Brief Summary of Minutes of Annual Meeting

The meeting started with a tour of the University of Kentucky Horticulture Research Farm at 8am where the discussion focused on the development of diversified vegetable production to help in the transition away from tobacco production. The farm is on the site of an historic hemp plantation, and at 9am there was an introduction to the history of hemp production in Kentucky and its recent resurgence. At 10am, the group toured Elmwood Stock Farm which is a diversified vegetable and livestock farm that primarily produces for Community Supported Agriculture (CSA) and farmers’ markets. After lunch the group traveled to the University of Kentucky’s Spindletop Research Farm to see various hemp production trial plots for fiber/grain/CBD production. The group then toured the Zelios CBD-processing facility to understand how the hemp is handled post-harvest and how the CBD oil is extracted. The day finished with a tour at Kenneth Anderson’s farm, which has shifted from tobacco production to hemp but while using the tobacco transplant growing model. Discussion focused on crop characteristics and harvest.

The business meeting began at 8am on Friday, September 6^th, in the C.E. Barnhart Building on the University of Kentucky campus. At the beginning of the meeting, a trainer provided an impact writing workshop to help researchers improve their ability to communicate the broader impacts of their research. After a presentation, there was an opportunity to practice writing. The workshop was followed by the business meeting. The first item of business was to select the location for the next meeting. It will be hosted by Washington State University and be in the Tri-cities area. There was a desire to adjust the format of the meeting to increase the meeting to a full two days. The meeting will still include demonstration of local advances in specialty crop production and the business meeting. Additionally, it will include a Brainstorming session and Roadmap planning workshop so that we can identify the key challenges and opportunities in specialty crop automation for the next several years. For tours, the intention is to include demonstrations of the newly commercialized robotic apple picking machines. Joe Dvorak requested that each station provide brief items for inclusion in the annual report within two weeks. Dana Choi of Pennsylvania State University was selected as the next secretary (nominated by Irwin, Alex seconded, all were in favor). Long He transitioned to Vice Chair, and Irwin transitioned to the Chair. This was followed by station reports which continued through lunch.

Highlights of station reports:

Washington State University – Qin Zhang

• Smart irrigation system development
• Robotic weed control
• Targeted apple harvesting (shake-and-catch harvesting, robotic picking)
• Crop load estimate using cellphone
• Robotic pruning research
• Blossom thinning research
• Green shoot thinning for grapevines
• UAS bird deterrence
• SSCDS- Solid spay system for tree orchards
• Steam treatment for citrus HLB
• Field Phenomics for variety selection
• Fire blight assessment for apple trees
• Potato/onion rot detection
• Preventing frost damage for fruit crops

Auburn University – Zhongyang Cheng

• Sensor development (Real-time detection of bacteria/spores)
  • With the capability of direct detection on fresh surface, detection from extra-low concentration, and in-Field detection
• Sensor for agriculture

Water/Humidity sensors (extra low cost), and soil elements mapping 

UC Davis – Irwin Donis-Gonzalez

• Handheld spectrophotometer (F-750)
  • Working with breeding/genetics for tomato and pepper.
• Non-Constructive characteristics of structure changes in vitro gastric digestion of apples using 3D time series micro computed tomography (Micro-CT) 

Texas A & M – Alex Thomasson

• UAV Image data calibration
• Fixed wings UAV, using manned aircraft for ground truth
• Plant height calibration
• 3D canopy model estimation
• Thermal calibration tiles 

Penn State – Daeun Choi

• Fruit detection for yield estimation using depth image
• Mushroom harvester
• Maize root phenotyping system
• Sensor based irrigation system for specialty crops
• Robotic branch pruning system for apple trees
• Frost protection using UAV and UGV 

Oklahoma State University – Ning Wang

• Developing phenotyping tools for peanut breeders
  • Canopy architecture
  • Flower counts
  • Stress indicators
• Developing sensing and control technologies for pecans and processors
• X-ray imaging for detecting peanut smut disease and automated system for peanut smut detection.

