W4009: Integrated Systems Research and Development in Automation and Sensors for Sustainability of Specialty Crops

(Multistate Research Project)

Status: Active

Project Menu

SAES-422 Reports

Annual/Termination Reports:

[10/02/2024] [01/01/1970]

Date of Annual Report: 10/02/2024

Report Information

Annual Meeting Dates: 08/02/2024 - 08/03/2024
Period the Report Covers: 10/01/2023 - 09/30/2024

Participants

Yu Jiang - Cornell University;
Yuzhen Lu - Michigan State;
Alex Thomasson - Mississippi State;
Lirong Xiang - North Carolina State University;
Shirin Ghatrehsamani - Penn State University;
Long He - Penn State University;
Mark Siemens - University of Arizona;
Haiquan Li - University of Arizona;
Pedro Sanchez - University of Arizona;
Alireza Pourreza - University of California Davis;
Farzaneh Khorsandi Kouhanestani - University of California Davis;
Alireza Moghimi - University of California Davis;
Mohsen Mesgaran - University of California Davis;
Irwin Donis-Gonzalez - University of California Davis;
Davie Kadyampakeni - University of Florida;
Won Suk Lee - University of Florida;
Dana Choi - University of Florida;
Changying Li - University of Florida;
Md Sultan Mahmud - University of Georgia;
Jianfeng Zhou - University of Missouri;
Hao Gan - University of Tennessee;
Daniel Jenkins - University of Hawaii

Brief Summary of Minutes

The meeting commenced with a welcome note from Chair Dr. Hao Gan at 8:00 AM on August 3, 2024.

Each university listed above presented their research outcomes.

Secretary Election: Dr. Md Sultan Mahmud self-nominated for the secretary position. He was the sole candidate. Dr. Jianfeng Zhou moved to approve, and Dr. Irwin Donis-Gonzalez seconded the motion.

Vice-Chair Election: Dr. Yuzhen Lu was approved for this position. A motion was conducted by Dr. Jianfeng Zhou. Dr. Irwin Donis-Gonzalez again seconded the motion.

Chair Continuation: Project members approached Dr. Hao Gan to continue his role as Chair for 2024-25. Dr. Gan expressed his willingness to serve and was subsequently approved by the members.

Next Annual Meeting: Project members discussed plans for the next annual meeting in 2025. Three locations were proposed: Michigan (Traverse City), Puerto Rico, and Alaska. Dr. Alireza Pourreza proposed Puerto Rico, expressing a desire to build connections with faculty there. Some members suggested Michigan (Traverse City). Dr. Irwin Donis-Gonzalez highlighted the beauty of Traverse City. Due to a lack of connections in Puerto Rico and Alaska, Dr. Alex Thomasson suggested Michigan. Dr. Yuzhen Lu expressed willingness to organize the meeting in Traverse City. It was decided that the meeting will be held before or after the 2025 ASABE Annual Meeting in Toronto, Canada. Dr. Hao Gan expressed an interest to build connections in Puerto Rico and/or Alaska for future meetings.

New Multi-State Project: Dr. Alex Thomasson discussed a new multi-state project, S1098 (Autonomy), and opportunities for W4009 members.

Collaboration: Dr. Thomasson also suggested coordinating meetings with other multi-state projects with similar interests.

White Paper Proposal: Dr. Yu Jiang proposed writing a white paper on Hawaiian agriculture to secure special funding. The paper will address the challenges and potential solutions for Hawaiian agriculture. He said he will lead this effort.

Annual Report Submission: Discussion on annual report submission emphasized that the report should be less than one page, with no limit on the publication section.

Research Impact Training: Dr. Thomasson discussed how to write research impact in project reports and mentioned checking for available training for faculty.

Dr. Yuzhen Lu discussed potential funding applications for engineering-focused projects within the W4009 groups.

Training Grants: Dr. Irwin Donis-Gonzalez discussed training grants for farmers and stakeholders and the possibility of including all states.

The meeting adjourned at around 12:00 PM with closing remarks by Dr. Hao Gan.

