Brief Summary of Minutes of Annual Meeting

See attachment for complete minutes.

Meeting called to order at 8:38am on 14-August-2019. Dr. Thomson delivered an update on the status of the NIFA relocation to Kansas City. Expect delays and changes as only 1/3 of the staff will be making the move. Updates on other Precision Agriculture meetings were shared. It was suggested that NCERA coordinate with S-1069 to create more collaboration opportunities.

NCERA180 will move to a winter meeting date in January or February starting in 2020 as requested by the members. The 2020 meeting will be in Raleigh hosted by Jason Ward at NC State and the 2021 meeting will be hosted at Purdue by John Evans.  Registration has been requested at approximately $125. Optimizing cost for the meetings is helpful for many attendees that may or may not have experiment station support.

There are no programs in USDA to support precision ag. National Program Leaders update RFAs every year. Collectively, Dr. Khosla suggests reaching out to program leaders to suggest that a new priority is needed. NCERA 180 discussed the real need for creating priority areas that cover precision ag, spectral signals for pests, other areas. If we were to focus on small areas. A short white paper from the group could create the language from which to draw a new or revised RFA. It was suggested to form a subcommittee for the whitepaper: Newell Kitchen, Van Kelley, Raj Khosla, Ganesh Bora.

NCERA-180 is up for renewal in 2021 and needs paperwork to start in 2020. 

The meeting was adjourned at 10:16a.

Colorado State University

Four Nitrogen management strategies that incorporates soil based and crop canopy based management of N fertilizer were evaluated in terms of grain yield and NUE

This project accomplished a number of objectives and associated scientific findings. The four N management strategies studied were: a uniform application of N fertilizer, a variable rate N management strategy that consisted of applying variable rate N across management zones referred to as “MZ”; a proximal sensor based variable rate N management strategy referred to as “RS”; and a variable rate N management strategy based on proximal sensing within each management zone referred to as “MZRS”. 

Results from our study comparing N management strategies were encouraging. It indicated that variable rate N management could be undertaken for both, maintaining or increasing grain yield as well as enhancing NUE.  Across the three years for which this analysis was performed, the MZ strategy consistently resulted in grain yields that were either the same as or greater than grain yield obtained when applying N uniformly across the field.  The RS and MZRS management strategies resulted in yields that were lower than both the uniform and MZ strategies. However, these differences were typically small in each of the three years. This indicates that with further refining, the RS and MZRS can be improved to maintain or increase grain yield relative the uniform application strategy.  However, all three variable N management strategies resulted in significantly improved NUE when compared to uniform N application strategy. Specifically, the two management strategies that incorporated proximal sensing strategy consistently resulted in the largest improvements in NUE.  

Based on the findings of this research, farmers could consider employing strategies for variable rate N management that result significant improvements in input use efficiency.  While there exists room for growth, improved implementation of these techniques require technical assistance. Farmers are in need of both support systems and personnel they can rely upon to address their concerns about implementing more nuanced approaches to N management.  Findings from this research could be coupled with extension services to enhance growers understanding and appreciation of precision N management and promote adoption.      

NC State University

Extension Activities

The modern agricultural landscape is a data-rich environment. Sensors on agricultural equipment, soils data, yield maps, official variety trials, variable-rate prescriptions, animal monitoring, and UAV imagery could all be used on the farm to help make decisions – if the data are collected, managed, and analyzed properly.  The range of digital agriculture products and services has increased in order of magnitude over the last five years. Equipment manufacturers and agricultural service providers are the experts in how to operate their products or integrate them into equipment. The real knowledge gap lies in putting the generated data into action. Key issues are emerging which can be addressed with Extension programmatic efforts: producers are not receiving unbiased information on how and why to use digital agriculture tools along with the potential implications of their decisions, Extension agents are not being seen as knowledgeable advisors in implementing digital agriculture, they need focused training to understand modern tools and how to use them, agricultural service providers need to understand both the limitations and potential of their data products so that meaningful insight is being delivered to producers.