University of Kentucky – Joe Dvorak

• UAV imaging for Alfa-Alfa (NDF, ADF, CP, Yield model estimation)
• Future: Precision navigation in specialty crops 

The station reports and discussion on the reports were concluded and the business meeting adjourned at 12:30pm.

Arizona

A high speed, centimeter scale resolution sprayer that can spot apply herbicides to weeds while traveling at speeds that are viable for commercial farming operations was developed and tested.  Results showed that targeting accuracy of spray delivered was ± 2 mm and that the percentage of off-target spray was less than 3%. Weed control efficacy exceeded 95% and there was no observable crop injury. Results were presented at the 2019 ASABE Annual International Meeting.

California

Objective 3: Study interactions between machinery and crops

A small fruit-catching system that utilizes inflatable tubes was developed for shake-catch harvesting. The effect of inflated tubes on fruit damage was tested in pear and cling-peach orchards. Fruits hanging up to 1 ft above the tubes were dropped on the tubes. Results showed no significant difference between dropped fruits and fruits collected manually, as control.

Objective 5: Design and evaluate automation systems

A harvest-aid orchard platform was modified so that pickers on the right side of the platform can work at variable heights from the ground. Actuation was performed via hydraulic cylinders under computer control. The platform’s speed was controlled with a computer-controlled hydraulic valve that modulated flow to the hydraulic motors. A stereo camera system in the front of the platform detected apples, and two instrumented picking bags measured each picker’s harvesting rate. An online optimizing controller varied each picker’s working height and platform speed, with the objective of maximizing the machine’s harvesting throughput. Experiments in a Fuji apple orchard block, in Lodi CA, showed that the machine’s harvesting throughput increased by an average of 9% over the throughput when pickers worked at fixed heights, which is the current situation.

To address the challenge of quantifying Plant water Status (PWS) in a convenient manner, a continuous sensor suite (- leaf monitor) was developed at UC Davis to measure leaf temperature and other microclimatic variables (i.e., photosynthetically active radiation (PAR), relative humidity, air temperature, and wind speed) and subsequently tested in almond, walnut, and grape crops.   Filed tests were conducted in an almond orchard located in Arbuckle, CA using a wireless network of leaf monitors and controllers to implement precision irrigation.   The analysis of the data indicated significant water savings without impacting yield or harvest quality. Compared to evapotranspiration estimates and soil moisture based methods used by grower, this plant-sensing irrigation scheme used an average of 70% and 83% of water, respectively. Moreover, water productivity was significantly higher for the plant stress-based (1870 ± 190 lb/acre-ft) treatment compared to grower treatment (2110 ± 300 lb/acre-ft).   In addition, a commercial version of these leaf monitors were installed and monitored throughout the 2018 growing season in a vineyard as well as in walnut and pistachio orchards.   In these vineyard and orchards, growers implemented their conventional irrigation practices and the leaf monitors were used to track PWS.   The system has performed well during the whole season and additional tests are being conducted during 2019 growing season.

Connecticut

A multi-disciplinary team at the University of Connecticut began work to develop a monitoring system for insect pest damage using small unmanned aerial systems (UAS) outfitted with spectral sensors. The project objective is to detect the presence and early plant damage caused by the potato leafhopper Empoasca fabae on beans. Initial steps are underway and primarily deal with documenting the leaf spectral responses generated during leafhopper feeding in a controlled setting.  Another insect under investigation is the cabbage aphid Brevicoryne brassicae. The plant spectral data collected so far were used in a preliminary analysis of spectral indices suggested in the literature for detection of plant stress. Further work will follow up on these results.

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.  

Iowa

Model and analyzed the effect of the tines of a mechanical weeder on soil and simulated plant disturbance.  

Investigated the current state of art in robotic weed management and control systems and wrote a book chapter that is now distributed globally.

