Brief Summary of Minutes of Annual Meeting

Meeting Summary:

11:00 am: Meeting call to order (Long He)

11:05 am: Member introduction

11:20 am: Business meeting

• Summary of 2020 meeting/reports
• Available on the NIMSS
• Approved by the committee
• 2022 meeting location and time
• Potential options:
• California, hosted by UC Davis group, mid-June (tentative), 2022
• Tentatively targeted at a few days after 10^th of June
• It is planned to be a two-day in-person meeting
• Motion made by Stavros, seconded by Ning, approved by the committee
• Dana Choi, Qin Zhang, and UC Davis host team will work together in planning 2022 meeting
• New officer nomination/election
• Farzaneh Khorsandi of UC Davis has been nominated then elected as the new Secretary in 2022
• Yu Jiang of Cornell University will serve as the new Vice Chair in 2022
• Dana Choi of Pennsylvania State University will serve as the new Chair in 2022
• Paul made motion and Alex seconded, 
• New announcements and opportunities of collaboration
• Farm of the Future RFA
• Consider inviting NIFA and/or NSF national program leaders to share information on potential funding opportunities in future meetings
• Ganesh Bora: NIFA is working on a workshop/projects related to robot safety (e.g., robot operation safety, potential concerns on robot to human safety)
• PSU: mushroom harvesting SCRI
• WSU: AI Institute (Contact: Qin Zhang)
• UC Davis: Workshop on emerging technologies addressing grand challenges in the produce industry, (January 18-20, 2022, Contact: Irwin Gonzalez)
• UC Davis: opportunities on robot safety and human health with cobot system (Contact: Farzaneh Khorsandi)
• WSU: Special issue in Journal of Field Robotics for agriculture robots (Contact: Manoj Karkee)
• MSU: new positions at USDA-ARS in Michigan and MSU (Contact: Daniel Guyer)
• Annual Report
• Written report of participating institutes for 2021 is due one month from the date of this meeting to Long He (current Chair)
• Long He will share 2020 report to the group as sample and format template
• Report from participating institutes should be concise and should emphasize accomplishments and impacts
• Long He will compile the report, send it to Qin Zhang (Admin advisor) 30 days after the meeting.  Qin will review, edit and finalize the report with admin staffs and submit it to NIFA before the deadline
• Other issues
• Manoj Karkee share the news with the group: Abundant Robotics has discontinued its business on developing and commercializing apple harvesting robots mainly due to funding limitations. The company plans to sell its IPs on the technology to other interested potential buyer(s).
• WSU team is working with and will continue to collaborate with FF Robotics in developing practical robotic solutions (and hopeful with a final marketable product) for robotic harvest of fresh market apples
• Robot cost is still higher than labor cost

12:30 pm: State report presentations by participating institutes

16:15 pm: State report presentations completed

16:30 pm: Meeting adjourned

Arizona

• A novel, energy efficient band-steam applicator for controlling soilborne pests was developed and tested. Trial results showed that band-steam reduced fusarium wilt and lettuce drop disease incidence by >70%, improved weed control by > 85%, reduced hand weeding labor requirements by roughly 30% and increased crop yield increases trends of 24% as compared to the untreated control. Machine work rate (0.5 ac/hr) and energy costs ($325/ac) were considered reasonable in field conditions where initial soil temperature was high (>110 °F).
• On-going field research with vegetable grower cooperators is focused on characterizing the spatial distribution of soil attributes that influence the ability of soil to retain and make available nitrogen applied as fertilizer to vegetable crops during the early phases of growth development. Results have shown that tailored variable-rate applications can reduce the overall rate of nitrogen fertilizer by as much as 40%. Results are shared with vegetable growers during Extension events and grower meetings.

California

• A prototype of an inline moisture estimation system for green coffee using time-domain reflectometry (TDR) was developed. The system can automatically estimate the moisture content of green coffee. Future work will evaluate the implementation of this system in large-scale coffee dry mill facilities.
• A smart machine was developed and deployed that can automatically perform the following tasks: create a deaerated tomato juice sample, sequentially perform color, soluble solids content and pH measurements of the juice, electronically transfer the measurement results to a statewide database, dispose of the tomato juice sample, and clean itself. The fully automated procedure ensures that the inspection process is conducted equitably and reliably statewide and eliminates data transcription errors associated with manual data entry.  A fleet of 30 of these smart machines were designed, fabricated and deployed in the state of California.  The machines were installed at each and every processing tomato facility in California in the 2021 production season. 
• A semi-automated almond weighing system was developed to measure individual tree yields, during commercial harvesting. The system consists of a custom designed funnel-shaped bin, that hangs from an aluminum frame from four load cells. At the bottom of the bin, a trap door is actuated by a hydraulic cylinder, at the push of a button, controlled by the machine operator. The bin can attach to existing harvesters, and specifically, to the receiver-vehicle of a shaker-receiver pair of vehicles. During shaking, the almonds are collected on the catching frames of the shaker and receiver units and diverted to a conveyor belt, which transports the almonds inside the bin. Once all the almonds of a given tree are collected inside the bin, their weight is measured, and the trap door releases them on the ground to empty the bin and prepare for the next tree-yield measurement. An RTK-GPS records the bin’s position, so that yield can be associated with individual trees, given a pre-existing map of the orchard. A custom-build data acquisition unit collects and stores all data. The system was built, calibrated and tested, and deployed in a commercial almond orchard in California.
• An autonomous ATV was developed to evaluate the performance of roll bars in the agricultural ATV incidents. In addition, a sensing setup and simulation models to evaluate the youth capabilities to ride ATV on farms, were developed. Our long-term goal is to reduce the likelihood of ATV-related fatalities and injuries of youth in the agriculture industry. The proposed study is in response to the need, as outlined by several agencies, to scientifically measure physical and ergonomic factors that if lacking may put youth at risk of injury or death while operating utility ATVs. The study will provide critical evidence that contributes to the scientific bases for modifying regulatory/advisory guidelines, and state and national policy for operating ATVs. In this project, we identify potential strength and anthropometric discrepancies between the requirements for operating utility ATVs and the physical characteristics and strength of youths of varying ages and height percentiles.
• An agricultural sensing kit (georeferenced RGB + depth imagery) that easily mounts on the front of a tractor and automatically turns on and off to sense the canopy during routine operations like spraying and sulfur dusting was developed. The system is used for early-season yield estimation in grape and almond. Future work will deploy multiple sensing kits during the growing season.