A train-the-trainer event titled Data Science for Ag Extension Agents proposed to educate agriculture and natural resources (ANR) Extension Agents on current digital agriculture technologies and how they could be used in on-farm investigation. The event was funded by NSF and the Sloan Foundation. Data Science for Ag Extension Agents was held on January 15-16, 2019 at the Lake Wheeler Road Field Laboratory in Raleigh, NC. Approximately 40 total people were present for all or part of the content including registered attendees, speakers, and additional attendees from on-campus departments or commodity groups. Speakers were invited from NC State, Kansas State University, The Ohio State University, and industry. Focus was placed on data generated from agricultural production in both crop and animal production systems and how that data could be properly analyzed to inform producer decision making. Best practices in conducting on-farm investigation and free, open-source data analysis tools were introduced. 

A field day event targeted at research station staff and Extension agents was organized on applying next generation planter technology, guidance, and tractor systems. This workshop was organized with industry partners and is expected to continue for future years. Further training was Created for the North Carolina Annual Extension Conference, along with colleagues, to introduce precision agriculture and small UAS applications.

Trainee self-assessment indicated that degree of understanding in all target knowledge areas increased. While this training did improve understanding, most knowledge areas were below a score of three out of a four point scale. This assessment indicates that additional training or repeated exposure to the content will be needed on increase advanced understanding. The Extension agents who attended have exceptional knowledge in their focus areas, adding functional knowledge of digital agriculture and data science to their day-to-day activities will require additional training avenues and training reinforcement.

University of Florida

An automated imaging system was developed to count the number of flowers and fruit in the strawberry field using artificial intelligence. Maps for flower distribution and estimated fruit yield were created to help growers for more efficient harvesting operations. Algorithms for detecting strawberry plant wetness were developed using color and thermal imaging. The results will be used for the Strawberry Advisory System which provides real-time fungicide recommendations to strawberry growers.      

University of Nebraska

Extension Activities:

Nebraska Digital Ag Workshops

In previous years, the UNL Precision Ag team had conducted in-person data management workshops to teach attendees methods for working with digital agriculture datasets. In 2018, we began to migrate these workshops to an online format where attendees could enroll in the self-paced course to learn at their convenience. To date, three of the courses have been migrated to the online system. We have currently had around two dozen individuals register and begin learning via the online environment. The courses are designed to collect impact/knowledge gained data after completion of each module. We hope to begin assessing this information in 2020.

Two day-long Digital Ag workshops were hosted for students at Highland Community College, Kansas with 20 total attendees (students were not surveyed for impact assessment).

Project SENSE (Sensors for Efficient Nitrogen Use and Stewardship of the Environment)

The 2019 growing season represented the fourth year of Project SENSE. During the year, we reported on significant project findings from the first three years as we conducted additional field activities. The final project grower/cooperator meeting was held in 2018. Feedback from the group indicated that while adoption of the sensing technology had not occurred, 50% of cooperators that had been involved in two or more years of the project had lowered nitrogen application rates in their fields as a result of working with the project. The primary feedback given was that they had observed much improved nitrogen use efficiency metrics via the sensor-based applications and they were trying to reduce nitrogen use in their operations. Interacting with Project SENSE provided confidence to them that reducing nitrogen rates would not negatively impact their crop yield significantly.

Education Activities:

Site-Specific Crop Management (AGEN/AGRO/MSYM 431) 3 Credit Hour Course

Student enrollment consisted of 82 agronomy, engineering, and mechanized systems management students in 2019. Students were exposed to a variety of topics related to precision agriculture; 15 computer laboratory exercises were conducted throughout the semester to provide hands-on learning experiences with agricultural data applications.

University of Wisconsin-Madison

The precision agriculture group at the University of Wisconsin-Madison has been focused on planter research, machinery movement and the impact of wheel traffic, and remote sensing. Two federally funded projects of note were awarded in 2018. An USDA-NRCS Conservation Innovation Grant was awarded to investigate planter technology pertaining to organic no-till practices with the goal of improving emergence and weed control (Dr. B. Luck). We recently concluded our first field season associated with this project and collaborations with industry partners providing down-force, depth control, and firming wheel control systems did impact planter performance and plant emergence. Secondly, a USDA-NIFA Alfalfa and Forage Research Program project was awarded to investigate the effect of wheel traffic on alfalfa yield and persistence (Dr. B.  Luck). We have completed our first field season with this project as well. Initial results indicate that compaction has an impact on alfalfa yield and this impact can be as great as 1.5 ton/ac depending on the amount of traffic applied to the plants. Soil compaction was measured and remote sensing data was collected via Unmanned Aerial Vehicle. This data is currently being analyzed. Finally, alfalfa yield prediction using UAV-based hyperspectral imagery and machine learning (Dr. Zhou Zhang)-The UAV-based hyperspectral imaging platform has been developed and become fully operational. Hyperspectral data over alfalfa fields have been successfully collected and pre-processed. Yield prediction model is under-development.