Analyzed data from spray drift experiments and found patterns in the likelihood of drift volumes with different wind velocity and distance into off-target regions.

Complete the robotic rover development for Enviratron facility for plant performance research under different environmental conditions. Designed and prototyped a field-based plant phenotyping robot for genomics research.

Kentucky

A UAV-based photogrammetry method to create a 3D model of the plant canopy has been developed for alfalfa. This 3D model is being used to estimate nutritive value and yield in this forage crop. Testing has also been performed in lettuce to determine if the procedure will be useful to predict lettuce yield as well.

Oklahoma

The practice of hand-opening pods for rating disease is a major bottleneck in breeding for peanut smut resistance. This method is so slow that disease ratings for one season are often not completed until seed for the next season is being planted. Healthy peanut pods filled with seed are denser than infected pods filled with teliospores of Thecaphora frezzii. In 2018-19, we have been evaluating alternative, efficient methods for screening peanut smut in the U.S. using faux-infected pods. The methods included X-ray imaging, gravity separation, microwave dielectric sensors, microwave resonant cavity, and audible sensors. The x-ray imaging method showed an advantage on detecting damaged seed(s) in pod.   

Pennsylvania

The RootRobot for plant excavation and phenotyping was fabricated and components were lab tested.

A sensor-based irrigation system using soil-moisture, evapotranspiration, and plant stress was installed and tested in apple orchards.

A robotic pruning end-effector with two rotational mechanism was developed for apple trees.

A machine vision system for early season apple yield estimation was developed.

Prototypes of automatic mushroom harvester were developed and tested. The system consisted of a vision system for maturity and size evaluation and an end-effector for pulling crops from production bed.

Washington

WA team has worked on multiple aspects of sensing and automation technologies, including robotic devices, crop sensing systems, advanced spraying technologies, for supporting more effective specialty crop, from vegetables, grapevines, to fruit tree, productions. The accomplishments in the past year including (but not limited to): (1) developed and integrated a vision-based fruit orientation estimation and obstacle avoidance system to a 12-armed robotic apple harvester, and evaluated in commercial orchards environment; (2) improved and further evaluated targeted shake-and-catch fresh-market harvesting system and reached a 90% or higher fruit removal efficiency with 10% or less fruit damage in formally trained orchards for selected apple varieties; (3) modified the self-leveling mechanism and improved the vision weed detection system of a self-propelling weeding robot and further evaluated it on commercial carrot and onion fields.  This improved machine system achieved a 99% or higher weed detection accuracy and 2 mm or less spraying accuracy while travelling on uneven natural terrain; (4) conceptualized an automated green shoot thinning mechanism for grapevines using a deep-learning-based machine vision system for detecting vine cordon and shoots and developed a trajectory fitting model to represent cordon position and orientation which reached a 80% cordon trajectory estimation; (5) further optimized a solid set chemical delivery system (SSCDS) for vineyards and orchards applications through creating a reservoir sub-system to maintain the pressure for achieving more precise application control and reducing drift losses to the air; (6) developed an Internet-of-Things (IoT) enabled Crop Physiology Sensing System (CPSS) through encompassing a thermal-RGB imager integrated with a single board computer for monitoring apple fruit surface temperature and controlling SSCDS performing automated evaporative cooling; and (7) conducted a preliminary field trial of spraying cellulose nanocrystal (CNC) for frost protection at two commercial orchards in 2019 Spring, and found it could increase bud cold hardness by up to 5 °C and did not observe any adverse impact on trees nor fruits.

Activities in Support of Accomplishments

Collaborated with a provide equipment manufacturer (FFRobotics) in developing a 12-armed full-scale robotic apple harvesting system (with the main contribution on designing the visual-based fruit detection and obstacle avoidance system), and participated in field evaluations in commercial orchards. 

Developed a new self-propelled research prototype of multi-layer shake-and-catch harvesting system fresh market apples and evaluated the new prototype in commercial orchards in 2018 harvest season. 