New York

• Ground autonomous robot for versatile vineyard research and management: A ground autonomous robot was developed with a reconfigurable mounting structure for various sensors. The robot is equipped with an RTK-GPS and IMU to provide GNSS-INS-based navigation. The robot was tested in multiple research vineyards for automated data collection in 2021.
• Development and testing of an AI-based imaging system for grape downy mildew monitoring: Developed a deep learning-based image analysis pipeline that can process RGB images collected using a custom stereo camera with strobe light illumination for grape downy mildew detection and quantification.
• Evaluation of an autonomous robotic carriage and UV-C lamp arrays light for plant disease suppression: We used an autonomous robotic carriage and UV-C lamp array to suppress diverse grapevine diseases, including powdery mildew (Erysiphe necator) and sour rot (a complex of bacteria, insects, and fungi). The robot was deployed in both a research vineyard at Cornell AgriTech (Geneva, NY) and a commercial vineyard in Penn Yan, NY. UV-C at 100 to 200 J/m**2, either once or twice weekly provided suppression equivalent to that provided by many conventional pesticides.  In parallel trials, the same technology suppressed powdery mildew on strawberry and cucurbits, fire blight on apple, and Cercospora leaf spot on beets.  However, UV-C provided only minimal suppress of the Oomycete pathogen causing grapevine downy mildew (Plasmopara viticola).  The foregoing experiments were expanded in 2021 to include cooperators in FL, CA, OR, WA, Canada (Nova Scotia), Great Britain, Italy, and Norway.

Florida

• A strawberry plant wetness detection system has been developed using color imaging and deep learning for strawberry production. Leaf wetness duration is an important measure to determine the risks of disease. The system could replace currently used commercial plant wetness sensors, which are unreliable and hard to calibrate.
• A smartphone-based tool was developed to detect and count two-spotted spider mites (TSSM) on strawberry plants. A deep learning method, You Look Only Once (YOLO), was used to detect TSSM and eggs, and a mean average precision of 0.65 was obtained.
• A strawberry bruise detection system for postharvest fruit quality evaluation was developed utilizing machine vision and deep learning.
• A disease detection and monitoring system was developed for downy mildew in watermelon utilizing UAV-based hyperspectral imaging and machine learning. This technique was able to classify several severity stages of the disease.
• A yield and related traits prediction system was developed for wheat under heat-related stress environments. This high-throughput system utilizes UAV-based hyperspectral imaging and machine learning. A yield prediction system was developed for citrus too utilizing UAV-based multispectral imaging and AI.
• A system was developed to determine leaf stomatal properties in citrus trees utilizing machine vision and artificial intelligence.
• A machine vision based system was developed to measure pecan nut growth utilizing deep learning for the better understanding of the fruit growth curve.

Kentucky

• An automated guidance and weed control study was performed at the University of Kentucky in kale. Horticulturalists at UK have developed machinery recommendations for organic crops in non-plasticulture growing systems. This study used the previously developed machinery field operations (bed preparation, drip tape laying, bed forming, transplanting, weeding), but considered the impact of using high precision RTK automated guidance for tractor control. With precision guidance for the tractor, it was possible to eliminate (with acceptable levels of crop damage) the requirement for secondary guidance on the weeding implement.

Mississippi

• Plastic Contaminant Sensor for Ginning Contaminant-Free Cotton: We are applying our sensor technology for specialty crop into a real-time system for detecting plastic contaminant in seed cotton (raw cotton) using machine learning algorithms. We integrate this sensor with a separator system designed to segregate contaminated cotton from “clean” cotton in a stream in real-time. The impact of this technology will include production of contaminant-free cotton which has better market value and preserve good reputation of producers.
• Inline Moisture monitoring system: We are incorporating capacitive sensor technology to monitor moisture of cotton stream in an inline real-time fashion to produce control data for automating drying systems inside a cotton gin. The impact of this technology will include a higher efficiency ginning process and the production of higher quality cotton.