An area of high interest has been assessing crop quality during corn silage harvest. In 2018, the UW-Madison group release a smart phone application (SilageSnap) that utilizes image processing methods to determine kernel processing score during harvest. Typically, farmers send samples to a feed quality laboratory to make this assessment. By the time results return the crop harvest has been completed. To date, SilageSnap has been downloaded nearly 1,000 times and many results from its use have been shared with the research team. This effort has had an impact on feed quality for dairy farms in Wisconsin and across the United States.

A new research push for the Wisconsin Precision Agriculture Group has, and will be, industrial hemp production. There is considerable interest in this potentially high value crop. In 2019, we grew 10 acres of industrial hemp for grain production. We assessed different row spacing and combine settings for optimal grain harvest. Results from this preliminary work showed that 15 inch row spacing yield was 2300 lb/ac (± 440 lb/ac) and 30 inch row spacing yielded 1600 lb/ac (± 170 lb/ac). At harvest, threshing the material went well, but variations in plant height had an effect on cutting the crop consistently. Losses were realized at the header and through the machine. We intend to continue this research in 2020 to better understand how to manage this crop. Additionally, site-specific management trials and remote sensing data collection will be conducted in 2020 with the goal of optimizing industrial hemp grain production.

Precision Agriculture extension efforts at the University of Wisconsin-Madison have remained very popular across the state. Multiple county meetings were attended with subject matters ranging from Unmanned Aerial Vehicles and remote sensing, digital agriculture, and planting technology for optimal seeding and emergence. Two international extension opportunities were also attended. Dr. Luck was invited to Temuco, Chile to speak on forage harvest technology and the future of agriculture and Sao Paulo, Brazil for a similar program. This reporting year yielded one peer-reviewed extension publication (see publications section).

Washington State University 

Precision chemical application is one of the major focus of WSU team. An optimized solid set canopy delivery system (SSCDS) was evaluated with different emitters/microsprayers– configurations in a high-density apple orchard and modified vertical shoot position (VSP) trained grapevines. To achieve uniform spray application over longer spray lengths, team has built a reservoir sub-system that integrates into SSCDS. We also have developed and tested the prototype automation sub-system to operate SSCDS. During 2018 season trials, the airblast sprayer and the SSCDS configurations tested in modified VSP had statistically similar within-canopy spray deposition. Drift losses to air were about 900 and 390 times higher for airblast sprayer compared to the studied SSCDS configurations at 6 ft. and 12 ft. downwind, respectively.

 To reduce chemical use in tree fruit production, WSU team is also exploring alternative pest management technologies. A laboratory–scale application technology unit was developed to apply horticultural oil (HO)–based thermotherapy. Using such unit, experiments were conducted to have spray treatments including four variables namely heat–condition (i.e., heat and no–heat), HO concentration (i.e., 0.5 and 1.0 %), two nozzle types and application pressure. Overall, HO combined with thermotherapy caused a rapid kill of pear psylla (mortality: 74.4±3.1 %).

 WA team also worked on fruit harvesting and handling technology using a 12-armed robot. A fruit orientation estimation and obstacle detection and avoidance capability have also been developed for the robot. The study is showing promising results in terms of harvesting speed and efficiency for commercial adoption. Another approach, targeted shake-and-catch system, was also evaluated for apple harvesting. Field tests showed that fruit detachment and collection efficiency increased with shorter branch length on similar size branches, and could reach 90% or more in modern, formally trained orchards for a number of varieties like Pink Lady and Scifresh.  The technique achieved a fruit damage rate of about 10% for some varieties including Pink Lady, Scifresh and Pacific Rose. This approach showed promise for faster and potentially low cost harvesting of apples for fresh market consumption. However, the method tended to show varietal dependence with Pink Lady, and Jazz showing higher removal efficiency and better quality fruit while varieties like gala and honey crisp suffering from either low removal efficiency, low fruit quality or both. 