Modified an improved full-scale research prototype of self-propelled robotic weeding machine based on lessons learned from field tests of the previous prototype.  This new machine was successfully tested on natural terrain commercial vegetable fields in 2019 weeding season. 

Another robotic machinery development activity was the improvement of a mechanized red raspberry cane bundling and tying device, focused on design, fabricate and field evaluation of an innovative cane tying mechanism. 

Initiated the project of SMART IRRIGATION, including setup test blocks in a research vineyard, developing research platform(s) for field data collection and bigdata processing.  This project was aimed to optimize water use in wine grape using big data analytics. 

Continued the development of machine vision systems for automating green shoot thinning on grapevines; and extensively conducted in laboratory evaluation for tuning the developed deep learning algorithm for image processing. 

Continued working on developing sensing and automation technologies based on both ground platforms and UASs, including for rapid quantifying and/or evaluating plant biotic and abiotic stressors using hyperspectral imaging and associated data analytics methods. The team has also evaluated the applicability of small-UAS-based multi-spectral sensing modules to monitor retrofitted or modified irrigation techniques.

Developed and evaluated an Internet-of-Things enabled Crop Physiology Sensing System (CPSS) for tree fruit crop loss management with initial focus on apple sunburn management.

Developed and evaluated a prototype horticultural oil thermotherapy system for pear psylla management.

West Virginia

Dr. Tabb gave five talks at industry groups, academic departments in horticulture and computer science, and gave a webinar on camera calibration for the American Society of Plant Biologists.  Through these talks, she shared her research on shape estimation for automating orchard work and phenotyping.  She also participated in some community initiatives, talking about research in agricultural robotics.

Automation and phenotyping for tree fruit. E&J Gallo precision agriculture group. Modesto, California. September 10, 2019.

Autonomously generating shape estimates of plant parts across scales. Phenome 2019. Tucson, Arizona. February 7, 2019.

Transforming Pixels to Millimeters: Geometric Camera Calibration. Plantae webinar series, American Society of Plant Biologists. November 29, 2018.

Autonomous shape phenotyping of trees: strategies using computer vision and robotics. Michigan State University Horticulture Department. East Lansing, Michigan. September 13, 2018.

Estimating plant shape in field settings. University of Minnesota Computer Science and Engineering colloquium. Minneapolis, Minnesota. September 10, 2018.

Panellist: sustainable agriculture at Sweet Briar College, April 23, 2019.

Panellist: Farms 2 Schools, WVU extension service, June 19, 2019.

Arizona

Raja, R., Slaughter, D.C., Fennimore, S.A., Nguyen, T.T., Vuong, V.L., Sinha, N., Tourte, L., Smith, R.F. & Siemens, M.C. 2019. Crop signaling: A novel crop recognition technique for robotic weed control. Biosystems Eng. (submitted)

Govindaraj. D.K., Zhu, L., Siemens, M.C., Nolte, K.D., Brassill, N., Rios, D.V., Galvez, R., Fonseca, J.M. & Ravishankar, S. 2018. Modified Coring Tool Designs Reduce Iceberg Lettuce Cross-Contamination. J. Food Protection. 82(3): 454-462. DOI: 10.4315/0362-028X.JFP-18-317.

Lefcourt, A.M., Siemens, M.C. & Rivadeneira, P. 2018. Optical parameters for using visible-wavelength reflectance or fluorescence imaging to detect bird excrements in produce fields. Appl. Sci., 9(4), [715]; DOI:10.3390/app9040715.

Everard, C.D., Kim, M.S., Siemens, M.C., Cho, H., Lefcourt, A.M. & O’Donnell, C. 2018. A multispectral imaging system using solar illumination to distinguish fecal matter on leafy greens and soils. Biosystems Eng. 171: 258-264.