Michigan

• Indirect collaboration on trials and testing continue with a U.S. specialty crops importer and also an international technology entity toward the commercial development and implementation of utilizing the concepts of application of Computed Tomography developed at Michigan State University for evaluation of internal quality of fresh chestnut. Through these entities, electronic sorting and measurement experience, including the development of new high-speed CT imaging, has been brought to the table and is involving testing and data analysis algorithm development focused on internal chestnut defect/quality assessment. Advancement has been promising to date with continued testing planned this season. The effort to date has been worked on or investigated with a faculty-student extracurricular engineering group and Mechanical Engineering colleagues interested in sustainable agriculture solutions. Possible external proposal opportunities have also been identified and will likely be pursued.  At least one Mechanical Engineering Capstone Design team is planned to work on this project in this fiscal year.

Oklahoma

• We conducted an experiment on using X-ray imaging techniques to detect peanut smut damages. As peanut with smut fungus could not be found in the US, our collaborator at USAD ARS made “fake peanut” samples for the tests using similar materials to the smut. The result showed a clear correlation between features extracted from the x-ray images and fake smut damaged peanuts. Based on the results, we designed an X-ray imaging system for peanut smut detection. The mechanical design for an X-ray Imaging system was completed and the system is being constructed. The outcome of this work will be an automated, x-ray imaging system for peanut smut detection. The system can also be used for other nuts on damage detection with revisions on image processing and analysis algorithms.

Oregon

• A recent study evaluated labor-saving machines in the wine grape industry with the aim of preserving and enhancing fruit and wine quality while using automation. Four vineyard tasks were evaluated that field data was readily available from growers, and the technology had the highest near-term chance of success. The four tasks were 1) precision pruning, 2) shoot thinning and desuckering, 3) leaf pulling, and 4) mechanical harvesting.

Pennsylvania

• A LoRaWAN based Internet of things (IoT) platform was developed for the soil moisture based precision irrigation system. The system was tested in a peach orchard, and the results indicated that the developed precision irrigation system can monitor the real-time (at one minute frequency) soil moisture levels and control the valve automatically to apply water to the crops based on the soil moisture levels.
• An inlet airflow control system was developed for the precision sprayer to control the airflow speed based on the canopy density. In the system, a 3D LiDAR was used to measure the canopy density, and an iris damper was mounted at the rear of the sprayer fan to adjust the inlet airflow. The system was tested and validated in the field condition with various tree canopy densities, and has been proven to be effective for precise spraying and drifting reduction.
• A series of green fruit removal dynamic tests were conducted for a few apple cultivars, and the required fruit removal forces were recorded. Then a green fruit removal end-effector was developed and tested with both a handheld mechanism and a robotic manipulator. This study provided guideline information for developing a robotic green fruit thinning system.
• To develop a robotic precision pollination system, a Mask R-CNN based detection algorithm was developed to identify the king flowers in the apple trees. The developed algorithm was able to detect and locate apple king flowers with the accuracy range from 60%-91% depending on different flowering stage.
• The RootRobot for maize root phenotyping was field tested in Fall 2020.  Issues with robustness were addressed.  Upgrades were completed in the lab in Winter, Spring, and early Summer 2021. The ability to handle multiple stalks for processing was added.  Field tests in Summer 2021 revealed issues with sensor light sensitivity and also the unit’s ability to excavate and grab the corn stalks.  An imaging chamber was developed, tested, and upgraded for taking images from 10 cameras at 1 degree increments, resulting in 3600 images per stalk.  These are used for developing 3-D models of the root architecture.
• A computational fluid dynamics model of orchard heating was created for the CPS frost project.  This links with the UAV sensing and communication to ground robots that would carry the heaters to cold locations within the orchard.
• A camera system with active lighting was developed to overcome varying outdoor conditions. There was substantial improvement in image brightness and color consistency by using the LED flashes. Images captured by the prototype system during an 11-hour period showed an average decrease of 85% in standard deviation for the Hue-Saturation-Value (HSV) channels compared to that of the auto-exposure setting. Additionally, the prototype system was able to fix motion blur in images averaging 7 mm in error for a stereo vision application with the camera moving at 7 km/hr.
• Heating requirement assessment methodology for frost protection in an apple orchard was developed utilizing UAV-based thermal and RGB cameras. A thermal image stitching algorithm using BRISK feature was developed for creating georeferenced orchard temperature maps, which attained a sub-centimeter map resolution and a stitching speed of 100 thermal images within 30 seconds. YOLOv4 classifiers for six apple flower bud growth stages in various network sizes were trained based on 5040 RGB images, and the best model achieved a 71.57% mAP for a test dataset consisted of 360 images.
• We developed a UAV-LiDAR system employing UAV’s built-in navigation units. A novel approach for registering UAV-LiDAR data of level agricultural fields was developed utilizing a colored iterative closest point (ICP) algorithm and GNSS location and IMU orientation information from the UAV. The proposed algorithm was tested in a peach tree parameter estimation experiment. Using manually measured crown widths in two perpendicular dimensions and heights of 11 trees as evaluation metrics, our proposed algorithm achieved a root mean square error (RMSE) range of 0.05 to 0.2 m depending on the tree parameter and flight altitude, and it was able to register tree point clouds up to 67% more accurately in terms of the extracted tree parameters than the georeferencing method.

