Brief Summary of Minutes of Annual Meeting

--------------------------------------- Roles and Responsibilities or the Group: --------------------------------------- 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. ---------------------------------- Participation and Future Meetings: ---------------------------------- 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. -------------------- Discussions on UAVs: -------------------- 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. ----------------- Other activities: ----------------- * 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)

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. -------------------------------------------------------------------------- ----------------------------------Research-------------------------------- -------- Florida: -------- 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. 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. 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. 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. -------- Arizona: -------- 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. 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. 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. 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 , -------- Alabama -------- 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 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. 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 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. ---------- Nebraska --------- 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. 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. 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. 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. 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). 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. 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. 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. 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). Water extraction from various soil depths varied with the season and N application rate (SCAL). Crop water use efficiency (CWUE) had a positive, quadratic relationship with N rate (SCAL). Irrigation water use efficiency (IWUE) had a positive, quadratic relationship with N rate, and was greatest at higher yields (SCAL). In general, a positive, quadratic relationship existed between evapotranspiration water use efficiency (ETWUE) and N rate (SCAL). ---------- Washington: ---------- 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. 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. 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 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. 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. Design and Development of Apple Harvesting Techniques: This research aimed to 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. 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 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 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%. ------------------------------------------------------------------------------ ----------------------------Extension/Outreach/Education---------------------- -------- Arizona: -------- 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. 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 Extension Education for Ag Professionals: Pilot Project. University of Arizona - Extension Office. Participation: Providing training to crop consultants in the use of GPS technology. Impact: Impacts from this program are still to be generated and will be timely reported. 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. 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. -------- Nebraska -------- 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/ 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. 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. http://www.agronomy.unl.edu/newprospective/distanceed/agro896-1.html ---------- Washington ---------- *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 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. 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 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 Center packing-line and demonstration citrus block. During this tour, we organized four round table meetings to discuss various aspects of the tools, 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 proposed solutions, barriers and next steps. 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. 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 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 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. 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 road map. Several monthly meetings have been planned for early 2013 to complete this process. *Stakeholder Education, Interaction and Collaboration: " Sep 13, 20112: Met with various growers and WTFRC representatives to discuss apple harvesting technology roadmap development " August 27, 2012: Demonstrated the solid set canopy delivery system to a group of growers and other stakeholders " August 3, 2012: Co-organized and participated in the technology demonstration to Washington Governor Gregoire and her team " July 23-24, 2012: Organized a planning meeting in Aurora, OR to discuss small fruit industry need in canopy management automation area " July 17, 2012: Presented a research project in USDA field day, Paterson, WA " June 4, 2012: Presented and demonstrated two research projects in cherry field day, Prosser, WA " April 24, 2012: Visited Auvil Fruit Company to discuss potential for mechanical and automated apple harvesting, Vantage, WA " Feb 21-22, 2012: Organized a planning meeting in Mt. Vernon, WA to discuss small fruit industry need in canopy management automation area " 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. 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 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 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.

--------------------------------------- Journal/Conference Articles and Posters --------------------------------------- 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 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 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. 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 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 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 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. 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. 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. 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. 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. 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. 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. 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. 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. Schueller, J.K. 2012. Another report from 25 years hence. Resource. July-August. 19(4):16-17. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Karkee, M. and Q. Zhang. 2012. Mechanization and Automation Technologies in Specialty Crop Production. Invited Article, ASABE Resource Magazine, Sep/Oct 2012: 16-17. Amatya, S.*, M. Karkee, 2012. Nitrogen stress detection for potato using hyperspectral imaging. 2013 ASABE International Meeting, Abstract No. 1589210. Ma, Shaochun*, M. Karkee, and Q. Zhang. 2012. Sugarcane Harvesting System-A Critical Review. 2013 ASABE International Meeting, Abstract No. 1574361. 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. 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. Gongal. A*, S. Amatya, and M. Karkee, 2012. Over-the-row Machine Vision for Improved Apple Crop Load Estimation. 2013 ASABE International Meeting. 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. 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. 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. De Kleine, M.E., M. Karkee, K. Lewis, Q. Zhang. 2012. Apple Harvesting Techniques. 108th WSHA Annual Meeting. Dec 3-5, 2012, Yakima, WA. 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. 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. 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. Adhikari*, B., M. Karkee. 2012. 3D Reconstruction of Apple Trees for Mechanical Pruning. WSU Academic Showcase, March 30, 2012, Pullman, WA. 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. 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. 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. 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). 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). 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). ---------------- Invited Seminars ---------------- 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. 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 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. 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. 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. 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). 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). 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). 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. 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. 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. 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. 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. Delegation of Chinese Officers from water conservancy - USDA-MOST flagship project on water saving irrigation. Presentation on MAC Extension program on precision agriculture. -------------------------- Workshops and other events -------------------------- 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. 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 University of Arizona, EPA - Worker protection standard training. Chemical (pesticide) handler safety training. Maricopa, AZ, 3/29/2012. 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. Manoj Karkee, Automation and Mechanization Research for Specialty Crops (Invited Talk) - Annual Hermiston Farm Fair and Trade Show, Hermiston, OR; Nov 29, 2012. Pruning Branch Identification for Automated Pruning of Apple Trees (Invited Talk) - Specialty crop engineering solutions workshop, Pittsburg, PA; Nov 28, 2012. Manoj Karkee, Agricultural Automation Research at WSU (Invited Seminar) University Putra Malaysia, Selangor, Malaysia, June 29th, 2012 Agricultural Automation Research at WSU (Invited Seminar)  TU, Engineering Campus, Dharan, Nepal, June 25th, 2012 Manoj Karkee, Precision Agriculture in Specialty Crops: Accomplishments, Challenges and Future Direction. First International Precision Agriculture Forum, Richland, WA, March 15-16, 2012. Media articles 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