Brief Summary of Minutes of Annual Meeting

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.

NCERA-180 Summary of Accomplishments 2011-2012 Research 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: Research Projects reported from Arizona: 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. 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. 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. 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. 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. 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. 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. Research Projects reported from Florida 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%. 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. 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. 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. 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. Research Projects reported from North Dakota Use of the Greenseeker and Holland Crop Circle Sensors to predict in-season N needs of field corn. 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. Incorporation of plant height data in predictive N requirements of sugarbeet, field corn and sunflower. 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 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. 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. Research Projects reported from Illinois 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. 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. Extension Extension Education for Ag Professionals: Pilot Project . University of Arizona - Extension Office. Participation: Providing training to crop consultants in the use of GPS technology. 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. 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. 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. 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.

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 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 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 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. 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. 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. 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. 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). 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 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 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 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 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 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 Yang, C. and W. S. Lee. 2011. Spectral signatures of blueberry fruits and leaves. ASABE Paper No. 1110582. St. Joseph, Mich.: ASABE. 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 Invited Seminars 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. 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. 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. Applied research and extension in precision agriculture of the United States. Mexican Association of Sugar-cane Producers. Oaxaca, Oaxaca, Mexico 12/7/2011. Presentations during extension and outreach events "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. "Using GPS for Pesticide Applications" Cotton Mid Season Meeting. Parker AZ 8/24/11. "Cotton yield monitors: Available systems, installation and data management" Cotton Late Season Meeting. Buckeye AZ 7/28/11. "Technical considerations when spraying defoliants in Cotton" Cotton Late Season Meetings. Yuma Agricultural Center. Yuma, AZ, 6/21/11. Demonstration on cotton yield monitor technology. Maricopa Agricultural Center Annual Field Day. Maricopa AZ 9/29/2011.