Washington

• Developed a trellis wire detection technique for a 12-armed robotic apple harvester and continued field evaluation of the harvester in commercial orchards.
• Developed a machine-vision system for detecting flower clusters (with 86% accuracy) and estimating flower density (with 84% accuracy) in apple and cherry orchards, which will be used in developing an automated thinning and pollination systems.
• Further developed a machine-vision (using a tracking camera) and robotic system for pruning apple trees, which was tested in the laboratory environment with actual apple trees brought from the field.
• Improved the design of the automated green shoot thinning machine and built the second version of the prototype (larger and faster than first prototype; installed in a gator for continuous operation) which has been tested in the lab environment.
• Developed a machine vision and end-effector technique for robotic pollination of apples.
• Optimized and refined fixed spray system (i.e. solid set chemical delivery system) configurations capable of spraying longer loop lengths using larger sized reservoirs, and conducted pertinent field validation in tree fruit orchards and spray deposition/coverage comparisons with conventional airblast sprayers. Added automation capabilities to operate & monitor system performance via mobile application.
• Improved and conducted field testing of an Internet-of-Things (IoT) enabled Crop Physiology Sensing System (CPSS 3.0) for apple fruit surface temperature monitoring and real-time actuation of modified evaporative cooling system for heat stress management in fresh market apple cultivars (cv Honeycrisp and Cosmic Crisp). CPSS 3.0 has been refined to extract other canopy/fruit quality attributes to aid grower decision support.
• Developed aerial imagery (multispectral and thermal) based spectral sensing integrated energy balance modeling approach to estimate evapotranspiration (ET) at high spatial resolution (10 cm/pixel). Validate the approach for ET and leaf level transpiration (aka crop water use) estimation in grapevine, apple and field crops (mint, alfalfa, potato).
• Developed and validated tree-row-volume and unit-canopy-ration driven base spray rate estimation approaches for an intelligent orchard sprayer to perform efficient spray applications in tall spindle apple crop canopies.

Arizona

Siemens, M.C., Godinez, Jr., V. & Gayler, R.R. 2021. Centimeter Scale Resolution Spot Sprayer for Precision In-Row Weed Control. In Proc. 73rd Annual California Weed Science Society 73:44. Salinas, Calif.: California Weed Science Society.

Siemens, M.C. Godinez, Jr., V., Bahr, N. & Fennimore, S.A. 2021. Development and evaluation of a novel band-steam applicator for controlling soilborne pathogens and weeds in lettuce. ASABE Paper No. 2100185. St. Joseph, Mich.: ASABE.

California

Rotta, N.M., Curry, S., Han, J., Reconco, R., Spang, E., Ristenpart, W.,  & Donis-González, I.R. 2021. A comprehensive analysis of operations and mass flows in postharvest processing of washed coffee. Resources, Conservation and Recycling 170: 105554.

Félix-Palomares, L., & & Donis-González, I.R. 2021. Optimization and Validation of Rancimat Operational Parameters to Determine Walnut Oil Oxidative Stability. Processes 9 (4): 651.

Kilinya Mayanja, I., Coates, M.C., Niederholzer, F., & Donis-González, I.R. 2021. Development of a Stockpile Heated and Ambient Air Dryer (SHAD) for Freshly Harvested Almonds. Applied Engineering in Agriculture. 37(3): 417-425. (doi: 10.13031/aea.14364).

Donis-González, I.R., Bergman, S.M., Sideli, G.M. ,Slaughter, D.C., & Crisosto, C.H. 2020. Color vision system to assess English walnut (Juglans Regia) kernel pellicle color.  Postharvest Biology and Technology 167 (111199): 1-11.

Su, W-H, Fennimore, S.A., Slaughter, D.C.  2020. Development of a systemic crop signalling system for automated real-time plant care in vegetable crops.  Biosystems Engineering. Vol. 193: 62-74. 

Raja, R., Nguyen, T.T., Vuong, V.L., Slaughter, D.C., Fennimore, S.A., 2020.  RTD-SEPs: Real-time detection of stem emerging points and classification of crop-weed for robotic weed control in producing tomato. Biosystems Engineering. Vol. 195: 152-171.

Arikapudi R., Vougioukas, S.G. (2021). Robotic Tree-fruit Harvesting with Telescoping Arms: A study of Linear Fruit Reachability under Geometric Constraints. IEEE Access. (9): 17114-17126 https://doi.org/10.1109/ACCESS.2021.3053490

Fei, Z., Vougioukas, S.G. (2021). Co-Robotic Harvest-aid Platforms: Real-time Control of Picker Lift Heights to Maximize Harvesting Efficiency. Computers and Electronics in Agriculture. (180): 105894. https://doi.org/10.1016/j.compag.2020.105894

Araujo, G. D. M., Khorsandi, F., & Abdullah, A. (2021). Ability of Youth to Activate Agricultural All-Terrain Vehicles’ Main Controls. In 2021 ASABE Annual International Virtual Meeting (p. 1). American Society of Agricultural and Biological Engineers.

Araujo, G. D. M., Khorsandi, F., Kabakibo, S., & Kreylos, O. (2021). Can youth reach agricultural all-terrain vehicle controls? In 2021 ASABE Annual International Virtual Meeting (p. 1). American Society of Agricultural and Biological Engineers.

Khorsandi, F., Ayers, P. D., Myers, M., Oesch, S., & White, D. J. (2021). Engineering Control Technologies to Protect Operators in Agricultural All-Terrain Vehicle Rollover Incidents. Journal of Agricultural Safety and Health, 0.

