D. Moshou, and C. Li. 2010. Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture 74(1): 2-33.<br /> <br /> Lee, W. S. 2010. The current situation and R&D trends of agricultural machinery industry in the U.S. First International symposium on the current situation and R&D trends of the world agricultural machinery industries, June 28-30, 2010, Chonbuk National University, Jeonju, Korea. <br /> <br /> Li, X., W. S. Lee, M. Li, R. Ehsani, A. Mishra, C. Yang, and R. Mangan. 2011. Comparison of different detection methods for citrus greening disease based on airborne multispectral and hyperspectral imagery. ASABE Paper No. 1110570. St. Joseph, Mich.: ASABE.<br /> <br /> Patil, R., W. S. Lee, R. Ehsani, and F. Roka. 2010. Elimination of debris using de-stemmers on a continuous citrus canopy shake and catch harvester. ASABE Paper No. 1008384. St. Joseph, Mich.: ASABE.<br /> <br /> Wang, Q., Q. Zhang, F. Rovira-Más, L. Tian. 2011. Stereovision-based lateral offset measurement for vehicle navigation in cultivated stubble fields. Biosystems Engineering. Volume 109, Issue 4, August 2011, Pages 258265<br /> <br /> Xiang, H. and L. Tian. 2011. Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform. Biosystems Engineering, Volume 108, Issue 2, February 2011, Pages 104-113<br /> <br /> Xiang, H. and L. Tian. 2011. Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosystems Engineering. Volume 108, Issue 2, February 2011, Pages 174190<br /> <br /> Xiang, H. and L. Tian. 2011. An automated stand-alone in-field remote sensing system (SIRSS) for in-season crop monitoring Computers and Electronics in Agriculture. Vol. 78 (1), August 2011, Pages 18<br /> <br /> Xiong, Y., L. Tian, T. Ahamed, B. Zhao. 2012. Development of the Reconfigurable Data Acquisition Vehicle for Bioenergy Crop Sensing and Management. ASME Journal of Mechanical Design. (Jan, 2012) Vol. 134 / 015001-1-7<br /> <br /> Yang, C. and W. S. Lee. 2011. Spectral signatures of blueberry fruits and leaves. ASABE Paper No. 1110582. St. Joseph, Mich.: ASABE.<br /> <br /> Zhao, B., L. Tian and T. Ahamed. 2010. Real-Time NDVI Measurement Using a Low-Cost Panchromatic Sensor for a Mobile Robot Platform. Environment Control in Biology, Vol. 48 (2010) No. 2, pp.73-79<br /> <br /> Invited Seminars<br /> Using Precision Agriculture technologies to increase the efficiency of mechanized operations in Arizona. Presentation during Brown Bag Seminar Series at the USDA ARS Arid Land Agricultural Research Center (ALARC). Maricopa, AZ 5/16/2011.<br /> <br /> Precision agriculture in the US semi-desert. Presentation for the Club of Progressive Farmers. University of California Cooperative Extension Riverside County. Blythe, CA 10/20/2011.<br /> <br /> Sensor-based management of cotton in Arizona. Seminar presented for students of Graduate Seminar ABE 696 Department of Agricultural & Biosystems Engineering. Tucson, AZ 11/28/2011.<br /> <br /> Applied research and extension in precision agriculture of the United States. Mexican Association of Sugar-cane Producers. Oaxaca, Oaxaca, Mexico 12/7/2011.<br /> <br /> Presentations during extension and outreach events<br /> "Using precision guidance to improve mechanical weed control in cotton. Cotton Mid Season Meetings. Coolidge AZ 6/15/11, Ak-Chin AZ 6/23/11, Buckeye 6/28/11 AZ, Marana AZ 7/11/11.<br /> <br /> "Using GPS for Pesticide Applications" Cotton Mid Season Meeting. Parker AZ 8/24/11.<br /> "Cotton yield monitors: Available systems, installation and data management" Cotton Late Season Meeting. Buckeye AZ 7/28/11.<br /> <br /> "Technical considerations when spraying defoliants in Cotton" Cotton Late Season Meetings. Yuma Agricultural Center. Yuma, AZ, 6/21/11.<br /> <br /> Demonstration on cotton yield monitor technology. Maricopa Agricultural Center Annual Field Day. Maricopa AZ 9/29/2011.<br />

Impact Statements

1. Organized ISPA Conference, the Precision Agriculture technical sessions for the 2011 ASABE Conference, the IUSS PSS workgroup, the ASA Symposia, etc.
2. NCERA members continued the mission to advance the science of precision agriculture globally. The International Society of Precision Agriculture (ISPA) is a non-profit professional scientific organization. Up till now, Precision ag exploding worldwide (12 international divisions of ISPA). 26 countries represented (2 reps per country).
3. Progress has been made in remote sensing applications in precision agriculture: a) for HLB detection, spectral characteristics of healthy and HLB infected canopies were analyzed. HLB detection accuracies ranged from 43% to 95% depending on different algorithms; b) for biomass yield measurement, using near-real-time remote sensing data to estimate the biomass yield (dry mass) the accuracy is more than 60%.
4. A New Model to Enhance Stakeholder Input, Program Planning and Outreach to Agricultural Clientele. Signature Program funded by the University of Arizona  College of Ag and Life Sciences.
5. NCERA members continued in precision agriculture research and examples of specific impacts can be found in the "Accomplishments" section.

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Report Information

Participants

Day 1 (3/27/2013): Tours

Joe Luck (University of Nebraska)
Harold Reetz (Reetz Agronomics)
Richard Ferguson (University of Nebraska)
Pedro Andrade-Sanchez (University of Arizona)
Sid Parks (Growmark0)
Liujun Li (University of Illinois Urbana-Champaign)
Lei Tian (University of Illinois Urbana-Champaign)


Day 2 (3/28/2013): Seminars

Van Kelly (South Dakota State University)
Newell Kitchen (USDA-ARS)
Earl Vories
Richard Ferguson (University of Nebraska)
Pedro Andrade-Sanchez (Universeity of Arizona)
Ganesh Bora (North Dakota State Unviersity)
Dharmendra Saraswat (University of Arkansas)
Ken Sudduth (USDA-ARS)
Sid Parks (Growmark)
Todd Peterson ()
Alexandra Kravchenko ()
Manoj Karkee (Washington State University)
Lei Tian (University of Illinois Urbana-Champaign)
Liujun Li (University of Illinois Urbana-Champaign)
Viacheslav Adamchuk (McGill University)
Tom Mueller (University of Kentucky)
John Reifsteck
Neal Merchen
Pradip Das (Monsanto Company)
KC Ting (University of Illinois Urbana-Champaign)
Randall Sandone (Riverside Research)
Shrini Upadhyaya (University of California, Davis)
Day 3 (3/29/2013): Business Meeting

Daniel Schmoldt (USDA-NIFA)
Shrini Upadhyaya (University of California, Davis)
Van Kelly (South Dakota State University)
Newell Kitchen (USDA-ARS)
Earl Vories
Richard Ferguson (University of Nebraska)
Pedro Andrade-Sanchez (Universeity of Arizona)
Ganesh Bora (North Dakota State Unviersity)
Dharmendra Saraswat (University of Arkansas)
Ken Sudduth (USDA-ARS)
Sid Parks (Growmark)
Todd Peterson ()
Alexandra Kravchenko ()
Manoj Karkee (Washington State University)
Lei Tian (University of Illinois Urbana-Champaign)
Liujun Li (University of Illinois Urbana-Champaign)
Tom Mueller (University of Kentucky)
Harald Reetz (Reetz Agronomics)

Brief Summary of Minutes

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Roles and Responsibilities or the Group:
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It was argued that this group is good to define where are we going in term of PA; the group also brings potential for young faculty members and scientists; The group provided an opportunity for young researchers to interact with seasoned researchers; we may want to have a mix of senior and young researchers; it would be great if we can attract more young scientists.

It is a good idea to bring industry into the meeting and listen what they have to say; Interaction between industry and academia is crucially important to be successful in PA research, extension and tech demos; it was also pointed out that we may have to be a bit more tolerant and be patient on industrys efforts on PA. It would be interesting to know more on what the big companies such as Monsanto and Deere are doing in PA and other advanced technologies for agriculture?

The participants also pointed out that cross-disciplinary nature and national scope of this group was unique and vital and we should be soliciting on maintaining such cross disciplinary partnership.

It was emphasized that we need to define and focus on performance matrix and define what is that we are trying to achieve. What we need to do to make positive and significant impact?

There was also a discussion on potentially developing a whitepaper after each annual meeting including statements on what we want to achieve?, what is the greatest need for research, education and extension?, What precision ag extension agents need to do? The outcome would be to redefine what needs to be done in the future.

Educational material such as corn and soil adviser apps form Arkansas came through some discussion from this meeting; similar products can be planned.

Other common outcomes would be books, new grants, research opportunities, and collaborations that come out of this meeting; It was suggested by Dan Schmoldt that self-organizing for publications, collaboration for proposals; white papers; and developing and sharing instrumentations could some areas to explore for this group.

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Participation and Future Meetings:
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It was suggested that we need to focus more on issues than presentations during annual meetings.

It was also pointed out that representation and participation in this group is going down; addressing issues and/or agendas of other of group members who have not been attending the meeting should be considered; the chart of attendees for last 10 years would be interesting to see;

One potential for low participation was considered to be the location; Coinciding this meeting with a grower event may increase participation; It was also suggested to co-schedule this meeting with other conferences such as InfoAg conference and ICPA. It was decided to move the venue of 2014 meeting from WA to Sacramento, CA to co-locate and co-schedule with ICPA 2014. It was also suggested to co-locate this meeting with ICPA or similar conferences every other year.

It was suggested that a survey be developed and conducted to identify potential topics for discussion in the 2014 annual meeting. Other possible questions for the survey:

Would you be able to attend or more likely to attend if co-located with ICPA? What would be the way to handle the meeting in new format? Ken Sudduth is in the organizing committee of ICPA 2014. Survey to include suggestions for ICPA 2014 would also be helpful.

Manoj Karkee will be leading the planning for the next meeting. Ken Sudduth will help in booking the room. Shrini Upadhyaya will help organize a few tours in Sacramento/Davis are in collaboration with the tours that may already been offered by ICPA.

For 2015, Joe Luck was nominated and approved to host the meeting in Nebraska. Time for the meeting is open with possibility of organizing it in Lincoln, NE area in April along with PlugFest meeting.

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Discussions on UAVs:
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It was expected that FAA regulation will be clear and UAVs will possibly expand rapidly in ag application area; this expansion will impact our team; In the past, several members of this team have used UAVs for Precision Ag. There is increased interest from other members/universities.

Areas of our interest include selecting suit of sensor that can we put on UAVs; Can we do pollination or other chemical application with it? communication and collaboration among multiple UAVs. Low cost, stable and robust system is needed for ag.

It was pointed out that the use of UAVs will provide a new platform with potential to get better spatial and temporal resolutions and applicability for field level management. However, the underlying problem of how to transfer data into actionable knowledge remains to be a challenge for this team; For example, identifying the nature of plant stress and be able to do something about it is more important;

It was also pointed out that traditional algorithm for data analysis may not work with high resolution dataset collected with UAVs. There may be issues with platform stability, image registration and mosaicing. Concern is also in image quality due to relatively higher speed and control of camera orientation being challenging. Micro copters may give good stable platform, though.

It was suggested that UAV-based Remote Sensing could be a session in ICPA. Shrini Upadhyaya may be able to DEMO a UAV system in UC Davis during 2014 meeting.

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Other activities:
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* Adviser Van Kelley reported on the status and future prospects on multi-state projects and deadlines of this project.
* USDA-NIFA report by Daniel Schmoldt (presentation to be distributed to the team)
* Newell Kitchen provided a brief updates on ASAs activities and meetings

*Seminars on 1) Biomass development project at UIUC, 2) 'Trends and Possibilities in Sustainable Agriculture' by Pradip Das (Monsanto), 3)'GROWMARK and cooperatives' by John Reifsteck, 4) 'ISO Standards for precision ag' by Viacheslav Adamchuk, 5) 'GIS-based vegetative Filter Strip Calculator' by Tom Mueller, 6) 'Indian Creek Watershed' project by Harold Reetz, and 7) 'Use of UAVs by Randall Sandone.

*Tours of 1) University of Illinois Supercomputing Center, 2) Ag and Biosystems Engineering Labs at UIUC, 3) Andersons Fertilizer and Grain Facility, 4)Monsanto Seed Plant (Picture not available)