 WSU team also worked on a robotic weeding system for vegetable crops with a self levelling system. The system was able to distinguish carrots and onions from different types of weeds with more than 99% accuracy and the weeds could be sprayed with a precision of 2 mm when the robot was travelling on an uneven field terrain. Similarly, the team is working on developing an automated system for green shoot thinning in vineyards. A deep-learning-based machine vision system was developed to detect shoots and cordon of the vines during early growing season and a trajectory fitting model was proposed to represent cordon position and orientation. A trajectory fitting model with a 6th degree polynomial was found to fit about 80% of cordon trajectories with an R-square value of 0.98. WSU team also has started working on a SMART IRRIGATION project, where internet of things is being used to collect various types of data including soil, environment and canopy parameters/maps in space and time and a big data analytics technique is being developed to understand the inherent relationships and patterns in the data, which is expected to improve the assessment of plant water needs and implementation of a decision support system. 

 WSU team is also working on the development of Internet-of-Things enabled Crop Physiology Sensing System (CPSS) for tree fruit crop loss management with initial focus on apple sunburn management. In 2018-19, CPSSS nodes that encompass a thermal-RGB imager integrated with a single board computer for data acquisition and on-board real-time data analytics were developed and tested in commercial apple orchard. Such nodes are capable of apple fruit surface temperature monitoring and actuation of automated SSCDS for evaporative cooling of apple orchard block.  

WSU Activities

 A 12-armed full scale robotic harvesting system (including the desired machine vision system) was developed (in collaboration with FFRobotics) and was evaluated in commercial orchards.

 A multi-layer self-propelled shake and catch harvesting platform was developed and further evaluated in 2018 harvest season.

 A robotic prototype was evaluated for automated bin movement in orchards. The prototype with four wheel steering system used RTK GPS and laser sensing systems to navigate in orchards.

 In another project, WSU team designed and fabricated a research prototype of self-propelled robotic weeding machine for vegetable production.

 Another activity was the investigation of mechanized red raspberry cane bundling and tying. A cane tying mechanism has been designed, fabricated, and then evaluated in field operation.

 A SMART IRRIGATION project was initiated to optimize water use in wine grape using big data analytics.

 A machine vision system was developed for automating green shoot thinning in wine grapes.  

 WSU team also works on sensing and automation technologies with UASs. One specific project we worked on was to use UASs to deter birds from fruit crops such as wine grapes, blueberries and apples. 

Optimization of SSCDS configurations in a high-density apple orchard and modified vertical shoot position trained grapevines.

Development and evaluation of prototype horticultural oil thermotherapy system for pear psylla management.

Development of Internet-of-Things enabled Crop Physiology Sensing System for tree fruit crop loss management with initial focus on apple sunburn management.

Synthesis of a novel Cellulose NanoCrystals (CNC) based dispersion that can be applied as a spray agent on tree fruit buds to prevent the frost damage. 

Barnes, E.M., Hake, K., Griffin, T.W., Rains, G.C., Maja, J.M.J., Thomasson, J.A., Griffin, J.A., Pelletier, M.G., Kimura, E., Morgan, G., Devine, J., Ibendahl, G. and Ayre, B.G. 2019. Initial Possibilities for Robotic Cotton Harvest. Beltwide Cotton Conference, New Orleans, LA

Griffin, T.W., Ibendahl, G., Regmi, M., Barnes, E., Devine, J., Cullop, J., Griffin, T.G. 2019. Optimal Robotic Utilization for Cotton Production. Beltwide Cotton Conference, New Orleans, LA. January 8, 2019.

Barnes, E.M., Ryan Kurtz, Jesse Daystar, John Wanjura, Jason Ward, Bobby Hardin, Kendall Kirk, Wes Porter. (2019). Capturing More Value from Cotton Data. Cotton Cultivated. https://cottoncultivated.cottoninc.com/research_reports/capturing-value-from-cotton-data/

Bhusal, S., K. Khanal, S. Goel, M. Taylor and M. Karkee. 2019. Bird Deterrence in a Vineyard using an Unmanned Aerial System (UAS). Transactions of the ASABE; 62(2): 561-569.