Khorsandi, F., Ayers, P., Denning, G., Jennissen, C., Jepsen, D., Myers, M., ... & White, D. J. (2020). Hazard control methods to improve agricultural all-terrain vehicle safety. Journal of agromedicine, 1-16.

Khorsandi, F., Ayers, P., Denning, G., Jennissen, C., Jepsen, D., Myers, M., ... & White, D. J. (2020). Hazard control methods to improve agricultural all-terrain vehicle safety. Journal of agromedicine, 1-16.

Chou, H. Y., Khorsandi, F., & Vougioukas, S. G. (2020). Developing and testing a gps-based steering control system for an autonomous all-terrain vehicle. In 2020 ASABE Annual International Virtual Meeting (p. 1). American Society of Agricultural and Biological Engineers.

Fei, Z., Olenskyj, A., Bailey, B., and Earles. J.M. (accepted). Enlisting 3D crop models and GANs for more data efficient and generalizable fruit detection. International Conference on Computer Vision (ICCV), 7th workshop on Computer Vision in Plant Phenotyping and Agriculture.

New York

Ertai Liu, Kaitlin M Gold, David Combs, Lance Cadle-Davidson, Yu Jiang. 2021. Deep Learning-based Autonomous Downy Mildew Detection and Severity Estimation in Vineyards. 2021 ASABE Annual International Virtual Meeting, Paper # 2100486. doi:10.13031/aim.202100486.

Rodrigo Borba Onofre, David M Gadoury, Arne Stensvand, Andrew Bierma, Mark S Rea, and Natalia A. Peres. 2021. Use of Ultraviolet Light to Suppress Powdery Mildew in Strawberry Fruit Production Fields. Plant Disease. https://doi.org/10.1094/PDIS-04-20-0781-RE

Florida

Publications

Costa L., McBreen J., Ampatzidis Y., Guo J., Reisi Gahrooei M., Babar A., 2021. Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes. Precision Agriculture (accepted).

Costa L., Ampatzidis Y., Rohla C., Maness N., Cheary B., Zhang L., 2021. Measuring pecan nut growth utilizing machine vision and deep learning for the better understanding of the fruit growth curve. Computers and Electronics in Agriculture, 181, 105964, doi.org/10.1016/j.compag.2020.105964.

Costa L., Archer L., Ampatzidis Y., Casteluci L., Caurin G.A.P., Albrecht U., 2021. Determining leaf stomatal properties in citrus trees utilizing machine vision and artificial intelligence. Precision Agriculture 22, 1107-1119, https://doi.org/10.1007/s11119-020-09771-x.

Kim, W.-S., D.-H. Lee, Y.-J. Kim, T. Kim, W. S. Lee, and C.-H. Choi. 2021. Stereo-vision-based crop height estimation for agricultural robots. Computers and Electronics in Agriculture 181: 105937. https://doi.org/10.1016/j.compag.2020.105937. 

Kim, W. S., W. S. Lee, and Y. J. Kim. 2020. A Review of the applications of the Internet of Things (IoT) for agricultural automation. J. Biosyst. Eng. 45: 385–400. https://doi.org/10.1007/s42853-020-00078-3.

Nunes L., Ampatzidis Y., Costa L., Wallau M., 2021. Horse foraging behavior detection using sound recognition techniques and artificial intelligence. Computers and Electronics in Agriculture, 183, 106080, doi.org/10.1016/j.compag.2021.106080.

Swarup, A., W. S. Lee, N. Peres, and C. Fraisse. 2020. Strawberry plant wetness detection using color and thermal imaging. J. of Biosystems Engineering. 45: 409-421. https://doi.org/10.1007/s42853-020-00080-9. 

Uyeh, D. D., J. Kim, S. Lohumi, T. Park, B.-K. Cho, S. Woo, W. S. Lee, and Y. Ha. 2021. Rapid and non-destructive monitoring of moisture content in livestock feed using a global hyperspectral model. Animals 11, 1299. https://doi.org/10.3390/ani11051299.

Vijayakumar V., Costa L., Ampatzidis Y., 2021. Prediction of citrus yield with AI using ground-based fruit detection and UAV imagery. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021, 2100493, doi:10.13031/aim.202100493.

Xie, C. and W. S. Lee. 2021. Detection of citrus black spot symptoms using spectral reflectance. Postharvest Biology and Technology 180: 111627. https://doi.org/10.1016/j.postharvbio.2021.111627.

Zhou, C., W. S. Lee, O. E. Liburd, I. Aygun, J. K. Schueller, and I. Ampatzidis. 2021. Smartphone-based tool for two-spotted spider mite detection in strawberry. ASABE Paper No. 2100558. St. Joseph, MI.: ASABE.

Zhou, X., Y. Ampatzidis, W. S. Lee, and S. Agehara. 2021. Postharvest strawberry bruise detection using deep learning. ASABE Paper No. 2100458. St. Joseph, MI.: ASABE.

Presentations

Abdulridha J., Ampatzidis Y., Qureshi J., Batuman O., Kakarla S., 2021. Detecting and monitoring the progress of downy mildew disease in watermelon by utilizing UAV–based hyperspectral imaging and machine learning. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.

Adosoglou G., Park S., Ampatzidis Y., Pardalos P., 2021. A high-level task planning of autonomous robots with multi-dimensional loading constraints for precision weed management under field variability. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.

Ampatzidis Y., 2021. AI applications in specialty crops. 2021 Virtual ASABE Annual International Meeting, Special Session - Processing Systems AI and Data Science Application in Food and Biological Material Processing, July 11-14, 2021.