Accomplishments

The NCERA 180 meetings and collaborations formed through this group has facilitated formation and implementation of various research and extension project in participating institutions. A number of precision agriculture-related projects were conducted last year by the participating researchers with outstanding accomplishments. In the following paragraphs, reports provided by some participating states will be presented.<br /> <br /> --------------------------------------------------------------------------<br /> ----------------------------------Research--------------------------------<br /> <br /> --------<br /> Florida:<br /> --------<br /> Various precision agriculture-related projects were conducted this year by the participats at University of Florida. One such project focused on developing image processing algorithm to identify fruit from images of postharvest citrus fruit from a commercial citrus grove. The system was installed in a citrus debris cleaning machine, which removes debris from mechanically harvested loads. The highest coefficient of determination between measured and estimated fruit masses was 0.95 and the root mean square error was 116 kg per harvested load. Another study for the fruit mass estimation using naive Bayes and artificial neural network yielded an R-square of 0.92, whereas decision tree based mass estimation resulted a value of 0.80. In addition, a novel and simple technique was developed to detect immature green citrus in tree canopy under natural outdoor conditions. Shape analysis, texture classification, SVM, graph based connected component algorithm, and Hough line detection were used to identify fruit and to remove false positives. Keypoints by scale invariant feature transform algorithm were used to further remove false positives. The algorithm was able to detect and count 80% of citrus fruit for validation.<br /> <br /> We also continued to develop different detection algorithms for the citrus greening disease using multispectral and hyperspectral images. Support vector machine (SVM) was able to provide a fast way to build a mask for tree canopy. Disease density maps were created for better management of the disease. A novel detection method called extended spectral angle mapping (ESAM) was developed to detect citrus greening disease using Savitzky-Golay smoothing filter, SVM and vertex component analysis. A portable citrus greening disease detection system was developed using monochrome cameras at two visible bands and polarization characteristics of accumulated starch in disease symptomatic leaves. Various statistical analyses and texture features were utilized. Detection accuracy over 90% was obtained. <br /> <br /> Blueberry fruit detection algorithm was another project conducted in 2012. Multispectral images of near-infrared, red and green bands were used. Various color models along with Bayesian classifier and SVM were applied for identifying different growth stages of fruit. SVM yielded better results with a 84% fruit detection rate than the other classifier.<br /> <br /> Another project the Florida team carried out focused on studying spectral signatures of vine-killed potatoes for grading and sorting with tubers having various diseases and damages. Statistical analyses with partial least squares and stepwise multiple linear regression showed that spectral differences due to the defects were found to be statistically significant, and could be utilized on a packing line.<br /> <br /> --------<br /> Arizona:<br /> --------<br /> <br /> University of Arizona researchers conducted a research project on ' Precision canopy and water management of specialty crops through sensor-based decision making' . This project uses proximal sensors mounted on a mobile platform to provide the information desired by stakeholders. These include information on canopy architecture and light interception using Pohtosynthetically Active Radiation (PAR) sensors, plant-soil water status using a sensor suite consisting of a thermal IR gun, ambient temperature, humidity and wind speed sensors. Moreover, this project aims to develop data visualization software and a decision support system to assist with management decisions. Another project was carried out combine plant parameters with existing sensor technology for cotton growth monitoring. Multispectral radiometer data and ultrasonic displacement sensors were used to estimate the height of plants on-the-go. Plant height is coupled with the weather-based estimation of the number of nodes to compute cotton. <br /> <br /> Another project conducted at University of Arizona was 'Assessment of hail damage in cotton using active-light spectral sensors'. This project was funded by by National Crop Insurance Services and is about using sensor technology to make a quick assessment of the amount of canopy and rate of recovery after a simulated hail event. <br /> <br /> Sensor-based Management of Mid-season N Fertilizer in Durum Wheat. Pedro Andrade-Sanchez and Michael Ottman. Research funded by Arizona Grain Research and Promotion Council. Description: The purpose of this study is to relate soil and plant Nitrate measurements with the response of spectral sensors during the growing season. The overall objective is to determine if it is possible to capture the response to in-season Nitrogen with spectral and plant-height sensors. Yield and grain quality will be analyzed as a function of input application.<br /> <br /> Other projects carried out by the participating researchers include Characterization of spatial variation in wheat yield and protein using soil and plant sensors , Improving Arizona tree crop weed management , and Soil compaction reduction of date yields , <br /> <br /> --------<br /> Alabama<br /> --------<br /> <br /> The agronomic and economic benefits of GPS based auto-guidance used in peanuts fields with differences in tillage and terrain was quantified. Six peanut fields under conventional and conservation tillage having contour rows and rolling terrain were selected for this study in Alabama and Georgia. The fields were planted and inverted in utilizing two treatments: with RTK GPS-based auto-guidance and without auto-guidance (Manual-MAN). Treatment differences were calculated by comparing yields from replicated strips. Results from a Student<br /> t-test indicated significant yield differences between the MAN and RTK reatments on two out of the six fields of this study. On those two fields RTK treatment out-yielded the manual guidance treatment by about 405 Kg/ha (field 1) and 588 Kg/ha (field 2). At three other fields yield benefits of using RTK guidance over manual guidance were of a magnitude of 137, 28, and 83 Kg/ha. In four peanut fields, the gross revenue of using use RTK guidance over manual guidance were: 101 $/ha, 431 $/ha, 20 $/ha, and 295 $/ha. The benefits of RTK guidance were also evaluated under straight row fields. Results showed that while a farmer using an RTK auto-steer guidance system could potentially expect additional net returns of between 94 and 404 $/ha compared to those from row deviations of 9 cm , higher net returns of between 323 and 695 $/ha could be perceived if the guidance system is used instead of having row deviations of 180 mm.<br /> <br /> Variable-rate application of nutrients continues to increase in Alabama and consequently research is being conducted to enhance distribution and successful implementation of nutrient management planning. One study evaluating different vegetation indices for variable rate application of nitrogen showed that indices including the red-edge wavelength performed better than those using the red wavelength. Indices such as NDRE , Chl In RE, and CSM-RE performed better than the NDVI. A canonical correlation analysis showed that those indices were<br /> able to assess differences in corn chlorophyll content and biomass early (V6) and late (V10) in the growing season. This result suggests that those indices could provide a better estimation of in-season yield potential than NDVI and can be used for variable rate nitrogen algorithms. Field data collected between 2009 and 2012 was used to calculate an Alabama variable rate nitrogen algorithm (V8 stage). None significant differences were found when this equation was compared to the known Oklahoma State University. Comparison between variable and uniform rate nitrogen application in a corn field was conducted in 2012. The 2012 data shows that the highest yield was achieved with the uniform N rate (highest N amount applied) followed by the VRN at V6 using the OK algorithm.<br /> <br /> ----------<br /> Nebraska<br /> ---------<br /> <br /> Interactions of Water and Nitrogen Supply for Irrigated Corn across Field Landscapes: A project initiated in 2011 was continued in 2012 to evaluate response of irrigated corn to site specific water and N management across variable landscapes. Before planting operations began, background data was collected for each site. Soil ECa was mapped with a Veris 3100 cart coupled with a RTK-GPS receiver for accurate topographical information. Guided soil samples were collected from each site and analyzed for soil organic matter, pH, conductivity, nitrate, and Bray-1 phosphorus. Watermark soil moisture sensors were installed shortly after planting on all fields and retrieved shortly before harvest. An automated tipping-bucket rain gauge was installed at each site. Aerial images were collected in mid-May, to provide a bare soil image, and again in late-June and late-August to provide growing season imagery. Imagery was collected in red, green, blue and near-infrared (NIR) wavebands.<br /> <br /> Four locations were used for the study in 2012. Two fields were on University of Nebraska (UNL) research sites [South Central Agricultural Laboratory (SCAL) and West Central Water Resources Field Laboratory (BWL)], and two fields were on cooperating producer s fields (1 field in Morrill County and 1 field in Hamilton County). The UNL research sites included more detailed measurements, and inclusion of treatments that are more yield limiting than those on producer fields. Locations were situated across a rainfall and soils gradient in Nebraska, allowing evaluation of site specific water/N management interactions over a range of annual rainfall and soil types. Three of the sites included the use of variable rate irrigation systems and one of the sites implemented canopy sensor based in season N treatments. <br /> <br /> Soil moisture was measured in specific treatments (at least two replications) every foot to a depth of 4 ft. Soil matric potential was measured hourly using Watermark granular matric sensors and monitors. John Deere CropSense capacitance probes were used, to the extent available, for comparison to other methods of soil moisture determination. Additionally, neutron scattering probes were used at UNL research centers to calibrate and complement Watermark and CropSense sensors in selected treatments. From extensive soil water status measurements in spatial and temporal scales, the distribution of soil moisture under various irrigation and nitrogen regimes were determined. The crop water uptake under various irrigation and nitrogen treatments were determined using profile soil water status, irrigation, and precipitation amounts. In order to establish crop response within landscape positions relative to water supply, three levels of irrigation water were evaluated at SCAL, BWL, and Morrill County producer site. Irrigation applications were managed based on pre determined depletion levels of the available soil water holding capacity. This was accomplished at SCAL (Clay Center) using pre determined soil matric potential values to time irrigation applications. In the fully irrigated plots, a typical value of 90 100 kPa for a silt loam soil was used to trigger irrigation. At each irrigation event, a total of 1.0 and 0.75 inches of water was applied to fully irrigated plots (100%) and 75% of fully irrigated treatments, respectively. Before the tassel stage, whenever the average soil matric potential value in the top 2 ft soil layer reached 90 100 kPa, irrigation was triggered. The same procedure was used for the average of top 3 ft soil layer after tassel to account for the root water uptake in the 3rd ft layer on irrigation management.<br /> <br /> Ancillary data was collected at UNL research locations to measure in-season corn response to variable water and N treatments. Leaf chlorophyll readings were taken weekly from V6 to R4 growth stage using a SPAD chlorophyll meter. Leaf area index (LAI) and relative leaf water content were measured every two weeks beginning at growth stage V8. Crop canopy reflectance was measured using two active canopy sensors (Crop Circle 210 sensor and Crop Circle 470 sensor) mounted to a high clearance vehicle. Aerial imagery was taken at SCAL and BWL locations at ~ the R4 growth stage that included visible and near-infrared bands. <br /> <br /> Treatment design consisted of variable N fertilizer levels at all locations with variable irrigation treatments at SCAL, BWL, and Morrill Co. cooperator site. Irrigation was delivered using either a center-pivot sprinkler system or a linear-move sprinkler system (SCAL). The SCAL research site consisted of three irrigation water levels (full crop water demand-100%, 75% of full crop water demand-75%, rainfed) with five N treatments (75, 125, 175, 225 lbs N acre-1, Sensor-based). Seeding rates were 30,000 and 26,000 plants acre-1 for the irrigated and rainfed plots respectively The West Central Water Resources Laboratory field treatment design consisted of three irrigation levels (fully irrigated-100%, 70% of fully irrigated, 40% of fully irrigated) with four N fertilizer levels (75, 125, 175, 225 lbs N acre-1). <br /> <br /> The Hamilton County site RA) had two four N strip treatments (75, 125, 175, 225 lbs N acre-1) with three replications. For the Morrill county location, three water levels (40, 70 and 100% of fully irrigated) and three N rates (125, 175 and 225 lb N acre-1) were imposed across differential landscape positions. Nitrogen rates were applied in field-length strips crossing the range of landscape position within the field. <br /> <br /> There were significant interactions between irrigation and nitrogen levels on grain yield at SCAL in 2011 and 2012. This suggests that management of one input will influence response to the other, even on this site with little landscape variation, and that spatial management of either water or N will require accounting for influences on crop requirements of the other input.<br /> <br /> Soil apparent electrical conductivity (ECa) was found to reasonably predict plant available water (PAW) for western, coarser-textured sites. However, there was no statistical relationship between ECa and PAW for finer-textured, eastern sites. This suggests that ECa may be an important layer for informing variable rate irrigation (VRI) for relatively coarse-textured locations, but other information layers, perhaps topography, will be important on relatively fine-textured locations.<br /> <br /> We observed a strong relationship between the developed evapotranspiration-nitrogen use efficiency index (ETN), N rate, and grain yield. Further research is needed to evaluate ETN for different climatic, soil and crop management conditions (SCAL).<br /> <br /> Water extraction from various soil depths varied with the season and N application rate (SCAL).<br /> <br /> Crop water use efficiency (CWUE) had a positive, quadratic relationship with N rate (SCAL).<br /> <br /> Irrigation water use efficiency (IWUE) had a positive, quadratic relationship with N rate, and was greatest at higher yields (SCAL).<br /> <br /> In general, a positive, quadratic relationship existed between evapotranspiration water use efficiency (ETWUE) and N rate (SCAL).<br /> <br /> ----------<br /> Washington:<br /> ----------<br /> <br /> At Washington State University, Manoj Karkee and his team conducted several research projects and achieved substantial accomplishments in developing systems and technologies for precision and automated agriculture. Major projects and corresponding accomplishments are listed below. <br /> <br /> 3D Machine Vision for Improved Apple Crop Load Estimation: Accurate estimation of apple cropload is essential for efficient orchard management. We designed an over the row platform capture images from two side of apple canopies to minimize the occlusions and improve the accuracy of cropload estimation. A tunnel structure was used to minimize the variation in lighting condition and artificial lights were installed for night time operation. A color camera, a 3D camera and an orientation sensor were mounted in the sensor platform and were moved along rows of apple trees in three different commercial orchards of Allan Bros. Inc., Prosser, WA. Overall, the images of apples trees were successfully captured from both sides of the row using this platform. Images were capture in both day and night times. A study of fruit count showed that about 35% more apples were visible when images were captured from two opposite sides of the canopy. These images are now being analyzed to identify apples and match it with 3D images to create 3D maps of apples.<br /> <br /> System Development for Automatic Pruning of apple Trees: In this work, a machine vision-based method was developed for 3D reconstruction of apple trees and identification of pruning branches in an orchard with central leader-based fruiting wall architecture. A time-of-flight-of-light-based 3D camera was used to obtain 3D information of apple trees in the dormant season. These images were preprocessed to remove external noise and distortion from the sensor. A skeleton was obtained to reduce the complexity of huge point cloud dataset and to represent the tree in a way that can be used for pruning branch and pruning point identification based on predefined pruning rules. A simplified two step pruning rule was used to identify pruning branches in the tree. Performance of the algorithm was compared against human pruning. The pruning branch identification of the algorithm was closest to the pruning branch identification of a worker with 10 years of experience. The algorithm suggested to remove 17% of branches in average whereas the worker removed 16% of branches in average. The relative pruning accuracy of the algorithm as compared to experienced human workers was 61%. This low relative pruning accuracy of the algorithm shows the<br /> variability of pruning preference between the algorithm and the human pruners. However, even when different branches were selected by the algorithm and human workers, a similar branch distribution was maintained within tree canopies. These results show promise for automated pruning of tall spindle apple trees in the future.<br /> <br /> Development and Optimization of Solid-Set Canopy Delivery Systems for Resource-Efficient, Ecologically Sustainable Apple and Cherry Production: This project is a subcontract to a SCRI project with Michigan State University and co-directed by Dr. Brunner of WSU. This project is performed a multidisciplinary research and extension team from three of the major fruit-producing states to develop, evaluate, and deliver resource-efficient, innovative management technologies and tactics for apple and cherry production systems. It aims to establish innovative delivery technologies for canopy and orchard floor inputs (including high efficiency irrigation systems, precision-activated micro-emitters, and reduced risk pesticides) to address critical fruit production needs as identified by commodity PMSPs and the Technology Roadmap for Tree Fruit Production. Direct outcomes of system implementation that will be analyzed include: economic and agro ecosystem impacts. Sociological research will focus on how these integrated technologies impact urban-farm relations, barriers to grower adoption, and how these factors can inform better extension and educational programmatic efforts.<br /> <br /> Design and Development of Apple Harvesting Techniques: This research aimed to<br /> investigate a few conceptual end effector designs capable of harvesting a cluster of apples in fruiting wall canopy architecture. Initially, the technology will be developed for apples but the extension to other similar-size fruits such as pears will be possible. The initial results from year 1 show a successful fruit removal technique applicable to apples grown on a trellised orchard system. Signs of branch punctures were visible on some of the apples. A method to seclude the apple from any branch or ensure that no branches can be pressed between the wheel and the apple skin was tried. Apples that were grown on small spurs tended to be removed easier than apples growing on long flexible branches. Long branches allowed apples to move more than apples growing on small spurs. This flexibility reduced the twisting, or torque, that was ultimately transferred to the apple and stem resulting in less effective removal rates. Horticulture can play an important role in aiding the fruit removal technique described in the above research.<br /> <br /> Multi- and Hyper-spectral imaging for potato stress sensing: Hyperspectral imaging system was used as a non-contact sensing instrument in this study for detecting water stress in potato plants non-destructively. An experiment was setup with potato plants planted at four different soil moisture content levels in a greenhouse. Reflectance plots of plant leaves at different soil moisture levels showed differences in spectral signature. Spectral indices were calculated from reflectance data and were correlated with soil moisture levels. It was found from this research that various spectral indices of plant canopies has good correlation with soil moisture content levels. Based on the reflectance data collected on May 4, 2012, correlation of modified NDVI, and Vogelmann Red Edge Index (VOG REI) 1 with soil moisture content were found to be -0.85, -0.88<br /> respectively. A multivariate linear model developed in this study predicted soil moisture content levels reasonably accurately. The R-squared value of the model was 0.8. The results showed a promise for non-destructive spectral sensing of plant canopies for monitoring soil moisture content and detecting<br /> threshold moisture content level for optimal water application. In this study, optimal soil moisture content to maximize the yield of Umatilla Potatoes in Silt Loam soil was found to be 17%.<br /> <br /> ------------------------------------------------------------------------------<br /> ----------------------------Extension/Outreach/Education----------------------<br /> <br /> --------<br /> Arizona:<br /> --------<br /> <br /> Agricultural Extension Team: A New Model to Enhance Stakeholder Input, Program Planning and Outreach to Agricultural Clientele. Signature Program funded by the University of Arizona College of Ag and Life Sciences. <br /> <br /> Impact: The goals of this group are to identify information needs of agriculture clientele statewide; to improve communication between specialists and agents within Cooperative Extension; to enhance program planning; and to provide quality up to date research based education to stakeholders via stakeholder meetings and workshops, e mail, USPS mail and publications. Program planning will be coordinated among team members to better meet the needs of our stakeholders<br /> <br /> Extension Education for Ag Professionals: Pilot Project. University of Arizona - Extension Office. <br /> <br /> Participation: Providing training to crop consultants in the use of GPS technology. <br /> <br /> Impact: Impacts from this program are still to be generated and will be timely reported.<br /> <br /> Characterizing plant height, canopy temperature and reflectance for high-throughput phenotyping. Extensive testing of proximal sensing techniques with high-clearance ground systems in a field-based approach. Collaboration with scientists of the USDA-ARS ALARC in Maricopa AZ.<br /> <br /> Cotton yield monitoring in commercial fields- 2011. Installation, training and data analysis of cotton yield data. Systems included John Deere and Case-IH. These systems required interfacing GPS to collect geo-referenced yield data in several fields in Buckeye, Paloma, Maricopa and Marana AZ.<br /> <br /> --------<br /> Nebraska<br /> --------<br /> <br /> Nebraska Agricultural Technology Association (NeATA). Association of crop producers, researchers and advisors related to site-specific crop management and other emerging agricultural technologies. Annual conference February 13-14, 2013; pre-conference symposium Variable Rate Technologies and Techniques, February 13, 2013. http://neata.org/<br /> <br /> <br /> Site-Specific Crop Management. AGRO/MSYM/AGEN 431. Senior level course on agronomic and engineering aspects of site-specific crop management. 3 credit hours. Offered fall semesters. Enrollment fall 2012 = 28; spring 2013 special problems offering enrollment = 6; enrollment fall 2013 = 55. <br /> <br /> Spatial Variability in Soils. AGRO 831. Extension workshop and graduate level distance education course. 2 credit hours. Offered spring semester of even-numbered years; last taught spring 2012, enrollment = 14.<br /> http://www.agronomy.unl.edu/newprospective/distanceed/agro896-1.html<br /> <br /> <br /> ----------<br /> Washington<br /> ---------- <br /> <br /> *World Ag Expo Group Tour - World Ag Expo is an international agricultural show claimed to be the largest annual exposition in the agricultural sector. The event has been one of the most popular venues for successful growers, leading<br /> researchers, engineers, manufacturers, and policy makers from around the world. This year's event brought together thousands of intriguing products and equipment from around the world, which was eye-opening for the development and adoption of new mechanization and automation solutions for sustainable tree fruit production in WA. Manoj Karkee organized a team of 13 individuals (5 WSU researchers and and 8 WA tree fruit industry representatives) to attend this show to experience the latest developments in the global agricultural industry. The travel also facilitated directed discussions in a trans-disciplinary group of growers and researchers and provided opportunity for networking with growers, scientists and entrepreneurs from around the world. <br /> <br /> Inputs and Outputs: The team visited the exposition on Feb 14 and 15, 2012. Participants divided into smaller groups of two to three individuals and explored the show on their own and based on their interests.Group discussions included different aspects of the equipment or technologies that might be of<br /> usefulness to WA tree fruit industry. The team attended two important tours of citrus research and industry facilities. Citrus Juicing Plant Tour (Feb 16, 2012) took the team to California Citrus Producers, Inc. The tour included visit to various production farms and a local packing house. We also organized a tour to the University of California Lindcove Research & Extension Center on Feb 15, 2012. This session included an overview and Q&A with center director Dr. Grafton-Cardwell, a citrus variety tasting session, tour of the UC R&E<br /> Center packing-line and demonstration citrus block. During this tour, we organized four round table meetings to discuss various aspects of the tools,<br /> technologies and machines that were observed in the expo and on tours. Horticulturist Craig Hornblow of Ag First NZ and two growers from New Zealand also joined us in these discussions. The group also discussed various issues on mechanization and automation of tree fruit production, related issues and<br /> proposed solutions, barriers and next steps.<br /> <br /> Impacts: This tour helped participants increase their knowledge and understanding of current technologies and systems, and sharpen the vision for increased mechanization and automation in tree fruit production. Participants identified various tools and technologies that were useful to them and showed interest in trying them out in near future. Tours were found to be highly educational. The small packing line own by university of California research and extension center could be used as a model here in WSU research and extension centers to create similar facilities. These packing lines provide various fruit quality related information to researchers in a much more flexible and reliable fashion. Understanding of citrus packing and juicing operations may be helpful to growers to think outside the box in terms of possible setups, machines and scale of operation of packing lines and different byproducts that can be developed from fruits.<br /> <br /> Mechanization of apple harvesting was one of the main focuses of various meetings and discussions. These discussions suggested that the industry should pull resources together (3M: money, man and machine) to work on a mechanical harvesting system so that a working prototype can be demonstrated in<br /> medium term. Another important outcome of this tour and associated discussions is that participants agreed to develop a detailed roadmap for mechanizing apple harvesting operation. This roadmap is expected to clearly layout how we can achieve short, medium and long term mechanization needs in apple harvesting. The<br /> roadmap will identify the actual problem(s), identify potential solutions, layout plans for developing those solutions and estimate necessary resources including 3M (man, machine, money) and time. All participants showed interest to provide needed support to this effort.<br /> <br /> Following this tour, WSU CPAAS organized two planning meetings in 2012 to develop strategies and timeline to complete this roadmap. New participants were identified and invited to this effort through the consultation with WTFRC and other industry groups. The team has started preparing the first draft of the<br /> road map. Several monthly meetings have been planned for early 2013 to complete this process. <br /> <br /> <br /> *Stakeholder Education, Interaction and Collaboration: <br /> " Sep 13, 20112: Met with various growers and WTFRC representatives to discuss apple harvesting technology roadmap development<br /> " August 27, 2012: Demonstrated the solid set canopy delivery system to a group of growers and other stakeholders<br /> " August 3, 2012: Co-organized and participated in the technology demonstration to Washington Governor Gregoire and her team<br /> " July 23-24, 2012: Organized a planning meeting in Aurora, OR to discuss small fruit industry need in canopy management automation area<br /> " July 17, 2012: Presented a research project in USDA field day, Paterson, WA<br /> " June 4, 2012: Presented and demonstrated two research projects in cherry field day, Prosser, WA<br /> " April 24, 2012: Visited Auvil Fruit Company to discuss potential for mechanical and automated apple harvesting, Vantage, WA<br /> " Feb 21-22, 2012: Organized a planning meeting in Mt. Vernon, WA to discuss small fruit industry need in canopy management automation area<br /> " Jan 4, 2012: Visited Mercer Canyon Inc., Prosser, to discuss about vegetable industry s need in weed control area and to seek collaboration in a SCRI proposal in automatic weed control.<br /> <br /> Impact: Results and outputs of WSU precuision and automated ag research program were presented to a broad audience of stakeholders through these diverse activities in WA and OR. In addition, various technologies developed through my<br /> research program was demonstrated to growers and other stakeholder groups, which will help them understand the benefits and applicability of the technology. The discussions we had with growers and other stakeholder groups have helped them understand the current state-of-the-art in the area of precision and atuomated agricultural systems, which will facilitate informed decision making for long-term sustainability of their operation. These efforts will also facilitate the commercialization of tools and technologies developed in our research program. Through these activities, we have continued existing collaborations and have started new collaborations with various stakeholder groups, which is crucial for successful completion of ongoing research projects and also for developing competitive research proposals. Finally, these outreach activities helped us get<br /> the feedback on my research projects and understand the need of the industry, which is critically important to refine my research goals and direction so that we can make high positive impact to the specialty crop industry.