Accomplishments

Arizona <ul> <li>Precision planting technology was implemented for the first time on commercial Arizona farms in the spring of 2024. More adoption of electronic control of planter parameters is expected to continue.</li> <li>A proof-of-concept study was conducted to demonstrate the feasibility of integrating the latest AI techniques with minimal curation efforts to recognize weeds in a lettuce field. A dataset of approximately 500 images was used to train and test two AI models. Results showed that both methods achieved around 80% average recall and precision. Further methodological refinement with more advanced transformer-based models and larger datasets is warranted.</li> <li>Designed and built a commercial scale steam applicator for thermally killing weed seed and soilborne pathogens prior to planting. On-farm trials were initiated in vegetable crops to demonstrate and validate viability of the technology for commercial farming operations.</li> </ul> California <ul> <li>Sixty instrumented strawberry picking carts were used by farm workers for six months to collect data that was used to calculate the yield maps of two fields (on in Salinas, one in Santa Maria) twice a week.</li> <li>A fully automated almond yield mapping system developed at UCD was placed on commercial harvesters and used to gather data in in two almond orchards (Woodland and Tulare) for three weeks.</li> <li>Developed and implemented outreach programs focused on agricultural safety, addressing topics such as ATVs, tractors, cannabis worker safety, sprayer safety, and emerging technologies like robotics.</li> <li>Created a youth ATV fitting checklist.</li> <li>Established an ATV safety test station.Collaboratively initiated a project on improving the performance of agricultural robots' worker detection systems under adverse light conditions.</li> <li>Exploring an ultrasound-based system to detect farmworkers’ spine health status while performing labor-intensive tasks.</li> <li>Exploring workers’ compensation records to investigate the role of emerging automated and robotic systems in injuries to farmworkers.</li> <li>We developed a CNN-based multi-trait model for grapevine nutrient sensing using hyperspectral data from various grape species and growth stages. This model integrates laboratory tissue analysis to more accurately predict multiple biochemical traits, such as nitrogen, phosphorus, and potassium.</li> <li>Developed novel methods for forecasting yield from satellite imagery time-series and management based information using custom deep learning approach.</li> <li>Developed an input-limited ETo model that utilizes solar radiation data and ML algorithms to estimate ETo without requiring a reference grass surface, making it applicable across the Central Valley.</li> <li>Evaluated the performance, computational cost, and complexity of various statistical and advanced machine learning (ML) and deep learning (DL) models for forecasting reference evapotranspiration (ETo) using monthly data from 107 standardized weather stations (CIMIS) in California.</li> <li>Approximately fifty precision yield maps were generated for strawberries and four tree-level yield maps for almond trees.</li> <li>The safety outreach program has delivered 105 presentations at national and international conferences and workshops and organized multiple events, including the Agricultural ATV Safety Symposium with 210 participants from 18 countries. Projects include creating the first U.S. agricultural ATV safety test station and conducting research on ATV safety for youth and female riders.</li> <li>The CNN-based model improves upon traditional single-trait models by capturing the synergistic effects of multiple nutrients. This approach enhances the precision and consistency of grapevine nutrient monitoring, offering deeper insights into plant health.</li> <li>Coffee professionals have discovered that the roast profile significantly impacts the flavor and quality of coffee. Our research on a 5-kg commercial drum roaster revealed that total titratable acidity (TA) peaks during the first crack and then returns to its original value by the second crack, regardless of the roast profile dynamics. This insight can help manipulate and achieve the desired sourness during roasting.</li> </ul> Connecticut <ul> <li>Field experiments were conducted with Provider green beans to demonstrate if a drone multispectral sensor could detect potato leafhopper stress and nitrogen deficiency in beans. Half of the bean plots received leafhoppers  and the others were kept insect-free. Insect treatments were combined with nitrogen manipulations so that beans in control and leafhopper infested plots were grown either with recommended nitrogen applications or without any nitrogen application.  Canopy reflectance was captured using a five-band multispectral sensor. Our findings indicate that drone imagery could detect nitrogen deficiency and potato leafhopper damage in beans.</li> <li>Oral Presentations: (1) Bhusal, B., A. Legrand and C. Witharana. 2024. Detecting nitrogen deficiency and potato leafhopper (Hemiptera: Cicadellidae) infestation in green beans using multispectral imagery from unmanned aerial vehicle. STRATUS 2024 Conference, Syracuse, NY. May 20, 2024. (2) Bhusal, B., A. Legrand and C. Witharana. 