Chakraborty, M., L. R. Khot, S. Sankaran and P. Jacoby. 2019. Evaluation of mobile 3D light detection and ranging based canopy mapping system for tree fruit crops. Computers and Electronics in Agriculture,158: 284-293. https://doi.org/10.1016/j.compag.2019.02.012

Chakraborty, M., L. R. Khot and R. T. Peters. 2019. Assessing suitability of modified center pivot irrigation systems in corn production using low altitude aerial imaging techniques. Information Processing in Agriculture, https://doi.org/10.1016/j.inpa.2019.06.001

Chandel, A., L. R. Khot, Y. Osroosh and R. T. Peters. 2018. Thermal-RGB imager derived in-field apple surface temperature estimates for sunburn management. Agricultural and Forest Meteorology, 253-254: 132-140. https://doi.org/10.1016/j.agrformet.2018.02.013

Chen, Y., W. S. Lee, H. Gan, N. Peres, C. Fraisse, Y. Zhang, and Y. He. 2019. Strawberry yield prediction based on a deep neural network using high-resolution aerial orthoimages. Remote Sensing, 11: 1584. Doi:10.3390/rs11131584. 

Cordero, E., Longchamps, L., Sacco, D., and Khosla, R. 2019. Spatial management strategies for nitrogen in maize production based on soil and crop data. Science of the Total Environment Journal. STOTEN.

Cullop, J., Ibendahl, G., Shockley, J., Barnes, E., Devine, J., & Griffin, T.W. 2018. Economics of Swarm Bot Profitability for Cotton Harvesting. In Proceedings of the 14th International Conference on Precision Agriculture (unpaginated, online). Monticello, IL: International Society of Precision Agriculture.

De Lara, A, R. Khosla, L. Longchamps. 2019. Soil water content and high resolution imagery: Maize yield. J. Agron. 

Drewry, J. L., B. D. Luck, R. M. Willett, E. M. C. Rocha, and J. D. Harmon, 2018. Assessing particle size distribution of harvested and processed corn silage via image processing techniques. Computers and Electronics in Agriculture160:  144-152. https://doi.org/10.1016/j.compag.2019.03.020

Drewry, J. L., J. M. Shutske, D. Trechter, B. D. Luck, L. Pitman. 2019. Assessment of digital technology adoption and access barriers among crop, dairy and livestock producers in Wisconsin. Computers and Electronics in Agriculture 165:  104960. https://doi.org/10.1016/j.compag.2019.104960

Ellixson, A., Griffin, T.W., Ferrell, S.L., and Goeringer, L.P. 2019. Legal and Economic Implications of Farm Data: Ownership and Possible Protections. Drake Journal of Agricultural Law. 24(2)

Ferrell, S.L. and Griffin, T.W. 2018. Managing Farm Risk Using Big Data: A guide to understanding the opportunities and challenges of agricultural data for your farm. Handbook sponsored by USDA NIFA RME http://agecon.okstate.edu/farmdata/

Finkenbiner, C., T.E. Franz, J. Gibson, D.M. Heeren, and J.D. Luck. 2018. Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management. Precision Agric. https://doi.org/10.1007/s11119-018-9582-5 (Contribution 10%)

Griffin, T.W., and Yeager, E.A. 2019. How quickly do farmers adopt technology? A duration analysis. Precision agriculture ’19. Ed. J.V. Stafford. 12th European Conference on Precision Agriculture. pp 843-849 https://doi.org/10.3920/978-90-8686-888-9_104

Griffin, T.W., Yeager, E.A., and Ibendahl, G. Adoption of Precision Agriculture Technology. International Farm Management Congress

Griffin, T.W., Shanoyan, A., Yeager, B.A., and Ibendahl, G. 2019. Agricultural Technology Adoption Path of Kansas Farms AIM American Society of Agricultural and Biological Engineers Boston, MA July 7-10, 2019

Griffin, T.W., Ibendahl, G., Barnes, E., and Devine, J. 2019. Optimal utilization of autonomous robotics for cotton harvest: how slow can we go? AIM American Society of Agricultural and Biological Engineers Boston, MA July 7-10, 2019

Griffin, T.W., Ibendahl, G., Yeager, E.A., and Shanoyan, A. 2019. Digital Agriculture as a Risk Management Strategy. National Farm Business Management Conference aka Triennial aka NAFBAS. Sheboygan, WI. June 9-13, 2019.