Publications

---------------------------------------<br /> Journal/Conference Articles and Posters<br /> ---------------------------------------<br /> <br /> Kaggwa-Asiimwe R., P. Andrade-Sanchez, G. Wang. 2013. Plant architecture influences growth and yield response of upland cotton to population density. Field Crops Research 145: 52-59<br /> <br /> Andrade-Sanchez, P., J.T. Heun, M.A. Gore, A.N. French, E. Carmo-Silva, M.E. Salvucci. 2012. Use of a moving platform for field deployment of plant sensors. ASABE paper number 121337985<br /> <br /> Carmo-Silva E.A., M.A. Gore, P. Andrade-Sanchez, A.N. French, D.J. Hunsaker, M.E. Salvucci. 2012. Decreased CO2 availability and inactivation of Rubisco limit photosynthesis in cotton plants under heat and drought stress in the field. Environmental and Experimental Botany. 83: 1-11.<br /> <br /> Andrade-Sanchez P. and Heun J.T. 2013. Yield monitoring technology for irrigated cotton and grains in Arizona: Hardware and software selection. Bulletin AZ1596. The University of Arizona - Cooperative Extension. Tucson, Arizona 85721<br /> <br /> Wang G., R. K. Asiimwe, and Pedro Andrade. 2011. Growth and yield response to plant population of two cotton varieties with different growth habits. Cotton Research & Outreach 2010-2011 Bulletin AZ1548. The University of Arizona - Cooperative Extension. Tucson, Arizona 85721<br /> <br /> Andrade-Sanchez P. and Heun J.T. 2012. From GPS to GNSS: Enhanced functionality of GPS-integrated systems in agricultural machines. Bulletin AZ1558. The University of Arizona - Cooperative Extension. Tucson, Arizona 85721<br /> <br /> Bansal, R., W. S. Lee, and S. Satish. 2012. Green citrus detection using Fast Fourier Transform (FFT) leakage. Precision Agriculture 13(6), DOI: 10.1007/s11119-012-9292-3. <br /> <br /> Khosro, F. A., J. M. Maja, R. Ehsani, and W. S. Lee. 2012. An automated tine control for tractor drawn citrus canopy shakers. ASABE Paper No. 12-1337183. ASABE 2012 Annual Meeting, July 29-Aug. 1, 2012, Dallas, Texas. <br /> <br /> Kumar, A., W. S. Lee, R. Ehsani, L. G. Albrigo, C. Yang, and R. L. Mangan. 2012. Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques. Journal of Applied Remote Sensing 6, 063542, http://link.aip.org/link/doi/10.1117/1.JRS.6.063542.<br /> <br /> Mason, A. D., R. Schnell, J. Ferrell, J. Sartain, and W. S. Lee. 2012. Comparing grid and directed zone soil sampling schemes for peanut production. ASA, CSSA, and SSSA International Annual Meetings, Oct. 21-24, 2012, Cincinnati, Ohio. <br /> <br /> Mishra, A. R., D. Karimi, R. Ehsani and W. S. Lee. 2012. Identification of citrus greening (HLB) using a VIS-NIR spectroscopy technique. Trans. ASABE 55(2): 711-720.<br /> <br /> Lee, W. S. 2012. Sensing technologies for precision agriculture: current status and future needs. Proceedings of the 6th International Symposium on Machinery and Mechatronics for Agriculture and Biosystems Engineering (ISMAB), June 18-20, 2012, Jeonju, Korea. <br /> <br /> Li, H., W. S. Lee, K. Wang, R. Ehsani, and C. Yang. 2012. Spectral angle mapper (SAM) based citrus greening disease detection using airborne hyperspectral imaging. 11th International Conference on Precision Agriculture, July 15-18, 2012, Indianapolis, Indiana. <br /> <br /> Li, X., W. S. Lee, M. Li, R. Ehsani, A. Mishra, C. Yang, and R. L. Mangan. 2012. Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers and Electronics in Agriculture 83: 32-46. <br /> <br /> Ruslan, R., R. Ehsani, and W. S. Lee. 2012. Quantification of total soluble solids and titratable acidity for citrus maturity using VIS-NIR spectroradiometer. Applied Engineering in Agriculture 28(5): 735-743.<br /> <br /> Schueller, J.K. 2012. Another report from 25 years hence. Resource. July-August. 19(4):16-17.<br /> <br /> Schueller, J.K. 2012. Integration of mechanical, electrical, and systems engineering in agriculture and food production. Keynote address. International Conference on Agricultural and Food Engineering. Putrajaya, Malaysia. November 26-28.<br /> <br /> Sengupta, S., and W. S. Lee. 2012. Identification and determination of the number of green citrus fruit under different ambient light conditions. International Conference of Agricultural Engineering CIGR-AgEng2012, July 8-12, 2012, Valencia, Spain. <br /> <br /> Shin, J. S., W. S. Lee, and R. Ehsani. 2012. Machine vision based citrus mass estimation during post-harvesting using supervised machine learning algorithms. Proceedings of the International Symposium of Mechanical Harvesting & Handling Systems of Fruits and Nuts, Lake Alfred, Florida, 1-4 April 2012, Acta Hort, International Society for Horticultural Science.<br /> <br /> Shin, J., W. S. Lee, and R. Ehsani. 2012. Postharvest citrus mass and size estimation using logistic classification model and watershed algorithm. Biosystems Engineering 113(1): 42-53.<br /> <br /> Yang, C. and W. S. Lee. 2012. Precision agricultural systems. In Agricultural automation: fundamentals and practices. Eds. Q. Zhang and F. J. Pierce. CRC Press. Accepted for publication. <br /> <br /> Yang, C. and W. S. Lee. 2012. Blueberry fruit detection by Bayesian classifier and support vector machine based on visible to near-infrared multispectral imaging. ASABE Paper No. 12-1338433. St. Joseph, Mich.: ASABE.<br /> <br /> Yang, C., W. S. Lee, and J. G. Williamson. 2012. Classification of blueberry fruit and leaves based on spectral signatures. Biosystems Engineering 113(4): 351-362.<br /> <br /> Zheng, L., W. S. Lee, M. Li, A. Katti, C. Yang, and H. Li. 2012. Analysis of soil phosphorus concentration based on Raman spectroscopy. SPIE Asia-Pacific Remote Sensing 2012, Kyoto, Japan, 29 Oct.  1 Nov., 2012. <br /> <br /> He, L., X. Du, R. Luo, M. Karkee, Q. Zhang. 2012. A Twining Robot for High Trellis String Tying in Hops Production. The Transactions of ASABE, 55(5): 1167-1673.<br /> <br /> Karkee, M., R. McNaull, S. J. Birrell, B. L. Steward. 2012. Estimation of Optimal Biomass Removal Rate Based on Tolerable Soil Erosion for Single-Pass Crop Grain and Biomass Harvesting System. Transactions of the ASABE, 55(1): 107-115.<br /> <br /> Abd Aziz, S., B. L. Steward, A. Kaleita, and M. Karkee. Assessing the Effects of DEM Error Uncertainty on Soil Loss Estimation in Agricultural Field. Transactions of the ASABE, 55(3): 785-798.<br /> <br /> Monga, M.*, M. Karkee, S. Sun, L. K. Tondehal, B. L. Steward, A. Kelkar, J. Zambreno. 2012. Real-time Simulation of Dynamic Vehicle Models using a High-performance Recon
gurable Platform. International Conference on Computational Science, ICCS 2012, June 4-6, 2012, Ames, IA 50011 USA.<br /> <br /> Amatya*, S., M. Karkee, A. K. Alva, P. A. Larbi, B. Adhikari. 2012. Hyperspectral Imaging for Detecting Water Stress in Potatoes. ASABE Paper No. 121345197. St. Joseph, Mich.: ASABE.<br /> <br /> He, L., J. Zhou, X. Du, D. Chen, Q. Zhang. M. Karkee. 2012. Shaking Energy Delivery on Sweet Cherry Trees in Different Excitation Models. ASABE Paper No. 12-1337766. St. Joseph, Mich.: ASABE. <br /> <br /> Zhou, J. L. He, X. Du, D. Chen, Q. Zhang, M. Karkee. 2012. Dynamic Response of Sweet Cherry Tree to the Vibration of a Limb Shaker. ASABE Paper No. 12-1337429. St. Joseph, Mich.: ASABE.<br /> <br /> Hashimoto, A., J. Arnold, J. Ayars, S. Crow, T. Eggeman, L. Jakeway, M. Karkee, S. Khanal, J. Kiniry, J. Matsunaga, G. Murthy, M. Nakahata, R. Ogoshi, B. Turano, S. Turn, J. Yanagida, Q. Zhang. 2012. High-Yield Tropical Biomass for Advanced Biofuels. Sun Grant Initiative National Conference, New Orleans, LA; Oct 2-5, 2012.<br /> <br /> Karkee, M., B. Steward, and J. Kruckeberg. 2013. Automation of Pesticide Application Systems. In Agricultural Automation: Fundamentals and Practices (Q. Zhang and F. Pierce editors; ISBN: 9781439880579). CRC Press: Boca Raton, Florida, USA. In Press. <br /> <br /> Karkee, M. and Q. Zhang. 2012. Mechanization and Automation Technologies in Specialty Crop Production. Invited Article, ASABE Resource Magazine, Sep/Oct 2012: 16-17. <br /> <br /> Amatya, S.*, M. Karkee, 2012. Nitrogen stress detection for potato using hyperspectral imaging. 2013 ASABE International Meeting, Abstract No. 1589210.<br /> <br /> Ma, Shaochun*, M. Karkee, and Q. Zhang. 2012. Sugarcane Harvesting System-A Critical Review. 2013 ASABE International Meeting, Abstract No. 1574361. <br /> <br /> Gongal. A*, B. Adhikari, S. Amatya, M. Karkee, Q. Zhang and K. Lewis, 2012. 3D Machine Vision for Improved Apple Crop Load Estimation. CPAAS 2nd Tech Expo. Oct 2,2012, Wenatchee ,WA.<br /> Gongal. A*, B. Adhikari, S. Amatya, M. Karkee, Q. Zhang and K. Lewis, 2012. 3D Machine Vision for Improved Apple Crop Load Estimation. 108th WSHA Annual Meeting, Poster Session. Dec 3  5, 2012, Yakima, WA.<br /> <br /> Gongal. A*, S. Amatya, and M. Karkee, 2012. Over-the-row Machine Vision for Improved Apple Crop Load Estimation. 2013 ASABE International Meeting.<br /> <br /> Sharda A., M. Karkee and Q. Zhang. Pressure dynamics in solid set canopy spray application system for tree fruit orchards. Washington State Horticultural Association 108th Annual Meeting and Trade Show, Yakima, WA. December 3-5, 2012.<br /> <br /> Sharda A., M. Karkee, Q. Zhang and I. Ewlanow. Effect of nozzle type, location and orientation around tree canopy on product coverage for solid set canopy delivery system. Washington State Horticultural Association 108th Annual Meeting and Trade Show, Yakima, WA. December 3-5, 2012.<br /> <br /> De Kleine, M.E., M. Karkee. 2012. A non-Newtonian Shear Thickening Surface for Fruit Impact Bruising Evaluation. 108th WSHA Annual Meeting. Dec 3-5, 2012, Yakima, WA.<br /> <br /> De Kleine, M.E., M. Karkee, K. Lewis, Q. Zhang. 2012. Apple Harvesting Techniques. 108th WSHA Annual Meeting. Dec 3-5, 2012, Yakima, WA.<br /> <br /> Larbi, P.A.*, M. Karkee, S. Amatya, M. De Kleine, Q. Zhang, and M.D. Whiting. 2012. Modification and Testing of an Experimental Sweet Cherry Harvester. 108th WSHA Annual Meeting, Poster Session. Dec 3  5, 2012, Yakima, WA.<br /> <br /> Larbi, P.A.*, M. Karkee, and Ines Hanrahan. 2012. Prospective of Hyperspectral Imaging Techniques for Predicting Chilling Injury Incidence in Honeycrisp" Apples. 108th WSHA Annual Meeting, Poster Session. Dec 3  5, 2012, Yakima, WA.<br /> <br /> Larbi, P.A.*, S. Amatya, and M. Karkee, and Ines Hanrahan. 2012. Characterizing the Response of a Hyperspectral Camera Used in Close Range Imaging under Laboratory Conditions. 2013 ASABE International Meeting, Abstract No. 1594789.<br /> <br /> Adhikari*, B., M. Karkee. 2012. 3D Reconstruction of Apple Trees for Mechanical Pruning. WSU Academic Showcase, March 30, 2012, Pullman, WA. <br /> <br /> Roberts, D.F., R.B. Ferguson, N.R. Kitchen, V.I. Adamchuk, and J.F. Shanahan. 2012. Relationships between soil-based management zones and canopy sensing for corn nitrogen management. Agron. J. 104:119-129.<br /> <br /> Adamchuk, V.I., A.S. Mat Su, R.A. Eigenberg, and R.B. Ferguson. 2011. Development of an angular scanning system for sensing vertical profiles of soil electrical conductivity. Trans. of the ASABE 54(3):1-11.<br /> <br /> Shiratsuchi, L., R. Ferguson, J. Shanahan, V. Adamchuk, D. Rundquist, D. Marx and G. Slater. 2011. Water and nitrogen effects on active canopy sensor vegetation indices. Agron. J. 103:1815-1826.<br /> <br /> Adamchuk, V., L. Shiratsuchi, C. Lutz, R. Ferguson. 2012. Integrated crop canopy sensing system for spatial analysis of in-season crop performance. In: Proceedings of the Eleventh International Conference on Precision Agriculture, International Society of Precision Agriculture, July 15-18, 2012, Indianapolis, IN. (CD publication).<br /> <br /> Ferguson, R., T. Shaver, N. Ward, S. Irmak, S. Van Donk, D. Rudnick, B. Wienhold, M. Schmer, V. Jin, D. Francis, V. Adamchuk, L. Hendrickson. 2012. Landscape influences on soil nitrogen supply and water holding capacity for irrigated corn. . In: Proceedings of the Eleventh International Conference on Precision Agriculture, International Society of Precision Agriculture, July 15-18, 2012, Indianapolis, IN. (CD publication).<br /> <br /> Pan L., V. Adamchuk, R. Ferguson. 2012. An approach to selection of soil water content monitoring locations within fields. In: Proceedings of the Eleventh International Conference on Precision Agriculture, International Society of Precision Agriculture, July 15-18, 2012, Indianapolis, IN. (CD publication).<br /> <br /> <br /> ----------------<br /> Invited Seminars<br /> ----------------<br /> <br /> Pedro Andrade, Using mobile platforms for continuous data acquisition of plant traits. Presentation during Brown Bag Seminar Series at the USDA ARS Arid Land Agricultural Research Center (ALARC). Presented in Maricopa, AZ on 4/23/2012.<br /> <br /> Pedro Andrade, Use of proximal sensing in Pecan. Presentation during the 13th International Symposium of Pecan (XIII SIMPOSIO INTERNACIONAL DE NOGAL PECANERO). Presented at Hermosillo, Sonora, Mexico on 9/13/2012<br /> <br /> Soil compaction in Medjool dates and its effect on root growth and fruit yield. Presentation at the Yuma Ag Summit on results of research in soil compaction in palm date production Presented at Yuma, AZ on 3/8/2012.<br /> <br /> Agronomic professional development refresher: Tillage and ground preparation. Presentation at the Yuma Ag Summit on new developments on GPS-based tractor/implement technology for land leveling and ground preparation Presented at Yuma, AZ on 3/8/2012.<br /> <br /> Pedro Andrade, Using GPS to mark the location for planting new trees. Presentation at the Arizona Pecan Growers Association Annual Meeting on the use of GPS systems w/advanced algorithms for navigation inside the orchard with auto-steering platforms Presented at Tucson, AZ on 9/21/2012.<br /> <br /> Pedro Andrade, Early season (cotton) talks to cover the topics of sofware updates and hardware upgrades. Goodyear AZ (1/26/12), Mesa AZ (1/27/12), Safford AZ (2/17/12), Maricopa AZ (2/20/12), Parker AZ (2/22&28/12), and Coolidge AZ (2/29).<br /> <br /> Pedro Andrade, Cotton mid-season talks to talk about close cultivation for weed control and GPS-based rate controllers for chemical applications: San-Tan AZ (6/21/12), Maricopa (6/27/12), Goodyear AZ (6/28/12), and Marana AZ (7/12/12).<br /> <br /> Pedro Andrade, Cotton late season talks about yield monitors systems and data management. Goodyear AZ (9/05/12), Casa Grande AZ (9/19/12), and Marana AZ (9/20/12).<br /> <br /> Pedro Andrade, Central Arizona College - Signal Peak Campus. Students from the Engineering Technology Division enrolled in the John Deere Tech Certificate Program visited UA-MAC on 4/19/12 to receive a demonstration on auto-steer with tractor simulator.<br /> <br /> Pedro Andrade, Chapingo University (Mexico) - Texcoco Campus. Students from the Irrigation Dept. visited UA-MAC on 10/31/12 to experience field demonstration on advanced technology in irrigated cotton production.<br /> <br /> Pedro Andrade, University of Arizona Desert Ag Ventures Program. Presentation and demonstration to Winter visitors on the use of advanced technology for farm production in Arizona. Maricopa Agricultural Center, 2/21&23/2012.<br /> <br /> Pedro Andrade, Maricopa Agricultural Center. Demonstration on the use of yield monitor data and management software to create prescription files for Nitrogen application. Audience included growers, applicators, pest-control advisors, and agriculture students from Arizona and Mexico.<br /> <br /> USDA-ARS-ALARC and UA-MAC. 2012 Farm Day. Community and stakeholder outreach event in Maricopa, AZ. Coordinated auto-steer tractor rides and demonstration of advanced technology for input application. The event was attended by about 600 people from surrounding communities.<br /> <br /> Delegation of Chinese Officers from water conservancy - USDA-MOST flagship project on water saving irrigation. Presentation on MAC Extension program on precision agriculture.<br /> <br /> --------------------------<br /> Workshops and other events<br /> --------------------------<br /> <br /> University of Arizona, Use of COTMAN plant mapping Sofware. The instructors included Dr. Tina Teague, Dr. Derrick Oosterhuis, and Dr. Dan Fromme. Maricopa Agricultural Center, May 8, 2012. Event sponsored by Cotton Inc.<br /> <br /> University of Arizona, Minimizing pesticide spray drift with advanced nozzle selection (with Bill McCloskey, Weed Specialist - UA). Southwest Ag Summit. Yuma AZ, March 8, 2012<br /> <br /> University of Arizona, EPA - Worker protection standard training. Chemical (pesticide) handler safety training. Maricopa, AZ, 3/29/2012.<br /> <br /> University of Arizona, 2012 Annual Meeting of Multistate project NIMSS NCERA-180 Site-Specific Management. The event took place in Maricopa AZ on 3/28-30.<br /> <br /> Manoj Karkee, Automation and Mechanization Research for Specialty Crops (Invited Talk) - Annual Hermiston Farm Fair and Trade Show, Hermiston, OR; Nov 29, 2012.<br /> Pruning Branch Identification for Automated Pruning of Apple Trees (Invited Talk) - Specialty crop engineering solutions workshop, Pittsburg, PA; Nov 28, 2012.<br /> <br /> Manoj Karkee, Agricultural Automation Research at WSU (Invited Seminar) University Putra Malaysia, Selangor, Malaysia, June 29th, 2012<br /> Agricultural Automation Research at WSU (Invited Seminar)  TU, Engineering Campus, Dharan, Nepal, June 25th, 2012<br /> <br /> Manoj Karkee, Precision Agriculture in Specialty Crops: Accomplishments, Challenges and Future Direction. First International Precision Agriculture Forum, Richland, WA, March 15-16, 2012.<br /> <br /> Media articles<br /> Andrade-Sanchez, P., M. Gore, J. White, and A. French. 2012. Information technologies for field-based high-throughput phenotyping. Thorp K., Information technologies for field-based high-throughput phenotyping. Published in Resource: Engineering & Technology for a Sustainable World Vol. 19(5): 8-9 ASABE<br />

Impact Statements

1. NCERA 180 team made advancements in remote sensing and machine vision systems development for applications in HLB detection; debris detection in citrus postharvest environment, biomass yield measurement, cotton plant damage assessment, apple crop load estimation, apple tree pruning, crop water and nitrogen stress sensing and other area of precision and automated agricultural systems. Progresses were also made in understanding interactions of water and nitrogen supply for irrigated corn production and establishing economic benefits of auto guidance and variable rate technologies for peanuts and other crops in Alabama and Georgia. More specific impacts of various projects carried out by NCERA 180 members can be found in the "Accomplishments" section.
2. A model developed to enhance stakeholder input, program planning and outreach to agricultural clientele was further improved this year. This program was funded by the University of Arizona  College of Ag and Life Sciences.
3. Several team members were involved in organizing sessions in the area of Precision and Automated Agriculture for the 2012 ASABE Conference, and the ASA conferences among others.
4. NCERA members continued the mission to advance the science of precision and automated agriculture globally. Now, Precision ag has expanded worldwide (with 12 international divisions from 26 countries registered to ISPA).