2023. Detection of stress induced by potato leafhopper (Hemiptera: Cicadellidae) in green beans using multispectral imagery from an unmanned aerial vehicle. Connecticut GIS Day 2023 Conference. University of Connecticut, Waterbury, CT. November 15, 2023. (3) Bhusal, B., A. Legrand and C. Witharana. 2023. Detection of stress induced by potato leafhopper (Hemiptera: Cicadellidae) in green beans using multispectral imagery from an unmanned aerial vehicle. Entomological Society of America Annual Meeting, Oxon Hill, MD Nov. 6, 2023.</li> </ul> Florida Lee: <ul> <li> A project funded by USDA NIFA for developing a detection system for two-spotted spider mites (TSSM) ended in 2023. TSSM is a major foliar pest of strawberries and almonds, causing significant yield losses. To improve TSSM monitoring, we developed a novel, easy-to-use method to count mites on strawberry and almond leaves using smartphones and AI. A portable detection module was also developed using a high-resolution camera, single-board computer, and deep learning algorithms.  </li> <li>The development of an automated strawberry flower and fruit counting system has continued to forecast yield accurately during the growing season. Strawberry field images were acquired weekly during the growing season, and the number of flowers and fruit were manually counted along with canopy volume measurement. Various algorithms have been investigated to develop strawberry yield forecasting models.</li> <li>The Strawberry Advisory System (SAS) is an advising system for strawberry crop growers. It provides recommendations for timing fungicide applications for controlling Anthracnose and Botrytis fruit rot diseases. In the current SAS, a leaf wetness sensor is used to indicate whether wetness is present in the plant and for how long it stays. Therefore, we are developing an automatic system for monitoring the wetness of strawberry plants using color imaging and AI so that the output of the system can be incorporated into the current Strawberry Advisory System (SAS). In 2023, we implemented a high-resolution camera to increase the performance of the system and obtained better detection accuracy.</li> </ul> Ampatzidis: <ul> <li>Several disease detection and monitoring systems were developed for vegetable crops utilizing UAV-based spectral imaging and machine learning. These technologies were able to classify several severity stages of the diseases.</li> <li>AI-driven yield prediction systems were developed for citrus and tomatoes using aerial and ground sensing systems.</li> <li>An automated truck injection system was developed for HLB-affected citrus trees.</li> <li>A smart tree crop sprayer was developed using sensor fusion and artificial intelligence.</li> </ul> Choi: <ul> <li>Mushroom Detection and Maturity Estimation: The automated system for detecting and estimating the maturity of white button mushrooms using depth images achieved a classification accuracy of 95.31%. This led to a significant reduction in manual labor dependency and enhanced efficiency in harvesting operations. It also increased productivity and ensured that mushrooms were harvested at their optimal maturity for longer shelf life.</li> <li>Mite Dispensing System for Biological Control: The system demonstrated a 69.3% accuracy in releasing predatory mites for the control of chilli thrips in strawberry fields. This system provides an eco-friendly alternative to chemical pesticides, significantly improving pest management and sustainability in strawberry farming.</li> <li>Synthetic Data-Driven AI for Strawberry Yield Estimation: The synthetic image dataset trained model achieved a 68% accuracy in detecting strawberries in the field, and a mixture of synthetic and real images improved accuracy to 90%. This approach reduces reliance on costly field data collection, advancing precision agriculture.</li> <li>Mushroom System: Developed and implemented a novel image processing technique using RGB and depth images for mushroom detection and maturity estimation. The system incorporated YOLOv8 for comparative analysis, achieving an F1-score of 0.92.</li> <li>Mite Dispensing System: Designed a ground-based, automated predatory mite dispensing system integrated with machine vision technology to detect strawberry plants and target pests accurately.</li> <li>Strawberry Yield Estimation System: Developed a hybrid synthetic-real dataset and used machine vision to estimate strawberry yield with high accuracy.</li> </ul> Li: <ul> <li>A custom-built multi-view robotic platform and deep learning model BerryNet were developed for high throughput in-field blueberry yield estimation and fruit machine-harvestability phenotyping.</li> <li>A deep-learning-based web app was developed to rapidly quantify blueberry fruit internal bruising across fifty genotypes.</li> </ul> Massachusetts <ul> <li>Extension Presentations Delivered: Audience consists of commercial growers, ag service providers, and gardeners. (1) Scheufele, S.B. 2024. Tunnel Talk: Managing tunnels for the long-term. Southeastern New England Ag Conference. February 4, 2024. 