Ampatzidis Y., 2021.  Automation, artificial intelligence and robotics in strawberry production. 9th International Strawberry Symposium (ISHS – ISS2021), May 1-5, 2021. Keynote Speaker.

Costa L., Ampatzidis Y., Shukla S., 2021. Citrus fruit maturity prediction utilizing UAV multispectral imaging and machine learning. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.

Vijayakumar V., Archer L., Ampatzidis Y., Albrecht U., Batuman O., 2021. An automated delivery system for therapeutic materials using needle-based trunk injection to treat HLB affected Citrus. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.

Vijayakumar V., Partel V., Ampatzidis Y., Silwal A., Kantor G., 2021. Autonomous smart sprayer for precision weed management using machine vision and AI. 2021 Virtual ASABE Annual International Meeting, July 11-14, 2021.

Extension-Outreach

Dr. Ampatzidis (Invited talks):

Emerging Technologies and AI for BMP. UF/IFAS Water Wednesdays. February 24, 2021.

Artificial Intelligence in Agriculture. UF EGN1935-FOAI (31730) - Freshman Engineering: Frontiers of AI (virtual lecture). February 9, 2021.

Applications of Artificial Intelligence in Precision Agriculture. Central District Ag BMP virtual meeting. February 3, 2021.

Drones, artificial intelligence, and the future of pest management in vegetable crops. Annual vegetable growers virtual meeting. The Oregon processed vegetable commission. January 25, 2021.

Saving Citrus with NVIDIA. NVIDIA Podcast #75 (https://www.storagereview.com/podcast/podcast-75-saving-citrus-with-nvidia-ai; 45 min), January 2021.

Kentucky

Dvorak, J., Pampolini, L., Jackson, J., Seyyedhasani, H., Goff, B., Sama, M. (2021). Predicting Quality and Yield of Growing Alfalfa from a UAV. Transactions of the ASABE. 64(1): 63-72. doi: 10.13031/trans.13769

Minch, C.*, Dvorak, J., Jackson, J., & Sheffield, S. T. (2021). Creating a Field-Wide Forage Canopy Model Using UAVs and Photogrammetry Processing. Remote Sensing, 13(13), 2487. MDPI AG. http://dx.doi.org/10.3390/rs13132487

Mississippi

Lucas Gay, Filip To, Ruixiu Sui: Moisture Determination of Cotton in Static Conditions Via Capacitive Sensing. Belt-wide Cotton Conference Proceedings, Cotton Engineering Systems, #20434, 3.5.2021

Joshua Tandio, Filip To , Ruixiu Sui: Bench Top Plastic Contaminant Detection in Cotton Using Deep Learning Neural Network Trained with Images Taken under 4 Lighting Colors, Belt-wide Cotton Conference Proceedings, Cotton Engineering Systems, #20420, 3.5.2021

Oregon

Western FarmPress, April 10, 2019, Startup States Push Precision Viticulture

Good Fruit Grower, May 22, 2019, The Margins of Mechanization: Oregon State University economist assesses the costs and benefits of mechanizing vineyard tasks. The Margins of Mechanization

OWRI and Washington Wine Commission sponsored webinar, June 11, 2019, titled "Can Mechanizing Vineyard Tasks Make you Money?" Webinar Recording

National Grape Research Alliance Newsletter, June 2019: The True Cost of Mechanization

Pennsylvania

Journal Articles

Mirbod, O., Choi, D., Thomas, R. and He, L. 2021. Overcurrent-driven LEDs for consistent image colour and brightness in agricultural machine vision applications. Computers and Electronics in Agriculture, 187, 106266.

Yuan, W., & Choi, D. (2021). UAV-Based Heating Requirement Determination for Frost Management in Apple Orchard. Remote Sensing, 13(2), 273.

Zahid, A., He, L., Choi, D., Schupp, J. and Heinemann, P. 2021. Investigation of Branch Accessibility with a Robotic Pruner for Pruning Apple Trees. Transactions of the ASABE, 64(5).

Zahid, A., Mahmud, M.S., He, L., Heinemann, P., Choi, D. and Schupp, J. 2021. Technological advancements towards developing a robotic pruner for apple trees: A review. Computers and Electronics in Agriculture, 189, 106383.

Huang, M., He, L., Choi, D., Pecchia, J. and Li, Y. 2021. Picking dynamic analysis for robotic harvesting of Agaricus bisporus mushrooms. Computers and Electronics in Agriculture, 185, 106145.

Huang, M., Jiang, X., He, L., Choi, D., Pecchia, J. and Li, Y. 2021. Development of A Robotic Harvesting Mechanism for Button Mushrooms. Transactions of the ASABE, 64(2), 565-575.

Mahmud, M.S., Zahid, A., He, L., Choi, D., Krawczyk, G., Zhu, H. and Heinemann, P. 2021. Development of a LiDAR-guided section-based tree canopy density measurement system for precision spray applications. Computers and Electronics in Agriculture, 182, 106053.

Jiang, X. and He, L. 2021. Investigation of Effective Irrigation Strategies for High-Density Apple Orchards in Pennsylvania. Agronomy, 11(4), 732.

Mahmud, M.S., Zahid, A., He, L. and Martin, P. 2021. Opportunities and Possibilities of Developing an Advanced Precision Spraying System for Tree Fruits. Sensors, 21(9), p.3262.