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Report Information

Participants

---Day 1 (07-23-2014) Tours:---


Hendrikson, Larry (hendricksonlarryl@johndeere.com) - Deere & Company; Luck, Joe (jluck2@unl.edu) - University of Nebraska-Lincoln; Saraswat, Dharmendra (dsaraswat@uaex.edu) - University of Arkansas; Franzen, Dave (david.franzen@ndsu.edu) - North Dakota State University; Ferguson, Richard (rferguson1@unl.edu) - University of Nebraska-Lincoln; Kitchen, Newell (newell.kitchen@ars.usda.gov) - USDA-ARS; Fulton, John (fulton.20@osu.edu) - Auburn University; Karkee, Manoj (manoj.karkee@wsu.edu) - Washington State University; Lee, Daniel (wslee@ufl.edu) - University of Florida;

---Day 2 (07-24-2014) Business Meeting:---


Luck, Joe (jluck2@unl.edu) - University of Nebraska-Lincoln; Sudduth, Ken (ken.sudduth@ars.usda.gov) - USDA-ARS; Saraswat, Dharmendra (dsaraswat@uaex.edu) - University of Arkansas; Franzen, Dave (david.franzen@ndsu.edu) - North Dakota State University; Ferguson, Richard (rferguson1@unl.edu) - University of Nebraska-Lincoln; Kitchen, Newell (newell.kitchen@ars.usda.gov) - USDA-ARS; Khosla, Raj (raj.khosla@colostate.edu) - Colorado State University; Nowatzki, John (john.nowatzki@ndsu.edu) - North Dakota State University; Fulton, John (fulton.20@osu.edu) - Auburn University; Karkee, Manoj (manoj.karkee@wsu.edu) - Washington State University; Lee, Daniel (wslee@ufl.edu) - University of Florida;

Brief Summary of Minutes

NCERA 180 Meeting Summary of Meeting Minutes
Thursday, 07-24-2014, 8:05 AM, Hyatt Regency Hotel, Sacramento, CA

A combined business meeting was held initially between the NCERA-180 and W-2009 groups.

Charlie Lee (UGA), W-2009 Chair and Manoj Karkee (WSU), NCERA-180 Chair presided (Summary does not include W-2009 discussions).

State reports are due within 30 days, they will be emailed to Joe Luck. Joe will send out a template, the final report is due 09-18-2014.

Van K. (180 Admin Rep) NCERA 180 renewal is coming up in the fall of 2015. Next year, we will have to get materials together. February of 2015 will be the time to review application materials. We have 60 days to complete/submit the report from today. USDA climate hubs (climate changes, excessive drought, moisture, etc.) have been designated, but no funding has come yet. An AFRI call in 2015 related to the climate hubs may be out. Appropriations (E & R) activities at the federal level are continuing to erode, impact statements, reporting are going to be critical for continuing funds.

Joe Luck mentioned potential USDA focus on extension and technology transfer activities. Ken Sudduth mentioned that past A-Z sessions were to focus on these types of activities related to precision ag. Joe Luck also mentioned that program impact measurements and surveys for extension professionals might be very beneficial if ISPA could develop some program or outreach/tech sessions along those lines.

ISPA: Ken Sudduth updated that the organizing committee was pleased that W2009 and NCERA-180 were able to co-locate their meetings with ICPA. They hope that the group(s) might consider the same for the 2016 ICPA conference (location TBD).

DC Update from Dan Schmoldt:
Reiterated the importance of impact documentation from programs (simple 4 sentences if written correctly can be useful) and this will help his group continue to go help secure funds for ag research, extension, and educational programs. Overview of the 2014 Farm Bill. Mandatory funding for several programs has returned (SCRI, OREI, BFRDP). Centers of Excellence were approved for funding, this will give priority to researchers if they are involved, for competitive grants. This could be very important for securing federal funding in the future!

Matching requirements for FY 2015 may not require matching funds (AFRI SCRI) 100% match from non-project resources will be required. However, land grants (and other institutions) will not be required…this will be exceptions, see programs for details. This will likely impact non-land grand universities, for example. Dan reviewed federal funding status, AFRI should have around $325M. Recommendation from PCAST report suggested to create innovation institutes to research issues related to agriculture projects. Three requested at $25M per year (total $75M for this year) but the proposal was not approved. Review the PCAST report if you would like to see some of the major topics identified for research needs.

AFRI Foundation support will be around $120M in 2014. Ag System Technology, trying to get back to releasing RFA around August, September (original cycle). Program description is quite broad again, funding will continue as in the past, $9M will be available for the current year. Challenge Areas will have $27M for 2014 (Food Security, etc.).

NRI program has continued interested among multiple agencies (NSF, NASA, DARPA, DoD). MOUs among the agencies are being signed. RFA is coming out in a couple of weeks, proposals due in November. Average NIFA NRI funds has averaged around $818K. SCRI program has $80M per year for the foreseeable future. Integrated projects are still mandatory. $25M set aside for citrus insect and disease monitoring. New matching funds requirement will apply.

Brief review of other programs currently in the: NIFA Fellowships, SARE, NIWQ program. Organic Ag Research and Extension, Crop Protection and Pest Management, Alfalfa and Forage Research Program, Beginning Farmer and Rancher program.

New MOU w/ NSF for Cyber-Physical systems was just signed. Internet of Things, creating a smarter agricultural system. Solicitation will likely come out in January.

Group Questions for Dan Schmoldt:
Q: Cyber physical relations-how will ag collaborators deal with data privacy.
A: data security is one emphasis of the program, certain levels need to be considered, server, cloud, wireless networks.
Q: Will CARE program continue?
A: CARE program will be a part of the AFRI Foundation program, they will continue. Director was very supportive of this particular program.
Q: Comment of specialty crop block grant program at the state level?
A: Funds go directly to Dept. of Ag within each state, the states decide which projects. Future status of that program (Ag Market Service) is unknown by Dan.
Further group discussions on federal funding: Dr. Lee: NRI funded 4 projects within the group of W2009 in the previous year. WSU is moving toward a project for mechanized apple harvesting.

Recap of Technical Tours/ICPA:
Tours were very good, Srini Upadhyaya did an excellent job with tours, the numbers of tourists and questions posed by the group was an indication of success. Could attendees be “parsed” by country? This would be a great metric as well in terms of interest by others. W2009 will be in September 13-16, co-located with a meeting in Prosser (Food, Vegetable Handling). Group agreed that tours were excellent, sessions w/ ICPA were great.

UAV needs-imagine stitching is a priority concern, this can be very costly. Future uses besides imagine (data collecting for samples) is an example. Reza Ehsani shared their experience with applying for a COA for flying UAVs for research activities. Dharmendra Saraswat also shared some experiences with U of Arkansas. Richard Ferfuson also contributed some experiences from UNL. Some type of improvement/development component likely needs to be included in COA application.

Comments about ASABE 2014: Safety standards for robotics and automated field. Three committees were involved (along with IEEE), MS-58, IET-318, MS-48 with initial discussions. A task force committee was formed. eForums will be used with ASABE to get information, a website at WSU will be hosted to keep information.

CIGR world congress will be in Beijing in September. Some key symposia will be associated with that meeting. International forum for precision agriculture (round table) to discuss future issues.

ECPA 2015 will be in Israel in July.

Update on past AETC meeting was provided by Joe Luck and mention of the future 2015 AETC meeting dates and location (02-09 and 02-10) in Louisville, KY.

Officer Elections for upcoming year:
NCERA-180: Election for a Vice-Chair. Dr. John Fulton from Auburn was nominated by Joe Luck Nominations were closed, and Dr. Fulton was accepted by acclamation.

Further discussions focused on planning for the 2015 meeting to be held in Lincoln, Nebraska. Challenges continue to be getting groups interested to attend the meeting. Joe and Richard will begin planning for the meeting and continue to involve the group throughout the planning process.

Future discussions need to focus on the NCERA 180 group mission and developing a unique service that it provides. Attendees felt that the group provide an opportunity for networking which would be especially beneficial for new faculty and those with R&D programs in industry. Government and other public groups may also benefit from continuity.

Van Kelley made a suggestion for group leaders to contact administrators within the region to ensure that their Universities were represented and continue working to get industry involvement.