Griffin, T.W., Shockley, J., and Mark, T.B. 2018. Economics of Precision Farming. In D.K. Shannon, D.E. Clay, and N.R. Kitchens (Eds.) Precision Agriculture Basics. USDA. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.

Griffin, T.W. and Yeager, E.A. 2018. Adoption of Precision Agriculture Technology: An Analysis of Kansas Farms. agri benchmark Cash Crop Conference 2018. Beijing China. June 2018.

Harris, K.D., and Griffin, T.W. 2019. The Old “Block” and Chain: How Farm Data Will Be Used on the Blockchain. Kansas State University Department of Agricultural Economics AgManager. July 18, 2019. https://www.agmanager.info/machinery/precision-agriculture/old-%E2%80%9Cblock%E2%80%9D-and-chain-how-farm-data-will-be-used-blockchain/

He, L., X. Zhang, Y. Ye, M. Karkee, and Q. Zhang. 2019. Effect of Shaking Location and Duration on Mechanical Harvesting of Fresh Market Apples. Applied Engineering in Agriculture; 35(2): 175-183.

Hohimer, C. J., H. Wang, S. Bhusal, J. Miller, C. Mo, M. Karkee. 2019. Design and field evaluation of a robot apple harvesting system with 3D printed soft-robotic end-effector. Transactions of the ASABE; 62(2): 405-414.

Karkee, M., J. Gordón, B. Sallto and M. Whiting, Optimizing fruit production efficiencies via mechanization. 2019. In Achieving sustainable cultivation of temperate zone tree fruits and berries, Volume 1 - Physiology, genetics and cultivation (Editor: Dr Greg Lang). Burleigh Dodds Science Publishing.

Khanal, K., S. Bhusal, M. Karkee, P. Scharf, and Qin Zhang. 2019. Design of Improved and Semi-Automated Red Raspberry Cane Bundling and Tying Machine Based on the Field Evaluation Results. Transactions of the ASABE. 62(3): 821-829.

Li, J., Shi, Y., Veeranampalayam-Sivakumar, A. N., & Schachtman, D. P. (2018). Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system. Frontiers in plant science, 9, 1406.

Lin, P., W. S. Lee, Y. M. Chen, N. Peres, and C. Fraisse. 2019. A deep-level region-based visual representation architecture for detecting strawberry flowers in an outdoor field. Precision Agriculture. Published online: 07 June 2019. https://doi.org/10.1007/s11119-019-09673-7.

Luck, B. D., R. Willett, J. L. Drewry, L. Ferraretto. Monitoring Kernel Processing During Harvest. University of Wisconsin Division of Extension Publication No. A4174. https://learningstore.extension.wisc.edu/products/kernel-monitoring-processing-during-harvest?_pos=1&_sid=6963f54c3&_ss=r

Miller, K.A., J.D. Luck, D.M. Heeren, T.H. Lo, D.L. Martin, and J.B. Barker. 2018. A geospatial variable rate irrigation control scenario evaluation methodology based on mining root zone available water capacity. Precision Agric. 19(4): 666-683. (Contribution 40%)

Miller, N.J., Griffin, T.W., Ciampitti, I., and Sharda, A. 2019. Farm Adoption of Embodied Knowledge and Information Intensive Precision Agriculture Technology Bundles. Precision Agriculture 20(2):348-361 https://rdcu.be/6LvT

Miller, N.J., Griffin, T.W., Goeringer, L.P., Ellixson, A. and Shanoyan, A. 2018. Estimating Value, Damages, and Remedies when Farm Data are Misappropriated. Choices. 4th Quarter 2018 33(4)