California

Vougioukas, S.G. (2019). Agricultural Robotics. Annual Review of Control, Robotics, and Autonomous Systems. 2:365-392. https://doi.org/10.1146/annurev-control-053018-023617

Charlton, D., Edward Taylor, j. E., Vougioukas, S.G. (2019). Can Wages Rise Quickly Enough to Keep Workers in the Fields? Choices, 2nd Quarter 34(2). http://www.choicesmagazine.org/choices-magazine/submitted-articles/can-wages-rise-quickly-enough-to-keep-workers-in-the-fields

Charlton, D., Edward Taylor, J.E., Vougioukas, S.G., Rutledge, Z. (2019). Innovations for a Shrinking Agricultural Workforce. Choices, 2nd Quarter 34(2).  http://www.choicesmagazine.org/choices-magazine/submitted-articles/estimating-value-damages-and-remedies-when-farm-data-are-misappropriated/innovations-for-a-shrinking-agricultural-workforce

Peng, C., Vougioukas, S.G. (2019). Scheduling performance of harvest-aiding crop-transport robots under varying earliness in access to transport-request predictions. Accepted. ASABE Annual International Meeting. Boston, Massachusetts.

Kizer, E., S. K. Upadhyaya, C. Ko-Madden, F. Rojo, K. Drechsler, and J. Meyers.2018. Good to the last drop-Getting the most out of precision irrigation.  Progressive Crop Consultant. May/June: 20,22,24-26.

Bazzi, C.L., K. Schenatto, S. K. Upadhyaya, F. Rojo, E. Kizer, and C. Ko-madden. 2019. Optimal placement of proximal sensors for precision irrigation in tree crops. J. Precision Ag. 20(4):663-674.

Dhillon, R., F. Rojo., S. K. Upadhyaya, J. Roach, R. Coates, and M. Delwiche. 2019.  Prediction of plant water status in almond and walnut trees using a continous leaf monitoring system.  Precision Ag. 20(4):723-745.

Bazzi, C. L., K. Schenatto, S. Upadhyaya and F. Rojo. 2018.  Optimal placement of proximal sensors for precision irrigation for in tree crops.  Proceedings of the 14th International Conference on Precision Agriculture, Montreal, Canada. 8pp.

Drechsler, K., I. Kisekka, and S. Upadhyaya. 2018. A comprehensive stress index for evaluating water stress in almond trees.   Proceedings of the 14th International Conference on Precision Agriculture, Montreal, Canada. 9pp.

Ko-Madden, C. T. 2018 Optimal placement of  minimal number of proximal sensors for precision irrigation management.  Unpublished MS thesis, Biological and Agricultural Engineering Department, University of California Davis, 145pp.

Kizer, E. E. 2018. A precision irrigation scheme to manage plant water status using leaf monitors in almonds. Unpublished MS thesis, Biological and Agricultural Engineering Department, University of California Davis.  116pp. 

Florida

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.

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. 

Iowa

Articles

Kshetri, S., B. L. Steward, J.J. Jiken, L. Tang, and M. Tekeste. 2019. Investigating effects of interaction of single tine and rotating tine mechanism with soil on weeding performance using simulated weeds. Transactions of the ASABE. doi: 10.13031/trans.13301

Schramm, M. W., H. M. Hanna, M. J. Darr, and S. J. Hoff, and B. L. Steward. 2019. Sub-second wind velocity changes one meter above the ground. Applied Engineering in Agriculture. doi: 10.13031/aea.12264.

Zhang, W., J. Gai, L. Tang, Y. Ding, Q. Liao. 2019. Double-DQN-based path smoothing and tracking control method for in-field robotic vehicle navigation. Computers and Electronics in Agriculture. DOI: 10.1016/j.compag.2019.104985.

Tu, X., J. Gai, L. Tang. 2019. Robust navigation control of a 4WD/4WS agricultural robotic vehicle. Computers and Electronics in Agriculture 164. DOI: 10.1016/j.compag.2019.104892

Gai, J., L. Tang, B. L. Steward. 2019. Automated crop plant detection based on the fusion of color and depth images for robotic weed control. Journal of Field Robotics 2019: 1-18. DOI: 10.1002/rob.21897.