Accomplishments

---Research Activities by State---<br /> <br /> <br /> ---Arizona---<br /> <br /> <br /> Currently funded projects for the reporting period include:<br /> <br /> Precision canopy and water management of specialty crops through sensor-based decision making. Pedro Andrade-Sanchez, Edward Martin, Murat Kacira, James Walworth, Trent Teegerstrom. Research funded by Specialty Crops Research Initiative - NIFA - USDA.<br /> <br /> Sensor-based Management of Mid-season N Fertilizer in Durum Wheat. Pedro Andrade-Sanchez and Michael Ottman. Research funded by Arizona Grain Research and Promotion Council.<br /> <br /> Development of economically viable variable rate P application protocols for desert vegetable production systems. 2013-2015. Pedro Andrade-Sanchez and Charles Sanchez. California Department of Food and Agriculture. Fertilizer Research and Education Program.<br /> <br /> Field-level analysis of yield variability in irrigated cotton in Arizona – 2013-2014. AZ Cotton Growers Association.<br /> <br /> <br /> ---California---<br /> <br /> Precision canopy and water management of specialty crops through sensor-based decision making (SCRI-USDA-NIFA-2010-01213. During 2014 growing, we continued our work with precision canopy and water management. The goal of this project is to develop technologies necessary to implement precision canopy and water management in specialty crops and to evaluate socio-economic implications of such practices. Please visit http://ucanr.org/sites/PCWM/ for more information on the project.<br /> <br /> Canopy Management: A knowledge of spatio-temporal variability in potential yield is essential for site-specific nutrient management in crop production. The objectives of this project were to develop a model for photosynthetically active radiation (PAR) intercepted by almond and walnut trees based on data obtained from respective tree(s) and estimate potential crop yield in individual trees or in blocks of five trees. This project used proximally sensed PAR interception data measured using a lightbar mounted on a mobile platform and a crop growth model to estimate potential yields of almond and walnut trees. An analytical model was developed to estimate PAR intercepted by the tree in which tree canopy was assumed to be spherical in shape. PAR intercepted by a tree was estimated taking into account the effect of row spacing, tree spacing within the row, latitude and longitude of the orchard, day of the year and row orientation. Scans were collected at solar noon in almond and walnut orchards during the 2012 and 2013 growing seasons. Moreover, diurnal scans were also collected during 2012. The results for the 2012 and 2013 seasons showed that the total amount of PAR intercepted by a block of five trees at any time during the day can be found analytically using one lightbar scan, early in the season, as a reference to estimate the radius of the canopy and its optical density. A good correlation was found between measured values of PAR intercepted and estimated values of PAR intercepted.<br /> <br /> A good correlation was also found between yield (for both actual and potential) and absolute midday PAR intercepted, and between actual and potential yield for both almond and walnut trees. Moreover, the actual yield from those blocks with lower absolute midday PAR intercepted was closer to their respective potential yield than those with higher absolute values of PAR intercepted. This result indicates that there is a potential to use spatially variable PAR interception data to implement site-specific input management and enhance production. The simulations showed that the effect of the tree spacing over the orchard productivity is strongly influenced by the tree size. This highlights the need to evaluate the effect of the tree growth over several seasons to obtain the orchard configuration that maximized the profit in the long run. Our results also suggest that the overlap effect is an important factor to be considered by models of PAR interception. This shading is the key information necessary to optimize spacing between trees so that they can capture maximum amount of PAR.<br /> <br /> Precision Water Management: <br /> <br /> Continuous Leaf Monitor: Almond and walnut are two major crops grown in the Central Valley of California. With virtually no rainfall in this area during summer, these crops need to be irrigated throughout the season. There is a demand for using irrigation scheduling tools for effective use of very limited supply of water available in California. Leaf temperature measurement using infrared thermometers has been used to predict plant water stress or to develop different indices to quantify plant water stress, but mostly on field crops. There have been very few studies conducted on tree crops. <br /> <br /> In this study, an inexpensive, easy to use sensing system called ‘Leaf Monitor’ was developed and evaluated to continuously measure leaf temperature and relevant microclimatic variables in the vicinity of a leaf for prediction of plant water status for tree crops. The system was installed on orchard trees to continuously monitor a selected leaf on each tree by logging leaf temperature, air temperature, relative humidity, wind speed and Photosynthetically Active Radiation (PAR). This study also proposed a method to develop a modified crop water stress index (MCWSI) in which reference well watered baseline was developed after every irrigation event for each tree for incorporating any temporal variability throughout the season. Design of leaf monitor also assists in controlling levels of disturbance variables like wind speed and light conditions. Leaf monitors were installed in almond and walnut orchards as a part of a wireless mesh network. <br /> <br /> Data were obtained remotely over the web, and daily MCWSI values were calculated by assigning first day after irrigation as the reference day. MCWSI values were found to be highly correlated with measured plant water stress. Sensing system has potential to be used as irrigation scheduling tool as it was able to provide daily stress index value which follows similar pattern as the actual plant water stress.<br /> <br /> Thermal IR Based Canopy Temperature Sensing Using a Drone Copter (Unmanned Aerial Vehicle): Monitoring water stress in specialty crops to increase water use efficiency (WUE) is becoming more necessary when faced with the reality of depleting water resources. Leaf temperature (TL) of almond [prunus dulcis] and walnut [juglans regia] trees has been shown to be closely linked to stem water potential, a sensitive indicator of stress in woody plants.<br /> <br /> This study was conducted to explore the feasibility of remotely measuring canopy temperature (Tcan) of walnut and almond trees with a small, inexpensive unmanned aerial vehicle (UAV). An infrared (IR) point sensor was installed with a lightweight camera on the underside of a multi-rotor UAV. The UAV was flown over a targeted tree canopy recording temperature and images. Image classification was used to identify the ground contents of each temperature measurement, and a linear system of equations utilizing the image/temperature records pertaining to a targeted tree canopy was established to approximate the temperature of the sunlit and shaded portions of that canopy. <br /> <br /> Analyses of three flights over almond tree canopies approximated the temperatures of the sunlit and shaded portions of the canopies within an average of 2.2oC of their respective ground truths for both portions, and analyses of four flights over walnut canopies approximated the sunlit and shaded portions within 1.0 and 1.3oC of their respective ground truths, the average difference for all temperature approximations between the seven trees being 1.5oC. With canopy temperatures ranging from 16 to 40oC, the approximations fit a linear trend with a coefficient of determination (r2 value) of 0.96.<br /> <br /> The use of an IR sensor coupled with a camera to establish a linear system of equations for individual trees showed promising ability to approximate a tree’s canopy temperature. This method also has the advantage of distinguishing between the sunlit and shaded portions of the canopy.<br /> <br /> <br /> ---Florida---<br /> <br /> A novel technique was developed to detect immature green citrus in tree canopy under natural outdoor conditions. Shape analysis, texture classification, SVM, graph based connected component algorithm, and Hough line detection were used to identify fruit and to remove false positives. Keypoints by scale invariant feature transform algorithm were used to further remove false positives. The algorithm was able to detect and count 80% of citrus fruit for validation. This method was published in the Biosystems Engineering. An algorithm for detecting immature peach fruit on the tree was also developed and was published in the Precision Agriculture journal. Hyperspectral images of blueberry fruit were taken in a commercial blueberry field. Mature fruit, intermediate fruit, young fruit and background were the four classes to be studied. A supervised band selection method was proposed using Kullback-Leibler divergence (KLD). Based on the analysis, six combined bands were selected. The test result showed that the proposed band selection method worked well for the task of blueberry growth stages detection.<br /> <br /> Using polarized filter and narrow band imaging technique, a portable machine vision system was developed to detect the citrus greening symptomatic leaves. This study yielded detection rates of over 90%, and this method was accepted for publication in the Transactions of the ASABE in 2014. Different dimension reduction methods were investigated to detect the citrus greening disease using airborne hyperspectral imaging. These methods yielded detection accuracies of 63-93%. A novel detection method, ‘extended spectral angle mapping (ESAM)’ was developed to detect citrus greening disease using Savitzky-Golay smoothing filter, SVM and vertex component analysis. A high detection accuracy of 86% was achieved for validation. Also satellite images were used to detect citrus greening disease over large areas from a Landsat 5 Thematic Mapper (TM) and a WorldView-2 images. It was demonstrated that there is a great potential for citrus greening disease detection using a satellite image.<br /> <br /> Another machine vision system was developed to detect dropped citrus fruit on the ground along with a GPS receiver. It can count the number of fruit and estimate mass of the fruit with an accuracy of 89%. <br /> <br /> A prototype laser weeding system was developed to kill in-row weeds using machine vision and a set of lasers to demonstrate the concept. This work was presented at the 5th Asian Conference on Precision Agriculture. An automated in-row weed control system was being tested, consisting of an ultrasonic sensor and a pair of pinch rollers. A preliminary field test results showed that the mechanical weeding machine were able to uproot weeds. Weeds height was ranged from 10 cm to 18 cm. Further field experiments will be conducted to evaluate the efficacy of the intra-row weeding prototype and crop injury.<br /> <br /> Equilibrium moisture content (EMC) for triticale seed was investigated. A prediction algorithm was developed to represent the relationship between relative humidity and EMC with coefficient of determination (R2) equal to 0.99. It was also found that the Modified Henderson equation represents this relationship accurately. A method was developed to determine the degrees of infestation (DI) in the triticale seed at two growth stages by measuring their spectral reflectance. The reflectance was measured from 400 nm to 2500 nm. The result showed that the DI for larvae 2nd instar stage could be detected using an average reflectance in 400 - 410 nm, with an R2 of 0.87. The adult outside stage also resulted in a good prediction, where it yielded four wavelengths that provided an acceptable result with an R2 of 0.87 for the adult outside stage. <br /> <br /> <br /> ---Kansas---<br /> <br /> <br /> Use of sUAVS in agricultural applications:<br /> <br /> Research projects are being conducted in Manhattan (KS) at the Kansas State University –Department of Agronomy- on winter canola and corn crop. The goals of this group are to identify the potential uses of the sUAVS for agronomic crops with emphasis on serving large farming systems and also research programs. The team is also working with the Agricultural Economics department to understand the benefits and costs in using this technology for providing services to farmers, public and private sectors. In addition to the latter, collaborations with the Aviation program at K-State Salina for developing new platforms and improving data collection are continuously pursued.<br /> <br /> On the research side, measurements in corn are involving but not limited to: canopy temperature, photosynthesis, leaf area index, plant height, number of green leaves, and biomass. The collection of new information using the sUAVS is related to the calculation of NDVI (estimation of biomass) and canopy temperature (thermal camera). Extensive testing of this last technique is currently under evaluation in collaboration with scientist of the Department of Biological and Agricultural Engineering.<br /> Canola biomass, nutrient uptake, and stand counts. Measurements of biomass and nutrient concentration are correlated with NDVI determinations at diverse growth stages. Determination of early stand count is also pursued comparing “ground truthing” information with the imagery collected from the sUAVS.<br /> In overall, these examples of the use of sUAVS together with the preparation of support decision tools will determine the potential contribution and impact of this technology in the precision agriculture discipline with the primary and ultimate objective of assisting producers, crop advisors, and other agri-business professionals for facilitating the decision-making process.<br /> <br /> <br /> ---Nebraska---<br /> <br /> <br /> At the University of Nebraska-Lincoln, several projects involving site-specific crop management and precision agriculture technology research include Richard Ferguson in the Department of Agronomy and Horticulture and Joe D. Luck in the Department of Biological Systems Engineering. Projects include:<br /> <br /> <br /> Interactions of Water and Nitrogen Supply for Irrigated Corn across Field Landscapes: This existing project was completed in 2013. The primary goal of the project was to evaluate response of irrigated corn to site specific water and N management across variable landscapes. The project provided a good deal of infotrmation regarding the performance of various soil moisture sensing technologies and the potential for variable-rate water and N management.<br /> <br /> <br /> Evaluation of Scrubber Ash as Soil Amendment: This research project was initiated in 2013, with the goal being to establish potential uses for this coal-production by product. This project utilized site-specific management application techniques to apply, track, and evaluate the addition of Scrubber Ash and the effects on corn yield. Preliminary results are expected in the fall of 2014.<br /> <br /> <br /> Improving Irrigation Water and Energy use Efficiency through Accurate Spatial and Temporal Management: The primary goal of this project is to determine guidelines for the adoption and management of variable-rate irrigation (VRI) systems based on spatial datasets that can be acquired by producers throughout the growing season. Objectives include the demonstration of potential water and energy savings by adopting VRI management. <br /> <br /> <br /> Deployment and Evaluation of a Tracking System for Improved Animal Management: Precision livestock management is evolving as a potential method for improving management of animals in confined spaces. The goal of this project is to build on preliminary research using commercially available systems to track animal movement. Specific objectives include the development of advanced identification techniques based on animal behavior signatures.<br /> <br /> <br /> Promoting Adoption of In-Season Canopy Sensor-Based Nitrogen Fertilization of Corn through the Nebraska On-Farm Research Network: While primarily an Extension activity, data will be collected during this project to improve N recommendation algorithms for use with in-field N application using crop canopy sensors. Demonstration sites will be setup across the state in five difference Natural Resource Districts to cover a broad range of climatic regions. <br /> <br /> <br /> ---North Dakota---<br /> <br /> Use of precision farming methods has increased in North Dakota within the past several years. Many farmers use auto-steer on their farm machinery, have yield monitors in their combines and DGPS aids in variable-rate fertilizer application. Variable-rate fertilizer application is common across North Dakota. AgVise Laboratories, Northwood, ND, is the state’s largest soil and plant analysis laboratory for agriculture. They reported in December, 2013 that they received more samples from zone-sampled fields than they received for whole-field composite tests for the first time in 2013. Use of precision farming methods has been adopted by growers of all crops, not just sugar beets.<br /> <br /> Research and Extension education in precision farming methods is supported by the following efforts (Franzen).<br /> <br /> Publication April, 2014 of new corn N recommendations with emphasis on side-dress N application using active-optical sensor algorithms for the GreenSeeker and Holland Crop Circle sensors. Publication by September 1, 2014 of algorithms for corn using the GreenSeeker and Crop Circle sensors. Ongoing research into additional or complimentary in-season N management tools for corn. Research began spring 2014 to build a database to support the use of active-optical sensors for in-season N application in sunflower. With work from Anne Denton and her colleagues and students in the NDSU Computer Science Department, a ‘machine-learning’ program for use by farmers adopting active-optical sensor in-season corn N application is being developed. With this tool, farmers can incorporate on-farm active-optical sensor data with published sensor algorithms, and morph published NDSU algorithms into their personal farm algorithm. Preliminary relationships between active-optical sensors and sugar beet yield and quality have been established and more work on improving the data base is planned. Relationships between NDVI satellite imagery (Rapid-Eye) and corn, sunflower, spring wheat and sugar beet yield have been produced, but more data is required to use these relationships as a logistics tool for farmers to screen fields for in-season N application and determine total in-season fertilizer N needs. With Joel Ransom, NDSU Plant Sciences Department, data is being accumulated to support the use of active-optical sensors with red edge NDVI capabilities to predict the need for post-anthesis N application for spring wheat protein enhancement to avoid protein discounts and take advantage of possible higher protein premiums from grain buyers.<br /> <br /> Additional Research efforts supported (Nowatzki) include: Use of UAV’s to enable improved field scouting, usefulness of compaction sensing for improved crop yield in North Dakota, and Use of active-optical sensors for use in aiding soybean fertilization (with Hans Kandel, Plant Sciences Department). Research from the Computer Science Department (Denton) include the use of data-mining techniques has shown that rainfall data can be used most effectively by relating monthly rainfall data to sugar beet yield and quality prediction. Ignoring rainfall amounts greater than 1 inch within a 24 hour day improved model prediction. Including small amounts of rainfall within a 24 hour period was surprisingly important to include in the model.<br /> <br /> <br /> ---Washington State University---<br /> <br /> At Washington State University, Manoj Karkee, Qin Zhang and their teams conducted several research projects and achieved substantial accomplishments in developing systems and technologies for precision and automated agriculture. Major projects and corresponding accomplishments are listed below.<br /> 3D Machine Vision for Improved Apple Crop Load Estimation <br /> Accurate estimation of apple crop-load is essential for efficient orchard management. We designed an over the row platform capture images from two side of apple canopies to minimize the occlusions and improve the accuracy of crop-load estimation. A tunnel structure was used to minimize the variation in lighting condition and artificial lights were installed for night time operation. A color camera, a 3D camera and an orientation sensor were mounted in the sensor platform and were moved along rows of apple trees in three different commercial orchards of Allan Bros. Inc., Prosser, WA. Images were capture in both day and night times. A apple identification algorithm and a 3D mapping algorithm was used to count apples while avoiding duplicate counting of apples that were visible from both sides of tree canopies. Crop load estimation improved by approximately 20% when imaged from two sides compared to that with single-side imaging.<br /> Human machine collaboration for automated harvesting of tree fruit <br /> The long-term goal of this work is to reduce dependency on human labor through mechanization and human-machine collaboration while increasing yields of premium quality fruit. The overall objective is to develop a framework for knowledge transfer and collaboration between human and machine. This objective will be achieved through the understanding of the dynamics of the hand picking of fruit, development of an effective end-effector based on the knowledge of hand picking, and a framework of hardware and software for optimal collaboration between human and machine for fruit identification. A trans-disciplinary team of experts is involved in this project, which is crucial for the successful completion of these activities.<br /> Development and Optimization of Solid-Set Canopy Delivery Systems for Resource-Efficient, Ecologically Sustainable Apple and Cherry Production<br /> This project is a subcontract to a SCRI project with Michigan State University. This is a multidisciplinary research and extension project to develop, evaluate, and deliver resource-efficient, innovative management technologies and tactics for apple and cherry production systems. It aims to establish innovative delivery technologies for canopy and orchard floor inputs (including high efficiency irrigation systems, precision-activated micro-emitters, and reduced risk pesticides) to address critical fruit production needs as identified by commodity PMSPs and the Technology Roadmap for Tree Fruit Production. Direct outcomes of system implementation that will be analyzed include: economic and agro ecosystem impacts. Sociological research will focus on how these integrated technologies impact urban-farm relations, barriers to grower adoption, and how these factors can inform better extension and educational programmatic efforts. <br /> Design and Development of Apple Harvesting Techniques<br /> This research aimed to investigate a few conceptual end effector designs capable of harvesting a cluster of apples in fruiting wall canopy architecture. A rotational robber-wheel based technique and a dual motor-based shaking system were developed and evaluated. Initially, the technology has been applied to apples but the extension to other similar-size fruits such as pears may be possible. The results from 2 years of field evaluation showed a successful fruit removal technique applicable to apples grown on a trellised orchard system. Signs of branch punctures were visible on some of the apples. A method to seclude the apple from any branch was tried. Apples that were grown on small spurs tended to be removed easier than apples growing on long flexible branches. Long branches allowed apples to move more than apples growing on small spurs. This flexibility reduced the effectiveness of both rotational and shaking mechanisms. Horticulture can play an important role in aiding the fruit removal technique described in the above research.<br /> Multi- and Hyper-spectral imaging for potato stress sensing <br /> Hyperspectral imaging system was used as a non-contact sensing instrument in this study for detecting nitrogen stress in potato plants non-destructively. An experiment was setup with potato plants planted at five different nitrogen levels in a research field. Reflectance plots of plant leaves at different nitrogen levels showed differences in spectral signature. Spectral indices were calculated from reflectance data and were correlated with nitrogen levels measured in the lab with leaf samples. A multivariate linear model is being developed in this study to predict nitrogen levels using reflectance at different frequencies. <br /> Red Raspberry trellising demonstration plot for development of automation <br /> Technologies: Cane management in red raspberry production is highly labor intensive. Labor availability is uncertain at best and labor cost is increasing. Currently, Washington growers estimate the pruning and tying cost in red-raspberry production to be from $500 to $800 per acre. In addition, labor is at risk for chronic and acute injury. Mechanization has the potential to substantially reduce labor use from cane management. Through this project, we established a red raspberry plot in Prosser, WA and collected preliminary data with different horticultural systems established in Mt. Vernon, WA. Further evaluations of different types cane management techniques will be carried out to identify mechanization friendly system for pruning and tying while maintaining or improving fruit quality and yield. <br /> Field phenomics platform development <br /> This project is to develop a field research platform and conduct preliminarily tests based on an automatically navigated and steered agricultural tractor. Field-based sensor and imaging devices will be mounted on the platform to efficiently collect data related to crop productivity, input-efficiency, and health while simultaneously applying methods to determine and account for spatial variation due to soil heterogeneity. This automated field research platform, with state-of-the-art imaging, sensing, and positioning/guidance systems, will be capable of rapid, in situ, assessment of crop nutrient and water status, crop health, vigor and productivity, and other important characteristics.<br /> <br /> <br /> ---Extension/Outreach/Teaching Activities by State---<br /> <br /> <br /> ---Arizona---<br /> <br /> <br /> Invited Seminars:<br /> Tree monitoring for advanced irrigation control. Presentation at the Arizona Pecan Growers Association Annual Meeting on the use of soil and plant sensors for irrigation timing in pressurized systems. Tucson, AZ, 9/13/2013.<br /> <br /> Development of cotton management app for irrigated cotton in AZ. CottonInc precision management meeting. New Orleans, LA, 1/8/2014. <br /> <br /> Variable rate management of P fertilizer for vegetable production in the low desert. Presentation at the Yuma <br /> Ag Summit on first year results of experimental work. Yuma, AZ 2/27/2014.<br /> <br /> Presentations during statewide extension and outreach events:<br /> Cotton pre-season meetings. Topic: Using mobile devices for improved management. Live demonstration on access and use of the University of Arizona cotton calculator with web-enabled mobile devices. Avondale AZ, 2/20/2013; Gilbert, AZ, 2/22/2013; Coolidge AZ, 2/26/2013; Marana AZ, 2/27/2013; and Safford AZ, 2/28/2013<br /> <br /> Delivered field demonstrations for delegations visiting Arizona including: Mexico's INIFAP (2/15/2013), Chapingo University irigation students (10/30/2013) and Chinese irrigation district managers (11/20/2013)<br /> <br /> Workshop in spraying technology using new displays for GPS-based rate and section control (October 10, 2013). Maricopa County Extension.<br /> <br /> Workshops and other events:<br /> Field phenomics part I: Developing and using a sensor array. On-line webinar, October 30, 2013. Materials available at the following URL: http://www.extension.org/pages/68269/part-one-of-field-phenomics:-developing-and-using-a-sensor-array<br /> <br /> Workshop in field-based high throughput phenotyping. NSF-funded workshop that targeted graduate students and early-career plant breeders in the use of electronic equipment such as plant sensors, GPS, data-loggers, and data post-processing and analysis. Maricopa AZ 4/7-10/2014<br /> <br /> Specialized-media articles:<br /> Gabrielle Fimbres. UA-developed apps aid Arizona cotton farmers. UANews, Office of University Communications. University of Arizona. Tucson, AZ. August 27, 2013.<br /> <br /> Karl Haro von Mogel. Taking the phenomics revolution into the field. CSA News 58(3): 4-10. ASA-CSSA-SSSA. March 2013.<br /> <br /> Cary Blake. Variable-rate management of P fertilizer. Western Farm Press. Agricultural Technology and Irrigation. Oct 2013/Vol 18 No.2<br /> <br /> <br /> ---Kansas---<br /> <br /> <br /> Extension Education for Ag Professionals: A total of 10 meetings were provided on the uses of sUAVS in agriculture since the last August 2013 (more than a year). This constitutes a continuous demand for our clientele and key-stake holders in understanding the uses of the new technologies and the impact of those in the future of agricultural production systems. Participation resulted in training to farmers, extension Ag agents, crop consultants in the use and agronomic applications of sUAVS technology. The extension effort impacted 100 agribusiness stakeholders in the last year and continuous to promote information on the use of this technology for agricultural purposes.<br /> <br /> <br /> ---North Dakota---<br /> <br /> <br /> Extension (Nowatzki) continues to have great success with the Precision Ag Workshop in Jamestown, ND. Winter 2014 the event was attended by over 200 producers and ag-industry people over a two day period.<br /> <br /> <br /> ---Nebraska---<br /> <br /> <br /> Recently developed Precision Ag Data Management Workshops have been developed to lead producers through hands-on farm management software activities using agriculture spatial data layers. The focus of the workshops is to communicate how to perform data collection and analysis to enable site-specific crop management in their operations. Emphasis is placed on details to connecting the farm office to the field equipment. Over 120 have attended the workshops in 2013 and 2014; workshops were held in Nebraska and Kentucky and represented a collaborative effort of multiple Universities in the NC region.<br /> <br /> <br /> The annual Nebraska Agricultural Technology Association (NeATA). Association Conference was held in February, 2014. Attendees included crop producers, researchers and advisors related to site-specific crop management and other emerging agricultural technologies. The conference includes a day long symposium followed by a second day of breakout sessions. Over 150 attended the 2014 conference in Grand Island, Nebraska. For more details visit: http://neata.org/ <br /> <br /> <br /> Site-Specific Crop Management. AGRO/MSYM/AGEN 431. Senior level course on agronomic and engineering aspects of site-specific crop management. 3 credit hours. Offered fall semesters. The enrollment in fall 2013 was 55; currently 51 students are enrolled in the fall 2014 semester. <br /> <br />