Thesis/Dissertations

Zahid, Azlan (2021). Development of a robotic manipulator for pruning apple trees. PhD dissertation. August 2021. Pennsylvania State University.

Zhang, Haozhe (2021). Internet of things (IoT)-based precision irrigation with LoRaWAN technology applied to vegetable production. MS thesis. May 2021. Pennsylvania State University.

Mirbod, Omeed (2021). In-field imaging of apple orchards using stereo vision for improved fruit size and yield estimation. MS thesis. May 2021. Pennsylvania State University.

Extension Talks (Long He)

Internet of Things (IoT) for Precision Irrigation Management in Tree Fruit Orchards. February 4, 2021. 2021 Cornell NYS Tree Fruit Conference.

Precision Irrigation Systems for Tree Fruit Orchards-2020 Season Updates. February 11, 2021. 2021 Mid-Atlantic Fruit and Vegetable Convention.

Developing Sensor-Based Smart Irrigation Systems for Vegetable Crops. February 11, 2021. 2021 Mid-Atlantic Fruit and Vegetable Convention.

Potential of Using Robotic Systems on Crop Load Management for Apples. On-Line, 234 Participants. (February 22, 2021). 2021 Penn State Extension Winter Fruit School.

Washington

Journal Articles

Arai, R., S. Sakai, A. Tatsuoka, and Q. Zhang, 2021. Analytical, experimental, and numerical investigation of energy in hydraulic cylinder dynamics of agriculture scale excavators. Energies, Accepted, In Press, https://doi.org/10.3390/en1010000

Chandel, A. K., B. Molaei, L. R. Khot, R. T. Peters, C. O. Stöckle, and P. W. Jacoby. 2021. High-resolution spatiotemporal water use mapping of surface and direct-root-zone drip irrigated grapevines using UAS-based thermal and multispectral remote sensing. Remote Sensing, 13, 954. https://doi.org/10.3390/rs13050954

Chandel , A. K., B. Molaei, L. R. Khot, R. T. Peters, and C. O. Stöckle. 2020. High resolution geospatial evapotranspiration mapping of irrigated field crops using multispectral and thermal infrared imagery with METRIC energy balance model. Drones, 4(3), 52. https://doi.org/10.3390/drones4030052 

Chandel , A., L. R. Khot, and L.-X. Yu. 2021. Alfalfa (Medicago sativa L.) crop vigor and yield characterization using high-resolution aerial multispectral and thermal infrared imaging technique. Computers and Electronics in Agriculture, 182, 105999. https://doi.org/10.1016/j.compag.2021.105999

Kothawade , G., S. Sankaran, A. A. Bates, B. K. Schroeder and L. R. Khot. 2020. Feasibility of volatile biomarker‐based detection of Pythium leak in postharvest stored potato tubers using field asymmetric ion mobility spectrometry. Sensors, 20(24), 7350. https://doi.org/10.3390/s20247350

Kothawade, G., A. K. Chandel , L. R. Khot, S. Sankaran, A. A. Bates, and B. Schroeder. 2021. Field asymmetric ion mobility spectrometry for pre-symptomatic rot detection in stored Ranger Russet and Russet Burbank potatoes. Postharvest Biology and Technology, 181, 111679 https://doi.org/10.1016/j.postharvbio.2021.111679

Majeed, Y., M. Karkee, Q. Zhang, L. Fu, and M.D. Whiting, 2021. Development and performance evaluation of a machine vision system and an integrated prototype for automated green shoot thinning in vineyards. Journal of Field Robotics, 38: 898-916. https://doi.org/10.1002/rob.22013

Marzougui, A., Y. Ma, R. J. McGee, L. R. Khot, and S. Sankaran. 2020. Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in Lentil. Plant Phenomics, 20, Article ID 2393062. https://doi.org/10.34133/2020/2393062

Marzougui, A., Y. Ma, R. J. McGee, L. R. Khot, and S. Sankaran. 2020. Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil [Dataset]. Zenodo. http://doi.org/10.5281/zenodo.4018168 

McCoy, M. L., G.-A. Hoheisel, L. R. Khot, and M. M. Moyer. 2021. Assessment of three commercial over-the-row sprayer technologies in Eastern Washington vineyards. American Journal of Enology and Viticulture, https://doi.org/10.5344/ajev.2021.20058.

Molaei, B., A. Chandel, R.T. Peters, L.R. Khot, and J.Q. Vargas.  2021.  Investigating lodging in Spearmint with overhead sprinklers compared to drag hoses using the texture feature from low altitude RGB imagery. Information Processing in Agriculture, https://doi.org/10.1016/j.inpa.2021.02.003  

Ranjan , R.; R. Sinha , L.R. Khot, G.-A. Hoheisel, M. Grieshop, and M. Ledebuhr. 2021. Spatial distribution of spray from a solid set canopy delivery system in a high-density apple orchard retrofitted with modified emitters. Applied Sciences, 11, 709. https://doi.org/10.3390/app11020709

Ranjan , R., L. R. Khot, R. Troy Peters, M. R. Salazar-Gutierrez and G. Shi . 2020. In-field crop physiology sensing aided real-time apple fruit surface temperature monitoring for sunburn prediction. Computers and Electronics in Agriculture, 157: 105558. https://doi.org/10.1016/j.compag.2020.105558

Rathnayake, A. P., A. Chandel, M. Schrader, G.-A. Hoheisel and L. R. Khot. 2021. Spray patterns and perceptive canopy interaction assessment of commercial airblast sprayers used in Pacific Northwest perennial specialty crop production. Computers and Electronics in Agriculture, 184, 106097 https://doi.org/10.1016/j.compag.2021.106097

Rathnayake, A. P., A. Chandel, M. Schrader, G.-A. Hoheisel and L. R. Khot. 2021. Air-assisted velocity profiles and perceptive canopy interactions of commercial airblast sprayers used in Pacific Northwest perennial specialty crop production. CIGR e-journal, Accepted, In Press.