Xiang, L., Y. Bao, L. Tang, D. Ortiz, M. G. Salas-Fernandez. 2019. Automated morphological traits extraction for sorghum plants via 3D point cloud data analysis. Computers and Electronics in Agriculture 162: 951-961. DOI: 10.1016/j.compag.2019.05.043.

Bao, Y., L. Tang, S. Srinivasan, P. S. Schnable. 2018. Plant architectural traits characterization for maize using time-of-flight 3D imaging. Biosystems Engineering 178: 86-101. DOI: 10.1016/j.biosystemseng.2018.11.005.

Book Chapter

Steward, B. L., J. Gai, and L. Tang. 2019. The use of agricultural robots in weed management and control. In Robotics and Automation for Improving Agriculture. ed. J. Billingsley. Burleigh Dodds Science Publishing: Cambridge, UK.

Conference Paper

Steward, B. L., H. M. Hanna, P. M. Dixon, R. K. Mompremier. 2019. Measuring and modeling the movement of spray droplets into off-target areas. ASABE Paper No. 1901496. St. Joseph, Mich.: ASABE. DOI: doi.org/10.13031/aim.201901496

Pennsylvania 

Shi, X., Choi, D., Heinemann, P., Lynch, J., and Hanlon, M. 2019. RootRobot: A field-based platform for maize root system architecture phenotyping. ASABE Paper No. 1900806. American Society of Agricultural and Biological Engineers. 6 pp.

He, L., Zeng, L., and Choi, D. 2019. Investigation of sensor-based irrigation systems for apple orchards. NABEC Paper No. 19-013. American Society of Agricultural and Biological Engineers. ASABE: St. Joseph, MI.  

Zahid, A., He, L. and Zeng, L. 2019. Development of a Robotic End Effector for Apple Tree Pruning. ASABE Paper No. 1900964. American Society of Agricultural and Biological Engineers. ASABE: St. Joseph, MI.  

He, L., Zhang, X., Ye, Y., Karkee, M., and Zhang, Q. 2019. Effect of shaking location and duration on mechanical harvesting of fresh market apples. Applied Engineering in Agriculture, 35(2), 175-183.

Feng, J., Zeng, L., and He, L. 2019. Apple fruit recognition algorithm based on multi-spectral dynamic image analysis. Sensors, 19(4), p. 949.

Lee, C., Choi, D., Pecchia, J., He, L., & Heinemann, P. 2019. Development of A Mushroom Harvesting Assistance System using Computer Vision. 2019 ASABE Annual International Meeting, Paper No. 190050, page 1-5, July 7 – July 10, 2019.

Jarvinen, T., Choi, D., Heinemann, P., Schupp, J., & Baugher, T. A. 2019. Tree trunk position estimation for accurate fruit counts in apple yield mapping2019 ASABE Annual International Meeting, Paper No. 1900918, page 1-7, July 7 – July 10, 2019.

Shi, Xiaomeng., Choi, D., Heinemann, P., Lynch, J., & Hanlon, M. 2019. RootRobot: A Field-based Platform for Maize Root System Architecture Phenotyping. 2019 ASABE Annual International Meeting, Paper No.1900806, page 1-7, July 7 – July 10, 2019.

Jarvinen, T., Choi, D., Heinemann, P., & Baugher, T. A. 2018. Multiple object tracking-by-detection for apple fruit counting on a tree canopy. 2018 ASABE Annual International Meeting, Paper No. 1801193, page 1-8, July 29 – Aug 1, 2018.

Choi, D., & Jarvinen, T. 2018. "A video processing strategy using camera movement estimation for apple yield forecasting." Proceedings of the 9th International Symposium on Machinery and Mechatronics for Agriculture and Biosystems Engineering, page 1-5, Jeju, South Korea, May 28-30, 2018.