Publications

Andrade-Sanchez P., M.A. Gore, J.T. Heun, K.R. Thorp, A.E. Carmo-Silva, A.N. French, M.E. Salvucci, and J.W. White. 2013. Development and evaluation of a field-based high-throughput phenotyping platform. Functional Plant Biology 41(1) 68-79.<br /> <br /> <br /> Andrade-Sanchez P. and Heun J.T. 2013. Operation of yield monitors in Central Arizona: Grains and cotton. Bulletin AZ1598. The University of Arizona - Cooperative Extension. Tucson, Arizona 85721<br /> <br /> <br /> Andrade-Sanchez P. and J.T. Heun. 2013. Sensor-based estimation of cotton plant height: Potential for site-specific plant growth management. Paper No. 131668472. ASABE Annual Int. Meetng.<br /> <br /> <br /> Balasundram, S.K, H. Memarian, and R. Khosla. 2013. Estimating Oil Palm Yields Using Vegetation indicesDerived from Quickbird. Life Sciences Journal 10(4) 851-860.<br /> <br /> <br /> Balasundram, S.K, H. Memarian, M.Y. Hakimah, L. Osmund, S. Nini and R. Khosla. 2013. Comparison between Pixel-based and Object-based Image Classification of a Tropical Landscape using SPOT-5 Imagery. J. Appl. Remote Sens. 7(1), 073512 (Aug 28, 2013). doi:10.1117/1.JRS.7.073512<br /> <br /> <br /> Bansal, R., W. S. Lee, and S. Satish. 2013. Green citrus detection using Fast Fourier Transform (FFT) leakage. Precision Agriculture 14(1): 59-70. http://dx.doi.org/10.1007/s11119-012-9292-3.<br /> <br /> <br /> Cao, Q., Y. Miao, H. Wang, S. Huang, S. Cheng, R. Khosla and R. Jiang. 2013. Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research. 154:133-144<br /> <br /> <br /> Choi, D., W. S. Lee, R. Ehsani, and A. Banerjee. 2013. Detecting and counting citrus fruit on the ground using machine vision. ASABE Paper No. 131591603. St. Joseph, Mich.: ASABE.<br /> <br /> <br /> Crawford, K., J. Roach, R. Dhillon, F. Rojo., and S. K. Upadhyaya. 2014. An inexpensive aerial platform for precise remote sensing of almond and walnut canopy temperature. A paper presented at the 12th International Conference on Precision Agriculture in Sacramento, CA, USA. July 20-23.<br /> <br /> <br /> Dhillon, R., V. Udompetaikul, F. Rojo, S. Upadhyaya, J. Roach, and D. Slaughter, B. Lampinen, K. Shackel. 2014. Detection of plant water stress using leaf temperature and microclimatic measurements in almond, walnut, and grape crops. Transaction of the ASABE (In press).<br /> <br /> <br /> Dhillon, R. S., F. Rojo, J. Roach, and S. Upadhyaya. 2014. Handheld sensor suite for plant water status measurements and a comparison of different techniques to measure canopy temperature in orchard crops. ASABE paper 141893976. ASABE St. Joseph, MI 49085.<br /> <br /> <br /> Dhillon, R., F. Rojo., J. Roach., R. Coates, S. K. Upadhyaya, M. Delwiche, and C. Han. 2014. Development and evaluation of a leaf monitoring systemfor continuous measurement of plant water status in almond and walnut crops. A paper presented at the 12th International Conference on Precision Agriculture in Sacramento, CA, USA. July 20-23.<br /> <br /> <br /> Eduardo, Claudio, R. Khosla, and R. Reich. 2013. Interpolation type and data computation of crop yield maps is important for precision crop production. J. of Plant Nut. Soil Analysis. (Accepted in Press).<br /> <br /> <br /> Garcia-Ruiz, F., S. Sankaran, J. M. Maja, W. S. Lee, J. Rasmussen, and R. Ehsani. 2013. Comparison of two aerial imaging platforms for identification of Huanglongbing infected citrus trees. Computers and Electronics in Agriculture 91: 106-115. http://dx.doi.org/10.1016/j.compag.2012.12.002<br /> <br /> <br /> He, L., J. Zhou, X. Du, D. Chen, Q. Zhang, and M. Karkee. 2013. Energy Efficacy Analysis of a Mechanical Shaker in Sweet Cherry Harvest. Biosystems Engineering, 116(4): 309-315.<br /> <br /> <br /> Katti, A. R., W. S. Lee, and C. Yang. 2013. Laser weeding system for elimination of in-row weeds. In Proceedings of the 5th Asian Conference on Precision Agriculture (ACPA), June 25-28, 2013, Jeju, Korea.<br /> <br /> <br /> Khedher Agha, M. K., W. S. Lee, R. A. Bucklin, A. A. Teixeira, and A. Blount. 2013. Equilibrium moisture content equation for triticale seed. ASABE Paper No. 131620333. St. Joseph, Mich.: ASABE.<br /> <br /> <br /> Khedher Agha, M. K., W. S. Lee, C. Wang, R. W. Mankin, N. Bliznyuk, and R. A. Bucklin. 2013. Determination degrees of insect infestation in triticale seed using NIR spectroscopy. ASABE Paper No. 131592957. St. Joseph, Mich.: ASABE.<br /> <br /> <br /> Lee, W. S. 2013. Book review: N. Kondo, M. Monta, and N. Noguchi, Agricultural robots - mechanisms and practice, Corona Publishing Co., Ltd. Tokyo, Japan, 2011, xii + 348 pp., ISBN: 978-4-87698-553-1. Journal of Biosystems Engineering 38(2): i. <br /> <br /> <br /> Li, H., W. S. Lee, and K. Wang. 2013. Airborne hyperspectral imaging based citrus greening disease detection using different dimension reduction methods. ASABE Paper No. 131592802. St. Joseph, Mich.: ASABE.<br /> <br /> <br /> Li, H., W. S. Lee, K. Wang, R. Ehsani, and C. Yang. 2013. ‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture. http://dx.doi.org/10.1007/s11119-013-9325-6.<br /> <br /> <br /> Li, H., W. S. Lee, and K. Wang. 2013. Spectral mixture analysis based citrus greening disease detection using satellite image of Florida. In Proceedings of the 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 25-28 June 2013, Gainesville, Florida, USA.<br /> <br /> <br /> Lo, T.H., L. Mateos, D.M. Heeren, and J.D. Luck. 2014. The applicability of VRI for managing variability in infiltration capacity and plant-available water: a preliminary discussion and GIS study. Paper No. 1897710 in Proc. of the 2014 ASABE International Meeting, July 13-16, Montreal, Quebec, Canada.<br /> <br /> <br /> Longchamps, L., and R. Khosla. 2014. Early detection of nitrogen variability in maize (Zea mays L.) using fluorescence. Agron. J Vol. 106 No. 2, p. 511-518.<br /> <br /> <br /> Longchamps, L., R. Khosla and D.G Westfall. 2013. Can fluorescence based sensing detect nitrogen deficiency at early growth stages of maize? In Precision Agriculture. J. Stafford (ed) Wageningen Academic Publishers. The Netherlands.<br /> <br /> <br /> Marx, S.A. and J.D. Luck. 2014. Assessing Accuracy of Machine CAN Bus Data using SAE J1939 and Nebraska Tractor Test Laboratory Data. Paper No. 1897599 in Proc. of the 2014 ASABE International Meeting, July 13-16, Montreal, Quebec, Canada.<br /> <br /> <br /> Miller, K.A., T.H. Lo, J.D. Luck, and D.M. Heeren. 2014. Combining Site Specific Data with Geospatial Analysis to Identify Variable Rate Irrigation Opportunities in Irrigated Agricultural Fields. Paper No. 1896808 in Proc. of the 2014 ASABE International Meeting, July 13-16, Montreal, Quebec, Canada.<br /> Moshia, M.E., R. Khosla, D.G. Westfall, J.G. Davis, R. Reich and L. Longchamps. 2014. Precision Manure Management across Site-Specific Management Zones: Grain Yield and Economic Analysis. Agron J. (Accepted: In Press).<br /> <br /> <br /> Moshia, M.E., R. Khosla, D.G. Westfall, J.G. Davis, R. Reich and L. Longchamps. 2014. Precision Manure Management across Site-Specific Management Zones: Nitrogen Mineralization Rates. J. of Plant Nut. Soil Analysis. (Accepted: In Press).<br /> <br /> <br /> Moshia, M.E., R. Khosla, D.G. Westfall, J.G. Davis, R. Reich and L. Longchamps. 2014. Precision Manure Management across Site-Specific Management Zones: Soil Quality. Commun. In Plant Sci and Anal. (Accepted: In Press).<br /> <br /> <br /> Pan, L., Adamchuk, V.I., Ferguson, R.B., Dutilleul, P.R.L. and Prasher, S.O. (2014) Analysis of Water Stress Prediction Quality as Influenced by the Number and Placement of Temporal Soil-Water Monitoring Sites. Journal of Water Resource and Protection, 6:961-971.<br /> <br /> <br /> Patil, V.C., Khalid A. Al-Gaadi, Rangaswamy Madugundu, ElKamil H.M. Tola, Samy A. Marey, A.M. Al-Omran, R. Khosla, S. K. Upadhyaya, David J. Mulla and Ali Al-Dosari. 2014. Delineation of Management Zones and Response of Spring Wheat (Triticum aestivum L.) to Irrigation and Nutrient Levels in Saudi Arabia. International J. of Agri. Biol. 1560–8530; ISSN Online: 1814–959613–035/2014/16–1–104–110.<br /> <br /> <br /> Peter J.A. Kleinman, Anthony R. Buda, Andrew N. Sharpley and Raj Khosla. 2014. Elements of Precision Manure Management. In Precision Conservation. J. Delgado (ed) In press. [Book Chapter]<br /> <br /> <br /> Pitla, S.K., J.D. Luck, and S.A. Shearer. 2014. Multi robot system control architecture (MRSCA) for agricultural mobile robots. In Proc. of the 2014 RHEA Conference, May 21-23, Madrid, Spain.<br /> <br /> <br /> Pitla, S.K., S.A. Shearer, J.D. Luck, N. Lin, B.A Schroeder, and A.A. Klopfenstein. 2013 Work and Load Performance Profiles for Agricultural Field Machinery. Proc. of the 71st International Conference on Agricultural Engineering 2013, Hannover, Germany.<br /> <br /> <br /> Pourreza, A., W. S. Lee, E. Raveh, Y. K. Hong, and H. J. Kim. 2013. Identification of citrus greening disease using a visible band image analysis. ASABE Paper No. 131591910. St. Joseph, Mich.: ASABE.<br /> <br /> <br /> Qiang Cao, Yuxin Miao, Guohui Feng, Xiaowei Gao, Fei Li, Bin Liu, Shanchao Yue, Shanshan Cheng, R. Khosla, and Susan L. Ustin. 2014. Active canopy sensing of winter wheat nitrogen status: an evaluation of two sensor systems. J. of Comp. Ag. (Accepted: In Press)<br /> <br /> <br /> Reich, R., A. Mohammed, R. Khosla, C. Aguirre-Bravo and M. Mendoza. 2014. Influence of Climatic Conditions, Topography and Soil Attributes on the Spatial Distribution of Site Productivity Index of the Species Rich Forests of Jalisco, Mexico. J. of Forestry Research 25 (1) 87-95.<br /> <br /> Rojo, F., R. Dhillon, S. Upadhyaya, B. Jenkins., B. Lampinen, J. Roach, K. Crawford, and S. Metcalf. 2014. Modeling canopy light interception for estimating yield in almond and walnut trees. A paper presented at the 12th International Conference on Precision Agriculture in Sacramento, CA, USA. July 20-23.<br /> <br /> <br /> Saber, M., W. S. Lee, T. F. Burks, G. E. MacDonald, and G. Salvador. 2013.An automated mechanical weed control system for organic row crop production. ASABE Paper No. 131593595. St. Joseph, Mich.: ASABE.<br /> <br /> <br /> Sharda, A., J.D. Luck, J.P. Fulton, T.P. McDonald, and S.A. Shearer. 2013. Field application uniformity and accuracy of two rate control systems with automatic section capabilities on agricultural sprayers. Precision Agric. 14(3): 307-322.<br /> <br /> <br /> Shaver, T.M., R. Khosla, and D.G. Westfall. 2014. Evaluation of two crop canopy sensors for nitrogen recommendations in irrigated maize. Journal of Plant Nutrition. Vol 37:406-419. <br /> <br /> <br /> Wang, H., Y. Miao, G. Zhao, Y. Yao, and R. Khosla. 2013. Evaluating different integrated precision rice management strategies in Northeast China. In Precision Agriculture. J. Stafford (ed) Wageningen Academic Publishers. The Netherlands.<br /> <br /> <br /> Yang, C., W. S. Lee, P. Gader, and H. Li. 2013. Hyperspectral band selection using Kullback-Leibler divergence for blueberry fruit detection. In Proceedings of the 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 25-28 June 2013, Gainesville, Florida, USA.<br /> <br /> <br /> Yang, C., W. S. Lee, and P. Gader. 2013. Band selection of hyperspectral imagery for the classification of blueberry fruit maturity stages and leaf. ASABE Paper No. 131593276. St. Joseph, Mich.: ASABE. <br /> <br /> <br /> Zandonadi, R.S., J.D., Luck, T.S. Stombaugh, and S.A. Shearer. 2013. Evaluating field shape descriptors for estimating off-target application area in agricultural fields. Computers Electronics Agric. 96: 217-226.<br /> <br /> <br /> Zhou, J., L. He, Q. Zhang, X. Du, D. Chen, and M. Karkee. 2013. Evaluation of the Influence of Shaking Frequency and Duration in Mechanical Harvesting of Sweet Cherry. Applied Engineering in Agriculture, 29(5): 607-612.<br />

Impact Statements

1. NCERA 180 team members continued to generate positive impacts based on research activities. Advancements in machine vision and remote sensing systems have been accomplished in multiple states. Issues addressed included crop growth and production monitoring and nutrient and water use efficiency improvement for specialty crops and row crops. Additional progress was made related to the integration of UAVs as a sensing platform for crop monitoring using multiple sensors for stress and production tracking. A full list of outcomes can be seen in the Accomplishments and Publications sections of this report.
2. Extension, Outreach, and Teaching activities conducted by NCERA 180 team members have resulted in positive impacts across the U.S. A collaborative extension project consisting of educators from multiple states has taught precision ag data management techniques using intensive hands-on training sessions. These efforts have reached over 120 producers in two states within the first year. Materials placed on the extension websites have been available to others interested in learning these techniques.
3. Conferences and workshops hosted in various states helped educate a variety of clientele (producers, consultants, industry and extension personnel) on integrating current and future technologies and techniques into their operations. Dissemination of extension outputs by team members was conducted using a variety of media outlets including webinars, workshops, extension publications, and presentations. Additional details on the variety of extension, outreach, and teaching activities are documented in the Accomplishments section.
4. 3. Several NCERA 180 team members were involved in organizing sessions in the area of Precision and Automated Agriculture and Site-Specific Crop and Nutrient Management for the 2014 ASABE Annual International Meeting and ASA/SSSA/CSSA Annual Conferences. Team members also presented research findings at multiple conferences and symposia (both national and international).
5. 4. NCERA 180 team members continued to support the advancement of precision agriculture research outreach at an international level through involvement in the International Society of Precision Agriculture. Annual conferences in Europe and the U.S. were held during the reporting period, team members supported these activities by contributing to conference organization and by presenting at various sessions. The ISPA membership has grown from 26 member countries represented in 2013 to 34 member countries in 2014.

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Report Information

Participants

Joe Luck (University of Nebraska-Lincoln), Ken Sudduth (USDA-ARS), Ajay Sharda (Kansas State University), Bruce Erickson (Purdue University), Dave Franzen (North Dakota State University), Sreekala Bajwa (North Dakota State University), Richard Ferguson (University of Nebraska-Lincoln), Newell Kitchen (USDA-ARS), Ignacio Ciampitti (Kansas State University), Raj Khosla (Colorado State University), Dave Clay (South Dakota State University), Van Kelley (South Dakota State University), Daniel Lee (University of Florida), John Fulton (Ohio State University), John Nowatzki (North Dakota State University), Scott Shearer (Ohio State University) and Manoj Karkee (Washington State University)