Osroosh, Y., L. R. Khot and R. T. Peters. 2019. Detecting fruit surface wetness using a custom-built low-resolution thermal-RGB imager. Computers and Electronics in Agriculture, 157: 509–517. https://doi.org/10.1016/j.compag.2019.01.023

Pena Quinones, A. J., M. Keller, M. R. Salazar-Gutierrez, L. R. Khot and G. Hoogenboom. 2019. Comparison between grapevine tissue temperature and air temperature. Scientia Horticulturae, 247: 407–420. https://doi.org/10.1016/j.scienta.2018.12.032

Ranjan, R., G. Shi, R. Sinha, L. R. Khot, G.–A. Hoheisel and M. Grieshop. 2019. Automated solid set canopy delivery system for large scale spray applications in perennial tree–fruit crops. Transactions of the ASABE, In Press. https://doi.org/10.13031/trans.13258   

Ranjan, R., A. Chandel, L. R. Khot, H. Bahlol, J. Zhou, R. Boydston and P. Miklas. 2019. Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology. Information Processing in Agriculture, https://doi.org/10.1016/j.inpa.2019.01.005

Rohrer, R.A., S.K. Pitla, J.D. Luck, and R.M. Hoy. 2018. Evaluation of the accuracy of machine reported CAN data for engine torque and speed. Trans. ASABE 61(5): 1547-1557 (Contribution 20%)

Santiago, W. E., N. J. Leite, B. J. Teruel, M. Karkee, C. A.M. Azania. 2019. Evaluation of bag-of-features (BoF) technique for weed management in sugarcane production. Australian Journal Crop of Science. Accepted.

Sharda, A., G. Hoheisel, M. Karkee, and Q. Zhang. 2019. Design and evaluation of solid set canopy delivery system for spray application in high-density apple orchards. Transactions of the ASABE.

Siegfried, J, R. Khosla, L. Longchamps. 2018. Multispectral satellite imagery to quantify in-field soil moisture variability. J. Soil Water Conserv. doi:10.2489/jswc.74.1.33

Sinha, R., L. R. Khot, A. Rathnayake, Z. Gao and N. Rayapati. 2019. Visible−near infrared spectroradiometry−based detection of grapevine leafroll−associated virus 3 in a red−fruited wine grape cultivar. Computers and Electronics in Agriculture, 162: 165-173. https://doi.org/10.1016/j.compag.2019.04.008 

Sinha, R., L. R. Khot, G.–A. Hoheisel, M. Grieshop and H. Y. Bahlol. 2019. Feasibility of a solid set canopy delivery system for efficient agrochemical delivery in vertical shoot positioning trained vineyards. Biosystems Engineering, 179: 59-70. https://doi.org/10.1016/j.biosystemseng.2018.12.011  

Sinha, R., R. Ranjan, G. Shi, G.-A. Hoheisel, M. Grieshop and L. R. Khot. 2019. Solid set canopy delivery system for efficient agrochemical delivery in modern architecture apple and grapevine canopies. Acta Horticulturae, Accepted, In Press.

Thompson, L.J., B.R. Krienke, R.B. Ferguson, and J.D. Luck. 360-degree Video for Immersive Learner Engagement. J. Extension [On-line], 53(4) Article 5TOT2. Available at: https://joe.org/joe/2018september/pdf/JOE_v56_5tt2.pdf (Contribution 20%)

Lv, G. Li, J.A. Benediktsson, Z. Zhang, and J. Yan, 2019. Training sample refining method using an adaptive neighbor to improve the classification performance of very high-spatial resolution remote sensing images. Journal of Applied Remote Sensing, vol. 13, no. 3, 2019.

Zhang, Q., M. Karkee, A. Tabb; The Use of Agricultural Robots in Orchard Management. In Robotics and Automation for a More Sustainable Agriculture (Editor: John Billingsley); rXiv preprint arXiv:1907.13114 (2019).

Zhang, Y. Jin, B. Chen, and P. Brown. California Almond Yield Prediction at the Orchard Level with a Machine Learning Approach. Frontiers in Plant Science, 10:809, 2019.

Zhao, R., Z. Shi, Z. Zou, and Z. Zhang. Ensemble-Based Cascaded Constrained Energy Minimization for Hyperspectral Target Detection. Remote Sensing, vol. 11, no. 11, 2019.