Rathnayake, A. P., L. R. Khot, G. A. Hoheisel, H. W. Thistle, M. E. Teske, and M. J. Willett. 2021. Downwind spray drift assessment for airblast sprayer applications in a modern apple orchard system. Transactions of the ASABE, 64(2): 601-613. https://doi.org/10.13031/trans.14324  

Sinha, R., J. Quiros Vargas, L. R. Khot and S. Sankaran. 2021. High resolution aerial photogrammetry based 3D mapping of fruit crop canopies for precision inputs management. Information Processing in Agriculture, https://doi.org/10.1016/j.inpa.2021.01.006.

Worasit, S., A. Marzougui, S. Sankaran, L. R. Khot, A. A. Bates, and B. Schroeder. 2021. Identification of volatile biomarkers for high-throughput sensing of soft rot and Pythium leak diseases in stored potatoes. Food Chemistry, Accepted, In Press.

Zhang, X., He, L., Karkee, M., Whiting, M. D., and Zhang, Q. 2020. Field Evaluation of Targeted Shake-and-Catch Harvesting Technologies for Fresh Market Apple. Transactions of the ASABE, 63(6): 1759-1771. https://doi.org/10.13031/trans.13779  

Thesis/Dissertations

Chandel, Abhilash (2021). Small unmanned aerial system based remote sensing to map geospatial water use of field and perennial specialty crops. April 2021. Washington State University.

Ranjan, Rakesh (2021). Sensing integrated automated solid set canopy delivery system for crop loss management in deciduous fruits and grapevines. April 2021. Washington State University.

Books and Book Chapters

Huang, Y. and Q. Zhang, 2021. Agricultural Cybernetics. Springer, ISBN: 978-3-030-72102-2, (255 pp).

He, Y., P. Nie, Q. Zhang and F. Liu, 2021. Agricultural Internet of Things: Technologies and Applications. Springer, ISBN: 978-3-030-65701-7, (439 pp).

Karkee, M. and Q. Zhang, 2021. Fundamentals of Agricultural and Field Robotics. Springer, ISBN: 978-3-030-70399-8, (455 pp).

Karkee, M., Q. Zhang, and A. Silwal, 2021.  Chapter 4. Agricultural Robots for Precision Agricultural Tasks in Tree Fruit Orchards.  In: Bechar, A. (ed). Innovation in Agricultural Robotics for Precision Agriculture. Springer (26 pp).

Zhang, Q. and M. Karkee, 2021. Chapter 1. Agricultural Robotics: An Introduction.  In: Karkee, M. & Q. Zhang (eds.). Fundamentals of Agricultural and Field Robotics. Springer (10 pp).

Karkee, M., B. Santosh, and Q. Zhang, 2021. Chapter 3. 3D Sensing Techniques and Systems.  In: Karkee, M. & Q. Zhang (eds.). Fundamentals of Agricultural and Field Robotics. Springer (39 pp).

Zhang, X., Q. Zhang, M. Karkee, and M.D. Whiting, 2021. Chapter 16. Machinery-Canopy Interactions in Tree Fruit Crops.  In: Karkee, M. & Q. Zhang (eds.). Fundamentals of Agricultural and Field Robotics. Springer (28 pp).

He, Y., Q. Zhang, and P. Nie, 2021. Chapter 1. Introduction of Agricultural IoT.  In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (21 pp).

He, Y., Y. Tang, Q. Zhang, and Y. Zhao, 2021. Chapter 2. Agricultural IoT Standardization and System Applications.  In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (17 pp).

Zhang, Q., Y. He, P. Nie, and S. Xiao, 2021. Chapter 3. Data Communication and Networking Technologies.  In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (56 pp).

Liu, F., Y. He, Q. Zhang, W. Wang, and T. Shen, 2021. Chapter 5. Crop Information Sensing Technology.  In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (32 pp).

Fang, H., Y. He, Q. Zhang, J. Zhang, and Y. Shi, 2021. Chapter 6. Field Condition Sensing Technology.  In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (28 pp).

Nie, P., Q. Zhang, and Y. He, 2021. Chapter 10. IoT Management of Field Crops and Orchards.  In: He, Y., P. Nie, Q. Zhang, & F. Liu (eds.). Agricultural Internet of Things, Technologies and Applications. Springer (12 pp).

Rovira-Más, F., Q. Zhang, and V. Saiz-Rubio, 2020. Chapter 11. Mechatronics and Intelligent Systems in Agricultural Machinery. In: Holden, N. M., Wolfe, M. L., Ogejo, J. A., & Cummins, E. J. (Ed.), Introduction to Biosystems Engineering. Virginia Tech Publishing.