Brief Summary of Minutes

Accomplishments

----- Arizona -----<br /> <br /> EXTENSION & EDUCATION<br /> Presentation. Cotton late-season meetings: Spectral sensing for cotton defoliation management. Yuma-Parker AZ (7/15-16/2014). Early-season meetings: Creating prescription files from yield monitor, field sampling and images. Safford AZ 3/4/2015. Tent talk: Implementing variable-rate technology: Hardware selection. Avondale, AZ, 7/8/2015. <br /> <br /> Presentation. Delivered field demonstrations on sensor-based management and information-based application technology for delegations visiting Arizona including: Chapingo University irrigation (11/5/2014) and agricultural machinery (2/9/2015); Arizona Department of Environmental Quality (8/28/2014 and 2/20/2015).<br /> <br /> Workshop: Use of sensor technology for farm production. Innovations in Agriculture series, Arizona Geographic Alliance – University of Arizona. 4/11/2015 Maricopa County Extension Office.<br /> <br /> Workshop: Using the Case-IH AFS Pro-600 display to record cotton yield data. On-line webinar Plant Management Network. September 2014 (http://www.plantmanagementnetwork.org/edcenter/seminars/cotton/AFSPro600Monitor/)<br /> <br /> Workshop in field-based high throughput phenotyping. NSF-funded workshop that targeted graduate students and early-career plant breeders in the use of electronic equipment such as plant sensors, GPS, data-loggers, and data post-processing and analysis. Maricopa AZ 3/16-19/2015<br /> <br /> Community outreach. Demonstration of new tractor technology w/ auto-pilot. Maricopa Farm Day 10/25/2014.<br /> <br /> Short course in machine systems for pecan production. New Mexico State University. Las Cruces, NM. 10/20-22/2014 <br /> <br /> Popular Press Article: Cary Blake. Precision agriculture opens research doors in pecan, other crops. Western Farm Press. Agricultural Technology and Irrigation. April 7, 2015<br /> <br /> RESEARCH<br /> Sensor-based management of mid-season N fertilizer in durum wheat. Pedro Andrade-Sanchez and Michael Ottman. Research funded by Arizona Grain Research and Promotion Council.<br /> <br /> Development of economically viable variable rate P application protocols for desert vegetable production systems. 2013-2015. Pedro Andrade-Sanchez and Charles Sanchez. California Department of Food and Agriculture. Fertilizer Research and Education Program.<br /> <br /> Data harmonization and phenomics for crop diseases under heat and drought. Special Cooperative Agreement- USDA-ARS. Pedro Andrade-Sanchez and Jeff White.<br /> <br /> <br /> <br /> ----- Florida -----<br /> <br /> Using starch accumulation on a Huanglongbing (HLB) or citrus greening infected citrus leaf, a new prototype sensor was developed based on machine vision to identify the HLB symptomatic leaves. The sensor consisted of a sensitive monochrome camera and a narrow band polarized illumination system, and was able to detect starch accumulation in HLB infected citrus leaves and differentiate it from leaves having other stresses such as nutrient deficiency. The sensor was tested in actual citrus grove conditions with healthy, HLB symptomatic, and zinc deficient leaf samples. A classifier was developed using simple statistical histogram features and detection accuracies of over 95% were obtained. This sensor system performed better than our first prototype, and increased detection accuracies with much simpler classifiers. Additionally, the effect of grinding citrus leaves was examined toward the disease detection. Four different citrus leaf classes were compared before and after being ground. The results showed that the freeze-dried ground leaves showed better separation among different classes.<br /> <br /> Due to the HLB disease, excessive premature citrus fruit drops may occur up to 25% of the entire production each year in Florida. To objectively and accurately estimate the amount of premature fruit drop at different locations, an in-field machine vision system was developed using a hardware system for automatic image acquisition, and an image processing algorithm using thresholding and K-mean clustering. A random forest classifier was used to remove background objects. The result showed that correct detection accuracies of 92% and 62% were obtained for recently dropped citrus and decayed fruit, respectively. An in-field spatial variability map of the estimated dropped fruit was created to help growers to identify potential problems of the fruit drop.<br /> <br /> An image processing algorithm was developed to forecast the number of immature green citrus and its size prior to harvesting. As a first step, a linear color space, OHTA, was used to segment green citrus from the background. Then, color, shape and texture features of the different objects (immature green citrus, leaves, soil, sky, twigs and branches) were used for identifying green citrus and background. A support vector machine (SVM) classifier was developed using gray gradient co-occurrence matrix and Tamura texture features. Shape features were used as a final step to detect citrus fruit. The method yielded an accuracy of 82% correct identification of immature green citrus fruit.<br /> <br /> Another machine vision system on a conveyor belt was developed to inspect mechanically harvested citrus fruit. Color RGB images were used using object based classification. Three ensemble learning classifiers, AdaBoost, bagging and random forest, were developed using 74 features including color histogram, textures and histogram intersection with immature and mature citrus color model. The three classifiers showed good classification accuracy for mature citrus with a minimum of 97%. Among them, the bagging trees showed the highest accuracies of 92, 89, 97, and 85% for green immature, intermediate, mature and diseased citrus, respectively.<br /> <br /> Toward the development of a citrus yield mapping, a new approach was proposed to identify individual mature citrus from clustered overlapping citrus using a marker controlled watershed method. Seed markers were obtained by morphological filtering and intensity based region growing. Compared to the traditional watershed algorithm, marker controlled watershed did not yield over-segmentation. Then Hough circle detection was used to identify each separated fruit and count them. This proposed method yielded a correct detection accuracy of 86% for a validation set of images.<br /> <br /> A novel laser weeding system was designed and built towards elimination of in-row weeds. The system consisted of machine vision/image processing, peripheral operations and central processing subsystems. A webcam was used to acquire images at regular intervals of travel distance. A binary image was created using OpenCV identifying only the vegetation in the picture. The system was able to differentiate plants (crops, weeds) from soil background. If the vegetation occupied considerable area in small grids in an image, a decision was made to fire laser on weed. A prototype was built and tested for its functionality as well as its real time performance. <br /> <br /> <br /> <br /> ----- Kansas -----<br /> <br /> Use of sUAVS in agricultural applications:<br /> Research projects are being conducted in Manhattan (KS) and across the state. Faculty involved with UAVS research projects are related to the Agronomy Department, Agricultural Biological Engineering (ABE), Veterinary, and Agricultural Economics at the Kansas State University. A PrecisionAg team was created in the last year, the main focus of the group is to facilitate the development and utilization of new technologies on all farming operations.<br /> <br /> Extension Education for Ag Professionals: A total of 20 meetings were provided on the uses of sUAVS in agriculture since the last August 2014 (more than a year). This topic continues to be in high-demand for our clientele, participation (presentations) and demonstrations on field days and other Extension meetings is requested every month from our group in order to provide some insights on how sUAVS can assist farmers and key-stakeholders in developing support tools.<br /> Participation: Providing training to farmers, extension Ag agents, crop consultants in the use and agronomic applications of sUAVS technology.<br /> <br /> Research: Measurements in corn, soybean involved estimation of plant height, biomass, canopy cover, stand counts, leaf area index, and number of green leaves. For corn, determination of early stand count is also pursued comparing “ground truthing” (stand counts at multiple growth stages) data with the imagery collected from the sUAVS. Similar approach has been taken for measuring plant height and biomass (using site-specific positioning). Uniformity of corn canopy is also estimated via imagery collection, in calibration with biomass samples across the entire cornfield (approx. 3 acres). Measurements related to thermal imagery projects are performed around the state in farming settings, but also in greenhouse and growth chambers (for calibration purposes). The latter technique was developed by Dr. Sharda (ABE Department) and it is currently calibrated under field conditions (different hybrids) on several on-farm fields.<br /> Overall, the past year was primarily dedicated to evaluate examples on the use of sUAVS together with the beginning of preparation of support decision tools for implementing those in the farming decision-making process. Still, there is a lot to be done on our side to determine the “true” potential of this new technology within the precision agriculture discipline. Theoretical to pragmatic projects are currently implemented with the final goal of developing sUAVS technologies that can assisting producers, crop advisors, and other agri-business professionals for facilitating the decision-making process.<br /> <br /> <br /> <br /> ----- Missouri -----<br /> <br /> <br /> EXTENSION & EDUCATION<br /> <br /> The University of Missouri has developed a Precision Agriculture Certificate program that is offered to full time MU students and through extension programming to producers and consultants. The certificate requires successful completion of four courses: 1) Precision Agriculture Science and Technology; 2) Machinery Management Using Precision Agriculture Technology; 3) Data Management and Analysis Using Precision Agriculture Technology; and 4) Profit Strategies Using Precision Agriculture Technologies. The courses are taught using classroom, online, and lab experience methods. In addition, course #1 listed above is a core class for a precision agriculture emphasis for several MU degree programs, with average annual enrollment of about 35.<br /> <br /> RESEARCH<br /> - A completed journal manuscript describes how soil and landscape properties were used to develop higher-resolution crop management zones than those provided by traditional soil survey soil maps. Yield maps were used to validate these management zones. <br /> - Agricultural Policy/Environmental eXtender (APEX) simulation of annual crop yields at different landscape positions worked well for average yields, but the temporal variability of simulated yields in response to water availability (dry and wet years) was lower than measured. The reason for this discrepancy is being investigated. <br /> - A collaborative project with researchers at 8 Land Grant universities is in its second year. This project examines the performance of in-season corn nitrogen management tools, including canopy sensing methods over a wide range of soil and weather scenarios (16 sites per year). Data analysis and interpretation to evaluate these tools relative to yield, profitability, fertilizer use efficiency, and off-field nitrogen loss has begun, with first year’s results to be reported at the 2015 ASA-CSSA-SSSA annual meeting in November. <br /> - Yield and profitability assessment data from a precision agriculture system (PAS) research field located near Centralia, MO continues. The PAS plan takes advantage of targeted management that addresses both crop production and environmental issues. The plan includes no-till, cover crops, growing wheat instead of corn for field areas where depth to the argillic horizon is shallow, site-specific N for wheat and corn using canopy reflectance sensing, variable-rate P, K, and lime using intensively grid sampled data, and targeting of herbicides based on weed pressure. Yield has slightly improved for corn (5%) and soybean (9%) with PAS over pre-PAS management. Risk as measured by grid-cell year-to-year yield coefficient of variation decreased 57% when comparing where wheat replaced corn with PAS, but has remained unchanged for soybeans. This field and the PAS system are now part of the USDA-ARS Central Mississippi River Basin Long Term Agroecosystem Research site. New soil, air, and weather instrumentation has been installed and is being evaluated. <br /> - The combination of near-infrared reflectance spectroscopy, soil apparent electrical conductivity sensing, and soil strength sensing was used to estimate parameters important in soil quality. Initial results were inconclusive, and a larger, more diverse dataset is being assembled. <br /> <br /> <br /> ----- Nebraska -----<br /> <br /> EDUCATION<br /> Site-Specific Crop Management: AGRO/MSYM/AGEN 431<br /> This senior-level course combines agronomic and engineering/technology aspects of site-specific crop management. The course is 3 credit hours, offered fall semesters. Enrollment has steadily increased, from 28 students in 2012 to 67 students in 2015. Curriculum is adjusted on a yearly basis to include state-of-the-art technologies and analysis methods that reflect real-world applications to give students experience with challenges they will face as they enter the job marketplace.<br /> <br /> Curricula Development <br /> The University of Nebraska – Lincoln is part of a multi-state effort – “Precision Farming Workforce Development: Standards, Working Groups and Experimental Learning Curricula” working towards updating curriculum for using in precision agriculture courses. At this stage of the project Nebraska has been marginally involved; efforts have focused on evaluating educational needs from industry, and on assembling syllabi from precision ag courses nationally. In 2016 we will become more involved with development of draft curricular materials, and evaluation of those materials in AGRO/MSYM/AGEN 431, Site-Specific Crop Management. The project is coordinated by South Dakota State University.<br /> <br /> EXTENSION<br /> Precision Agriculture Data Management Workshops<br /> A recent survey of Nebraska producers indicated the need for more educational outreach regarding the collection, management, analysis, and usage of precision agriculture data. In response to this feedback, hands-on workshops were created where attendees utilize farm management software with actual precision agriculture datasets to learn concepts and skills for addressing these needs. In 2015, ten one-day workshops were held in various counties across the state. Nearly 140 producers, consultants, retailers, and other agricultural professionals attended the workshops. In addition, 36 other attendees have taken part in this extension activity in North Dakota and Kentucky in 2015. Based on post-workshop surveys received to date, 60% to 70% of attendees thought they would start utilizing practices learned or expand their current practices based on the workshops. A majority of survey respondents have indicated moderate to significant improvements in knowledge regarding agricultural data management. The program has been innovative in the methods of presenting complex information into useful practices that can be readily applied by producers in their own operations. While many field operations are overwhelmed with too much information, this program is unique in providing hands-on tools for using data to make better decisions and in this way, the program serves as a model for educational programming in the applied data science area. Impact will be large as it is leading toward an increase in producer adoption of precision agriculture based technologies and for them to apply this information directly for management decisions (rather than merely collecting data and images for archival purposes).<br /> <br /> Project SENSE<br /> In response to continuing expansion of areas in Nebraska with elevated groundwater nitrate levels, and observations that long-term trends for increasing fertilizer nitrogen use efficiency (NUE) may be plateauing, an educational/on-farm research project was initiated in 2015 to encourage crop producers to increase their use of in-season nitrogen fertilization. Project SENSE (Sensors for Efficient Nitrogen Use and Stewardship of the Environment) is a joint effort of the University of Nebraska-Lincoln, the Nebraska Corn Board, five Natural Resources Districts (NRDs), USDA-NIFA, and producers participating in the Nebraska On-Farm Research Network. In 2015 a total of 17 sites were used on cooperating producers fields in which active crop canopy sensor-based in-season N fertilization occurred. Field days were held in each NRD in July and August. Yield data will be collected this fall and information shared during grower meetings this winter on the efficacy of sensor-based fertilization to increase NUE and profitability. A research component of the project with graduate student support will begin in 2016. The project will continue in 2016 and 2017.<br /> <br /> Nebraska Agricultural Technologies Association (NEATA)<br /> This organization is composed of crop producers, their advisors, input providers, and researchers related to site-specific crop management and other emerging agricultural technologies. The association holds an annual conference, in recent years with a pre-conference symposium on variable rate technologies. The last conference and symposium was held in Grand Island, NE, February 4-5, 2015. More information is available on the association website: http://neata.org/<br /> <br /> RESEARCH<br /> Improving Irrigation Water and Energy Use Efficiency through Accurate Spatial and Temporal Management<br /> The adoption of variable-rate irrigation (VRI) technology has been slow across the state of Nebraska, primarily due to lack of information regarding cost-to-benefit ratios and knowledge for successful management of these systems throughout the growing season. This project sought to begin quantifying the potential benefits from adopting VRI with respect to utilizing stored root zone water holding capacity (RZWHC). This project has resulted in three significant accomplishments related to irrigation management using advanced technologies. Geospatial data layers including EC, OM, historical yield, etc. were combined with terrain analysis data (from field elevation) and used to relate these readily available data to RZWHC measurements from field monitoring sites. These data were then extrapolated to other field areas to determine spatial variability at the field scale. <br /> Georeferenced VRI pivot control scenarios were created in GIS software to quantify how commercially available sector or zone control systems could be used to address field AWHC variability and potentially improve irrigation water use efficiency. Using these data layers, dynamic irrigation control maps could potentially be generated based on any data (weather, soil moisture, crop growth) being collected. <br /> This project’s use of public data to estimate VRI irrigation savings for the majority of center pivots in Nebraska sets it apart from other VRI research was also seen as a potential breakthrough given that potential benefits of VRI have not been quantified beyond a small number of intensely studied fields and potential variability within the state of Nebraska can be assessed.<br /> <br /> Use of Sensing Systems to Detect Crop Stress<br /> Several projects related to nitrogen management use a variety of sensors to either evaluate treatment effects – such as fertilizer N rate or use of urease or nitrification inhibitors – on canopy N status of irrigated corn. The Holland Scientific RapidScan CS-45 is the most commonly used ground-based sensor for such projects, along with the SPAD 502 chlorophyll meter. Aerial sensing systems are used on unmanned aerial vehicles (UAVs) for evaluation in a number of projects as well, most commonly using a Tetracam MCA-6 with an integrated incoming radiometer for spectral correction.<br /> <br /> Several other projects have made use of various aerial sensors, either multispectral or RGB, to assess crop status. These include; use of RGB sensing on a bi-weekly basis in the spring and fall to evaluate buffalograss cultivar variation in green color duration; multispectral phenotyping of maize breeding lines for drought tolerance. <br /> <br /> Macro-scale Spatial and Temporal Distribution of Nutrient Pulses in Relation to Grazing Strategies<br /> This interdisciplinary project involves entomology, range science, and soil science perspectives in evaluating carbon and nitrogen cycling in grazed rangeland systems. Multispectral UAV imagery is being used to assess distribution patterns of dung pats as influenced by grazing management system.<br /> <br /> ----- Ohio -----<br /> <br /> EDUCATION<br /> ASM 4580 – INTRODUCTION TO PRECISION AGRICULTURE was taught the fall of 2014. This senior-level, 3-credit hour course provides overview on precision ag technologies and practices allow students to 1) Identify the major terminology associated with each topic and be able to use those terms correctly when discussing material from the course, 2) demonstrate a familiarity with equipment and software used in the course, and 3) demonstrate knowledge of basic procedures discussed in lecture AND be able to apply that knowledge through hands-on laboratory skill activities. The Fall 2014 enrollment was 45 students. <br /> <br /> EXTENSION <br /> Precision Ag Online Certification Course – a series of 8 educational modules were developed and recorded covering various precision ag technologies. Professional students watch each module then complete an online test. The course is management through eXtension and support by the Alabama Cooperative Extension System.<br /> <br /> An Ohio State Precision Ag web presence was created to provide timely information and other sources of precision ag material. The team is developing a website (http://fabe.osu.edu/precisionag) along with a social media presence to disseminate general precision ag information and research findings.<br /> <br /> The Ohio State Precision Ag made over eighty five presentations focused on “Big Data”, precision ag services and new technologies. The team continues to speak and educate data topics. Events that team members help facilitate and speak at on data included, the Big Data Workshop: Managing Your Most Exclusive Farm Assets, Ames, IA (25AUG2014) and Big Data: Understanding and Leveraging The Most Elusive Farm Asset, Columbus, Ohio (16FEB2015). We also hosted the Top Farmers of Ohio group for a day-long overview of Precision Ag research at Ohio State (5AUG2014).<br /> <br /> RESEARCH <br /> Downforce By Seeding Depth: Row-unit Downforce (or Margin commonly termed) can significantly influence final seeding depth. Soil texture significantly affected final corn seeding depth with deepest occurring in Sandy Loam versus Silty Clay Loam and Silt Loam. Emergence timing and final live corn plant populations were influenced by final seeding depth and downforce. In general and across all sites, heavy downforce reduced the final live population and delayed emergence.<br /> <br /> Precision Seeding Meters Evaluation: Significant differences existed between the John Standard meter setup and the JD ProMax40 and Precision Planting eSet. The John Deere Standard corn seed plates and meter can be inconsistent at times under field conditions and can be sensitive to changes in seed size. No significant differences existed between John Deere ProMax 40 and Precision Planting eSet meter setups for corn. Testing indicated for best field results to run the vacuum gauge on the high side for the recommendations provided by the manufacturer:<br /> <br /> Row-Crop Planter Requirements to Support Variable-Rate Seeding of Maize: Results from this investigation indicated that final seeding depth of maize was impacted by both the planter depth setting and downforce applied on the gauge wheels. Final seeding depth did not equal the target depth for both Fields 1 and 2. Maize emergence was affected by both target planting depth and downforce in Fields 1 and 2. More variability in planting depth was measured at the 2.5-cm treatment compared to the 5.1-cm depth treatment. Final yield for both fields was most influenced by soil type which was expected since these fields had different yield potential for maize. In Field 3 where VRS was implemented, the time to make a rate change (e.g. response time) was less than 1.0 sec regardless of the magnitude in the rate change and ground speed. No trends existed for the time to make a rate change as ground speed and seeding rate varied indicating quick and consistent performance of the VRT used on this planter. However, a delay or lag was observed when a rate change occurred when crossing a management zone but varied depending up travel direction. The delay was 7.7 m when traveling East-to-West versus 3.8 m for West-to-East. Therefore, the correct planter and display setups must be used including defining the GPS location relative to the seed meter and entering the right look-ahead time within the display. Improper setup can impact final maize population and rate changes can initiate before or after the preferred MZ boundary. Significant differences were found between the two different metering technologies evaluated. The eSet meter setup provided a more consistent and better quality of seed metering in terms of singulation and seed spacing. Overall, the quality of seed metering degraded regardless of meter type at higher meter speeds (> 38 rpm) with this aspect not clearly indicated at times in the as-planted maps. The as-planted maps from the two commercial systems provided general representation of the planter population across the field but did not reflect the correct location of rate changes or did they take into consideration the actual planter performance when comparing to the final, emerged seed spacing. This study recommended that operators need to ensure the correct planter and display setups in order to achieve needed seed placement performance to support variable-rate seeding. In conclusion, implementing VRS in maize needs to consider the setup of the VR planter and technology to maintain desired seeding depth and final emergence while as-planted data must be improved and possibly include other parameters such as downforce and seeding depth.<br /> <br /> <br /> ----- Washington -----<br /> <br /> Team at Washington State University (Manoj Karkee, Lav Khot and Qin Zhang) conducted several research projects and achieved substantial accomplishments in developing systems and technologies for precision and automated agriculture. Major projects and corresponding accomplishments are listed below.<br /> <br /> Unmanned Aerial Systems (UASs) for Mitigating Bird Damage in Blueberry Crops<br /> Every year, significant fruit yield loss is attributed to bird damage in WA and other parts of the country. The issue is particularly prevalent to Washington and Oregon vineyards but is also a critical issue for cherries and other fruits including blueberries and raspberries. Washington State grape, blueberry, cherry, and Honeycrisp apple farmers lose $80 million annually to bird damage. Netting, auditory scare devices, visual scare devices, chemical applications, and active methods such as trapping, falconry, and lethal shooting are the most common ways that bird control is practiced. However, netting is the only method viewed by most farmers as effective, which also is costly and lethal to a host of wildlife. In this work, we plan to investigate the efficacy of using fixed wing, quadrotor and/or other Unmanned Aerial Systems (UASs) to deter birds from vineyards. After showing that human-guided UASs can effectively deter birds, our longer term goal is to apply machine learning techniques to autonomously deter birds out of an area. <br /> <br /> In-field Sensing and Decision Support System to Prevent Cherry Fruit Cracking due to Rainwater<br /> Fruit cracking due to early summer rain remains the key concern for fresh market sweet cherry growers worldwide. Existing mechanical rainwater removal techniques (e.g. orchard sprayers or fans, aerial helicopters) are used by growers but there has been little systematic research on when and how much water needs to be removed from cherry canopies and the effectiveness of water removal. Through this research efforts, we have developed an in-field sensing to monitor real-time rainwater level of orchard canopies to assist grower decision making. Sensing system constitutes array of wetness sensors placed in canopies transmitting logged data in real-time to base station over wireless network. In year 2015, we plan to extensively test the robustness of the sensing system and develop decision rules through field studies. Field studies towards evaluating efficiency of orchard sprayer airblast, manned and unmanned helicopter downwash in rainwater removal will be conducted.<br /> <br /> 3D Machine Vision for Improved Apple Crop Load Estimation <br /> Accurate estimation of apple crop-load is essential for efficient orchard management. We continued field evaluation of an over the row platform to capture images from two side of apple canopies, that helped minimize the occlusions and improve the accuracy of crop-load estimation. A color camera, and a 3D camera were mounted in the sensor platform and were moved along rows of apple trees in two different commercial orchards of Allan Bros. Inc., Prosser, WA. Images were capture in both day and night times. A apple identification algorithm and a 3D mapping algorithm was used to count and estimate size of apples while avoiding duplicate counting of apples that were visible from both sides of tree canopies. Apple counting accuracy improved approximately by 20% when imaged from two sides compared to that with single-side imaging. Apple sizing accuracy was 89%.<br /> <br /> Human machine collaboration for automated harvesting of tree fruit <br /> The long-term goal of this work is to reduce dependency on human labor through mechanization and human-machine collaboration while increasing yields of premium quality fruit. The overall objective is to develop a framework for knowledge transfer and collaboration between human and machine. The team focused on understanding the dynamics of the hand picking of fruit, development of an effective end-effector based on the knowledge of hand picking, and a framework of hardware and software for optimal collaboration between human and machine for fruit identification. The machine vision system developed in this project achieved a fruit detection accuracy of 98%. The preliminary results of the first prototype of the integrated robotic harvester achieved a cycle time of ~7 sec per fruit.<br /> <br /> Shake and Catch Apple Harvesting<br /> Formally training fruiting wall architecture of modern apple orchards provides an opportunity to shake tree branches locally and capture fruit very close to where they are, potentially leading to a shake and catch harvesting system with acceptable fruit damage rate. In this project, a trans-disciplinary team of researcher is working on developing various types of shaking and catching mechanisms and understanding basics of energy transmission and fruit removal efficiency around tree canopies. <br /> <br /> Development and Optimization of Solid-Set Canopy Delivery Systems for Resource-Efficient, Ecologically Sustainable Apple and Cherry Production<br /> This was a multidisciplinary research and extension project to develop, evaluate, and deliver resource-efficient, innovative management technologies and tactics for apple and cherry production systems. The aim was to establish innovative delivery technologies for canopy and orchard floor inputs (including high efficiency irrigation systems, precision-activated micro-emitters, and reduced risk pesticides). The study showed that a solid set delivery system with ~1” diameter hose could be installed to ~400 ft rows with acceptable pressure loss. In a tall spindle apple orchard in Prosser, WA, the system achieved a similar or higher level of chemical coverage on upper-side of leaves but lower coverage on under-side of leaves compared to the coverage achieved by a conventional airblast sprayer. <br /> <br /> Mechanizing Red Raspberry Pruning and Tying System<br /> Cane management in red raspberry production is highly labor intensive. Labor availability is uncertain at best and labor cost is increasing. Currently, Washington growers estimate the pruning and tying cost in red-raspberry production to be from $500 to $800 per acre. In addition, labor is at risk for chronic and acute injury. Mechanization has the potential to substantially reduce labor use from cane management. In this project, we designed a few types of cane bundling and tying end effectors and developed a prototype. Field evaluation will be carried out in a red raspberry plot established in Prosser, WA though this project. <br /> <br /> Precision Canopy and Water Management of Specialty Crops through Sensor-Based Decision Making<br /> This project is a subcontract to a SCRI project with UC Davis as the leading institution. WSU team is contributing to nine different objectives and is playing critical roles in a few objectives. We have been refining the sensor system and perform the canopy PAR/shape assessment in tree fruit orchards; and we have been developing a research-grade sensing and mapping system to gather the data for each plant using multiple sensors to predict plant water status. WSU investigators have been leading the development of a visualized decision support system to meet the decision support needs of growers, university researchers; and have been involved in the development and implementation of site-specific application of water and fertilizer using autonomous units. Collaborating with external partners, we have also been conducting studies on assessing social impacts of developed innovative technologies through collecting, analyzing and summarizing collected data.