Wang, C., Lee, W. S., Zou, X., Choi, D., Gan, H., & Diamond, J. 2018. Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images. Precision Agriculture. ISBN/ISSN #/Case #/DOI #: https://doi.org/10.1007/s11119-018-9574-5. Online publication.

Washington

Bhusal, S., K. Khanal, S. Goel, M. Karkee, and M. Taylor. 2019. Bird deterrence in a vineyard using an unmanned aerial system (UAS). Transactions of the ASABE; 62(2): 561-569 (doi: 10.13031/trans.12923).

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 (doi: 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, In Press (doi: 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 (doi: 10.1016/j.agrformet.2018.02.013).

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 (doi: 10.13031/aea.12974).

Hohimer, C.J., H. Wang, S. Bhusal, J. Miller, C. Mo, and 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 (doi: 10.13031/trans.12986).

Khanal, K., S. Bhusal, M. Karkee, P. Scharf, and Q. 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 (doi: 10.13031/trans.12973).

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 (doi:  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 (doi: 10.1016/j.scienta.2018.12.032). 

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, In Press (doi: 10.1016/j.inpa.2019.01.005) 

Ranjan, R., G. Shi, R. Sinha, L. R. Khot, G. 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, 62(3): 585-592 (doi: 10.13031/trans.13258).   

Sharda, A., M. Karkee, G. Hoheisel, and Q. Zhang. 2019. Design and evaluation of solid set canopy delivery system for spray application in high-density apple orchards. Applied Engineering in Agriculture 35(5): 751-757 (doi: 10.13031/aea.12512).

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

Sinha, R., L.R. Khot, GA. 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 (doi: 10.1016/j.biosystemseng.2018.12.011). 

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.

Zhang, Q., M. Karkee, and A. Tabb, 2019. 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.

Zhang, Q. 2019.  Basics of Hydraulic Systems (2^nd Edition). CRC Press, (324 pp).

Zhang, X., C. Mo, M.D. Whiting, and Q. Zhang. 2019. Plant-based compositions for the protection of plants from cold damage. US Patent (filed, Docket No. 12770096TA). 

West Virginia

L.J. Nixon, A. Tabb, W. M. Morrison, K. Rice, E. G. Brockerhoff, T.C. Leskey, S. Goldson, M. Rostas, Volatile release, mobility, and mortality of diapausing Halyomorpha halys during simulated shipping movements and temperature changes," J Pest Sci (2019). Doi 10.1007/s10340-019-01084-x

2018. A. Dias, A. Tabb and H. Medeiros, “Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network," in IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3003-3010, Oct. 2018. Doi 10.1109/LRA.2018.2849498
2019. Stumph, M. Hernandez Virto, H. Medeiros, A. Tabb, S. Wolford, K. Rice, T. Leskey, “Quantifcation of Dispersal Patterns of Invasive Insects with Unmanned Aerial Vehicles," in 2019 IEEE International Conference on Robotics and Automation (ICRA), 2019. doi: 10.1109/ICRA.2019.8794116 and arXiv:1903.00815 [cs.RO].

P.A. Dias, Z. Shen, A. Tabb and H. Medeiros, “FreeLabel: a publicly available annotation tool based on freehand traces," in 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). doi: 10.1109/WACV.2019.00010

Datasets and code releases

2019. Tabb, K. E. Duncan, C. N. Topp, “Code and Data from: Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets," [Data set]. Zenodo. 2019. 10.5281/zenodo.3333709, 10.5281/zenodo.3344906
2020. Tabb, “Code from: Using cameras for precise measurement of two-dimensional plant features," Ag Data Commons, 2019. https://data.nal.usda.gov/dataset/code-using-cameras-precise-measurement-two-dimensional-plant-features
2021. A. Dias, A. Tabb, H. Medeiros, “Multi-species fruit flower detection using a refined semantic segmentation network," Ag Data Commons, 2018. 10.15482/USDA.ADC/1423466