Publications

Chung, S., Sudduth, K.A., Drummond, S.T., Kitchen, N.R. 2014. Spatial variability of soil properties using nested variograms at multiple scales. Journal of Biosystems Engineering. 39(4):377-388.<br /> <br /> Sudduth, K.A., Kim, H.J., Motavalli, P.P. 2014. Soil. In: Moretto, L., Kalcher, K. editors. Environmental Analysis by Electrochemical Sensors and Biosensors, Vol. 1: Fundamentals. New York, NY: Springer. p. 23-61.<br /> <br /> Yang, W., Wikle, C.K., Holan, S.H., Myers, D.B., Sudduth, K.A. 2015. Bayesian analysis of spatially-dependent functional responses with spatially-dependent multi-dimensional functional predictors. Statistica Sinica. 25:205-223.<br /> <br /> Veum, K.S., Sudduth, K.A., Kremer, R.J., Kitchen, N.R. 2015. Estimating a soil quality index with VNIR reflectance spectroscopy. Soil Science Society of America Journal. 79:637-649.<br /> <br /> Wikle, C.K., Holan, S.H., Sudduth, K.A., Myers, D. 2014. Soil property estimation and design for agroecosystem management using hierarchical geospatial functional data models. Journal of the Indian Society of Agricultural Statistics. 68(2):203-216. <br /> <br /> Vories, E.D., Jones, A., Sudduth, K.A., Drummond, S.T., Benson, R. 2014. Sensing nitrogen requirements for irrigated and rainfed cotton. Applied Engineering in Agriculture. 30(5):707-716.<br /> <br /> Vories, E.D., Stevens, G., Sudduth, K.A., Drummond, S.T., Benson, R. 2015. Impact of soil variability on irrigated and rainfed cotton. Journal of Cotton Science. 19(1):1-14.<br /> <br /> Khedher Agha, M. K., W. S. Lee, R. A. Bucklin, A. A. Teixeira, and A. R. Blount. 2014. Sorption isotherms for triticale seed. Trans. ASABE 57(3): 901-904. <br /> <br /> Kurtulmus, F., W. S. Lee, and A. Vardar. 2014. Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network. Precision Agriculture 15: 57-79. http://dx.doi.org/10.1007/s11119-013-9323-8.<br /> <br /> Lee, W. S., and R. Ehsani. 2014. Sensing systems for precision agriculture in Florida. Computers and Electronics in Agriculture. doi:10.1016/j.compag.2014.11.005. <br /> <br /> Li, H., W. S. Lee, K. Wang, R. Ehsani, and C. Yang. 2014. ‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture 15: 162-183. http://dx.doi.org/10.1007/s11119-013-9325-6. <br /> <br /> Li, H., W. S. Lee, and K. Wang. 2014. Identifying blueberry fruit of different growth stages using natural outdoor color images. Computers and Electronics in Agriculture 106: 91-101.<br /> <br /> Ma, H., H. Ji, and W. S. Lee. 2014. Detection of citrus greening based on VIS-NIR spectroscopy and spectral feature analysis. Spectroscopy and spectral analysis 34(10): 2713-2718.<br /> <br /> Pourreza, A. W. S. Lee, E. Raveh, R. Ehsani, and E. Etxeberria. 2014. Citrus Huanglongbing detection using narrow band imaging and polarized illumination. Trans. ASABE 57(1): 259-272.<br /> <br /> Sengupta, S., and W. S. Lee. 2014. Identification and determination of the number of immature green citrus fruit under different ambient light conditions. Biosystems Engineering 117: 51-61. http://dx.doi.org/10.1016/j.biosystemseng.2013.07.007. <br /> <br /> Yang, C., W. S. Lee, and P. Gader. 2014. Hyperspectral band selection for detecting different blueberry fruit maturity stages. Computers and Electronics in Agriculture 109: 23-31.<br /> <br /> Pourreza, A., W. S. Lee, R. Ehsani, J. K. Schueller, and E. Raveh. 2015. An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor. Computers and Electronics in Agriculture 110: 221-232. <br /> <br /> Pourreza, A., W. S. Lee, E. Etxeberria, and A. Banerjee. 2015. An evaluation of a vision based sensor performance in Huanglongbing disease identification. Biosystems Engineering 130: 13-22.<br /> <br /> Kweon, G., E. D. Lund, C. Maxton, W. S. Lee, and D. B. Mengel. 2015. Comparison of soil phosphorus measurements. Trans ASABE 58(2): 405-414 <br /> <br /> Li, X., W. S. Lee, M. Li, R. Ehsani, A. R. Mishra, C. Yang, and R. L. Mangan. 2015. Feasibility study on Huanglongbing (citrus greening) detection based on WorldView-2 satellite imagery. Biosystems Engineering 132: 28-38. <br /> <br /> Choi, D., W. S. Lee, C. Yang, R. Ehsani, and F. Roka. 2014. Post-harvest quality evaluation system on conveyor belt for mechanically harvested citrus. 12th International Conference on Precision Agriculture. July 20-23, 2014, Hyatt Regency, Sacramento, California. <br /> <br /> Pourreza, A., and W. S. Lee. 2014. Effect of starch accumulation in Huanglongbing symptomatic leaves on reflecting polarized light. 12th International Conference on Precision Agriculture. July 20-23, 2014, Hyatt Regency, Sacramento, California.<br /> <br /> Chen, Y., and W. S. Lee. 2014. Identification and Determination of the Number of Overlapping Citrus Fruit in Natural Outdoor Conditions. ASABE Paper No. 1903766. St. Joseph, Mich.: ASABE.<br /> <br /> Choi, D., W. S. Lee, R. Ehsani, and F. Roka. 2014. Estimation of Prematurely Dropped Citrus Count on the Ground before Harvesting. ASABE Paper No. 1892107. St. Joseph, Mich.: ASABE.<br /> <br /> Ni, Z., T. F. Burks, and W. S. Lee. 2014. 3D Reconstruction of Plant/Tree Canopy from Multiple Views Using Machine Vision. ASABE Paper No. 1893190. St. Joseph, Mich.: ASABE.<br /> <br /> Pourreza, A., W. S. Lee, and R. Ehsani. 2014. A vision based sensor for Huanglongbing disease detection under a simulated field condition. ASABE Paper No. 1900251. St. Joseph, Mich.: ASABE.<br /> <br /> Pourreza, A., and W. S. Lee, and E. Etxeberria. 2014. Rapid in-field diagnosis of Huanglongbing disease using computer vision. Paper presented at the 127th Florida State Horticultural Society Annual Meeting, Clearwater, Florida, USA, June 1-3, 2014. <br /> <br /> Vyasaraja, S. and W. S. Lee. 2014. Laser weeding system for elimination of in-row weeds: new hardware system. The 5th International Workshop: Applications of Computer Image Analysis and Spectroscopy in Agriculture to be held on July 12-13, 2014 in Montreal, Quebec, Canada. <br /> <br /> Zhao, C., W. S. Lee, and D. He. 2014. Immature green citrus detection by multiple features using machine vision. ASABE Paper No. 1911329. St. Joseph, Mich.: ASABE.<br /> <br /> Marx, S.E.1, J.D. Luck, R.M. Hoy, S.K. Pitla, M.J. Darr, and E. Blankenship. Validation of machine CAN Bus J1939 fuel rate accuracy using Nebraska Tractor Test Laboratory fuel rate data. Computers Electronics Agric. (accepted, in press).<br /> <br /> Luck, J.D., J.P. Fulton, and J. Rees. Hands-on precision agriculture data management workshops for producers and industry professionals: Development and Assessment. J. Extension. 53(4). DOI: http://www.joe.org/joe/2015august/tt10.php<br /> <br /> Sama, M.P., J.D. Luck, and T.S. Stombaugh. 2015. Scalable control architecture for variable-rate turn compensation. App. Eng. Agric. 31(3): 425-435.<br /> <br /> Pitla, S.K., L. Nin, J.D. Luck, and S.A. Shearer. 2015. Use of controller area network (CAN) data to determine field efficiencies of agricultural machinery. App. Eng. Agric. 30(6): 829-839.<br /> <br /> Ferguson, Richard B. 2015. Groundwater quality and nitrogen use efficiency in Nebraska’s Central Platte River Valley. Journal of Environmental Quality doi:10.2134/jeq2014.02.0085.<br /> <br /> Krienke, B., R. Ferguson, B. Maharjan. 2015. Using an unmanned aerial vehicle to evaluate nitrogen availability and distance effect with an active crop canopy sensor. In J. Stafford (ed.): Precision Agriculture ’15, Proceedings of the 10th European Conference on Precision Agriculture, Tel Aviv, Israel, 12-16 July 2015.<br /> <br /> Pan, L., Adamchuk, V.I., Ferguson, R.B., Dutilleul, P.R.L. and Prasher, S.O. 2014. Analysis of Water Stress Prediction Quality as Influenced by the Number and Placement of Temporal Soil-Water Monitoring Sites. Journal of Water Resource and Protection, 6:961-971.<br /> <br /> Luck, J.D. and J.P. Fulton. 2014. Best Management Practices for Collecting Accurate Yield Data and Avoiding Errors during Harvest. Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln, EC-2004.<br /> <br /> Luck, J.D., J.P. Fulton, and N.M. Mueller. 2015. Improving Yield Map Quality by Reducing Errors through Yield Data File Post-Processing. Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln, EC-2005.<br /> <br /> Luck, J.D. 2014. Understanding the Benefits and Limitations of Current Pesticide Application Field Equipment. In Proceedings of the 2014 University of Nebraska-Lincoln Crop Production Clinics.<br /> <br /> Amatya, S., M. Karkee, A. K. Alva, P. A. Larbi, and B. Adhikari. 2014. Hyperspectral Imaging for Detecting Water Stress in Potatoes. GSTF Journal on Agricultural Engineering, 1(1): 52-61.<br /> <br /> Ampatzidis, Y.G., S.G. Vougioukas, M.D. Whiting, and Q. Zhang. 2014. Applying the machine repair model to improve efficiency of harvesting fruit. Biosystems Engineering, 120: 25-33.<br /> <br /> Karkee, M., B. Adhikari, S. Amatya, and Q. Zhang. 2014. Identification of Pruning Branches in Tall Spindle Apple Trees for Automated Pruning. Computers and Electronics in Agriculture, 103:127-135.<br /> <br /> Larbi, P. A., and M. Karkee. 2014. Effects of Orchard Characteristics and Operator Performance on Harvesting Rate of a Mechanical Sweet Cherry Harvester. GSTF Journal on Agricultural Engineering, 1(1): 1-11.<br /> <br /> Sharda, A., M. Karkee, Q. Zhang, J. Brunner, I. Ewlanow, and U. Adameit. 2014. Effect of emitter type and mounting configuration on spray coverage for Solid Set Canopy Delivery Systems. Computers and Electronics in Agriculture, 112: 184-192. <br /> <br /> Silwal, A., A. Gongal, and M. Karkee. 2014. Apple identification in field environment with over the row machine vision system. Agricultural Engineering International: CIGR Journal, 16(4): 66-75.<br /> <br /> Zhou, J., L. He, Q. Zhang, and M. Karkee. 2014. Effect of Excitation Position of a Handheld Shaker on Fruit Removal Efficiency and Damage in Mechanical Harvesting of Sweet Cherry. Biosystems Engineering, 125:36-44.<br /> <br /> Gongal, A., S. Amatya, M. Karkee*, Q. Zhang, and K. Lewis. 2014. Identification of Repetitive Apples for Improved Crop-Load Estimation with Dual-Side Imaging. Proceedings of The 19th World Congress of the International Federation of Automatic Control; 24-29 August 2014; Cape Town, South Africa. <br /> <br /> Ma, S., P. Scharf, M. Karkee, Q. Zhang, J. Tong, and L. Yu. 2014. Effects of Off-Track Errors of a Sugarcane Harvester On Stubble Height and Weight. Proceedings of the 6th Automation Technology for Off-road Equipment Conference (ATOE),15-19 September 2014, Beijing, China.<br /> <br /> Silwal, A., A. Gongal, and M. Karkee. 2014. Apple Identification In Field Environment With Over The Row Machine Vision System. Proceedings of the 6th Automation Technology for Off-road Equipment Conference (ATOE), 15-19 September 2014, Beijing, China.<br /> <br /> Sharda, A., D. Mangus, M. Karkee, and Q. Zhang. 2014. Effect Of Time Of Application On Spray Coverage Using Solid Set Canopy Delivery System. Proceedings of 12th International Conference on Precision Agriculture; July 20-23, Sacramento, CA. <br /> <br /> De Kleine, M.E., M. Karkee, K. Lewis, and Q. Zhang. 2014. A Dual Motor Actuator used to Detach Fruit by Shaking Limbs of Fruit Trees. Proceedings of 12th International Conference on Precision Agriculture; July 20-23, Sacramento, CA. <br /> <br /> Gongal, A., S. Amatya, and M. Karkee. 2014. Identification of Repetitive Apples for Improved Crop-Load Estimation with Dual-Side Imaging. ASABE Paper No.141888882. St. Joseph, Mich.: ASABE.<br /> <br /> Tong, J., Q. Zhang, M. Karkee, H. Jiang, and J. Zhou. 2014. Understanding the Dynamics of Hand Picking Patterns of Fresh Market Apples. ASABE Paper No.141898024. St. Joseph, Mich.: ASABE.<br /> <br /> De Kleine, M. E., M. Karkee, K. Lewis, and Q. Zhang. 2014. An End Effector Concept for Removing Fresh-Market Apples from a Tree Limb. ASABE Paper No.141906284. St. Joseph, Mich.: ASABE.<br /> <br /> Larbi, P. A., M. Karkee, S. Amatya, Q. Zhang, and M. D. Whiting. 2014. Field Evaluation of a Modified Mechanical Sweet Cherry Harvester. ASABE Paper No.141896871. St. Joseph, Mich.: ASABE.<br /> <br /> Sharda, A., J.P. Fulton, and T.P. McDonald. 2015. Impact of control system response characteristics on nozzle flow stabilization during simulated field scenarios. Computers and Electronics in Agriculture. 112(2015): 139-148. (doi: http://dx.doi.org/10.1016/j.compag.2014.11.001).<br /> <br /> Torino, M.S., B.V. Ortiz, J.P. Fulton, K.S. Balkcom, and C.W. Wood. 2014. Evaluation of vegetation indices for early assessment of corn status and yield potential in Southeastern U.S. Agronomy Journal. 106(4): 1389-1401.<br /> <br /> Virk, S.S., A. Poncet, J.P. Fulton, K.S. Balkcom, B. Ortiz, T.P. McDonald, and G.L. Pate. 2014. Row-Crop Planter Requirements to Support Variable-rate Seeding of Maize. In Proceedings of the 12th International Conference on Precision Agriculture, Sacramento, CA, July 20-23.<br /> <br /> Virk, S.S., J.P. Fulton, T.P. McDonald, K.S. Balkcom, A. Poncet, and A.B. Brooke. 2014. Impact of Ground Speed and Varying Seeding Rates on Meter Performance. ASABE Paper No. 141897985. St. Joseph, Mich.: ASABE.<br /> <br /> Virk, S.S., J.P. Fulton, T.P. McDonald, K.S. Balkcom, A. Poncet, and A.B. Brooke. 2014. Current Performance of Planter Technology to Support Variable-rate Seeding in the Southern US. ASABE Paper No. 141898019. St. Joseph, Mich.: ASABE.

Impact Statements

1. KANSAS: The goals of the PrecisionAg group at K-State are to identify the potential uses of the sUAVS for agronomic crops with emphasis on serving large farming systems and also research programs. The diverse applications currently investigating are: 1) thermal image in crops, livestock, forages and rangeland (lead by Dr. Sharda, ABE); 2) investigation on plant height, biomass, canopy cover, and stand counts in corn and soybean with the ultimate goal of developing algorithms and support tools for disseminating these applications within the Ag community (lead by Dr. Ciampitti, Agronomy); 3) calculation of economic cost on using sUAVS and applications (lead by Dr. Burton, AgEcon); 4) analysis of big data and potential applications (lead by Dr. Griffin, AgEcon). In addition to the latter, collaborations with the Aviation program (sUAVS lab) at K-State Salina were developed during the last year with the goal of developing platforms and improving data collection.
2. KANSAS: The extension effort impacted approximately 1,000 agribusiness stakeholders in the last year and continuous to promote information on the use of this technology for agricultural purposes.
3. FLORIDA: The proposed HLB detection system can help citrus growers to efficiently manage the infected trees and protect the rest of their groves. It can detect infected trees with an accuracy of over 95%. The detection system for citrus fruit dropped on the ground could provide an objective and accurate estimation of crop loss due to the HLB disease before harvesting. The developed image processing algorithms for detecting green immature and mature citrus fruit could well be used for citrus yield mapping, which in turn could be used for identifying various factors causing yield variability to increase yield and profit.
4. MISSOURI: Sensors estimate soil quality. To evaluate the ability of in-field soil sensors to estimate soil quality, ARS scientists at Columbia MO paired reflectance spectroscopy sensing with traditional laboratory soil testing. This study benefits scientists and producers by demonstrating the potential for rapid and inexpensive soil quality assessment in the field. This will save time and money for scientists and producers, and provide valuable information to drive management decisions and increase profitability.
5. MISSOURI: New method for analysis of large spatial datasets. Scientists in many fields are often confronted with analyzing and interpreting datasets containing a large number of related variables. With colleagues at the University of Missouri, ARS scientists at Columbia MO developed a new approach called multi-dimensional spatial functional models. The approach has potential for improved interpretation of large datasets such as those obtained in proximal soil sensing. This could enhance the ability of scientists and practitioners to utilize these data in the fields of precision agriculture and digital soil mapping.
6. MISSOURI: Demonstrated the impact of soil spatial variability on cotton yield and irrigation water use efficiency. Soils in the U.S. Mid-South are highly variable and that variability affects water holding capacity, infiltration, and other properties as well as yields of cotton. ARS scientists developed detailed spatially referenced datasets of soil apparent electrical conductivity (ECa). An equation relating total irrigation and ECa to seed cotton yield demonstrated that yields decreased with excessive water application. This research aids producers in the proper use of VRI systems and obtaining the optimum use of irrigation water supplies.
7. NEBRASKA: Precision Ag Data Management Workshops. 60% to 70% of attendees thought they would start utilizing practices learned or expand their current practices based on the workshops. A majority of survey respondents have indicated moderate to significant improvements in knowledge regarding agricultural data management. Impact will be large as it is leading toward an increase in producer adoption of precision agriculture based technologies and for them to apply this information directly for management decisions (rather than merely collecting data and images for archival purposes).

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