30 attendees. (2) Scheufele, S.B. 2024. Winter Greens Production, Pest Management, and Profitability. Mid-Atlantic Fruit and Vegetable Convention. PAVGA. Hershey, PA. February 1, 2024. 80 attendees. (3) Higgins, G. 2024. Winter Greens Production in the Northeast. Perennia: Tunnel Talks. Online, based in Nova Scotia, CAN. September 4, 2024. 100 attendees.</li> <li>Newsletter Articles Published: Our Vegetable Notes newsletter is sent weekly to >3,080 growers, Extension professionals, ag service providers, and gardeners from Canada to Georgia. (1) Higgins, G. 2023. Winter High Tunnel Spinach Variety Trial Results, 2022-23. Vegetable Notes. August 17, 2023. Vol. 35:18. (2) Higgins, G. 2024. Improving Production & Yield of Winter Spinach in the Northeast. Vegetable Notes. January 2024. Vegetable Notes 2024 Vol. 36:1. (3) Higgins, G. 2023. Improving Germination and Stand in Winter High Tunnel Spinach. Vegetable Notes. September 14, 2023. Vegetable Notes 2023 Vol. 35:21</li> <li>Factsheets Maintained: Our website receives 318,965 unique hits per year and the factsheets tab is the most clicked after the Vegetable Notes Newsletter Archive. (1) Spinach Downy Mildew: <a href="https://ag.umass.edu/vegetable/fact-sheets/spinach-downy-mildew">https://ag.umass.edu/vegetable/fact-sheets/spinach-downy-mildew</a>. (2) Cladosporium Leaf Spot: <a href="https://ag.umass.edu/vegetable/fact-sheets/spinach-cladosporium-leaf-spot">https://ag.umass.edu/vegetable/fact-sheets/spinach-cladosporium-leaf-spot</a>. (3) Cercospora Leaf Spot of Beet, Spinach, and Swiss Chard: <a href="https://ag.umass.edu/vegetable/fact-sheets/cercospora-leaf-spot-of-swiss-chard-beets-spinach">https://ag.umass.edu/vegetable/fact-sheets/cercospora-leaf-spot-of-swiss-chard-beets-spinach</a></li> </ul> Michigan <ul> <li>Developed and evaluated a machine vision-based, AI-powered automated sweetpotato grading and sorting system, and developed a preliminary smart sprayer with dedicated software for precision vegetable weeding.</li> <li>Published 11 peer-reviewed journal articles, presented 6 conference papers, and published two open-access datasets on weed detection and hyperspectral imagery of blueberries for fruit defect detection.</li> <li>Applied artificial intelligence (AI)-based computer vision methods to fruit quality grading, curated and published open-access datasets on weed detection, and investigated AI-based generative modeling methods for weed image generation. AI and the Internet of Things were leveraged for irrigation and disease management of specialty crops. </li> </ul> Missouri Project: Mechanical harvesting technology for American elderberry: <ul> <li>Activity 1: Baseline study for determining elderberry characteristics: A baseline study is crucial for developing a mechanical elderberry harvester as it provides essential data on fruit characteristics, tree structures, and harvesting challenges. We surveyed eight commercial and research elderberry farms, including three verities (Adam, Bob Gordon and Pocahontas) from Allen’s Farm, Frank Gordon’s Farm, Terry Durham’s Farm and HARF farm. The survey included collecting data of 20 randomly selected plants from each farm. Collected data included: plant height, width, cluster number, cluster size (length, width and height), and pull force.</li> <li>Activity 2: Determining optimum shaking frequency and harvester configurations: Eight hand-held shakers were developed by modifying reciprocating saws with various stroke lengths. Shakers were tested in different farms to study the harvest performance (fruit removal and quality and optimal vibration frequency to remove fruit efficiently. We tested three strokes (0.625, 1 and 1.25 in) and frequency range from 15 to 22 Hz. We found that shakers with stroke 0.625 in at the frequency of 18 to 20 Hz showed less harvesting time, damage rate, and better berry maturity.</li> <li>Activity 3: Design and Development of Mechanical Harvester for Elderberry: A massive elderberry harvester prototype was fabricated and demonstrated to farmers in the prject annual meeting. We are continuing to improve its performance.</li> <li>Activity 4: Fruit Maturity level determination: Fruit Maturity is important for optimum fruit quality and harvesting parameters. We collected fruits from three varieties (Adam, Bob Gordon, and Pocahontas) at different maturity levels in seven fields. Fruits were grouped into 5 maturity levels based on their color, i.e., green, greenish purple, purple, red-purple, and black purple. Photos of grouped fruits were taken. Laboratory analyses, including physical and chemical analyses, were conducted to determine the maturity levels. Maturity prediction models will be developed using color information, which is expected to be used for developing maturity assessment tools for farmers.</li> <li>Activity 5: Elderberry fruit responses to vibration shakers using a high-speed camera: A study was done to determine elderberry cluster behavior under different vibration frequencies and shaker heads.The experiment was conducted using a customized bench-mount shaker at two strokes (0.5 in and 1 in) and 18 to 20 Hz frequency. It was found that hooking position and maturity level greatly affect harvesting effectiveness. We are processing the data.</li> </ul> Mississippi To: <ul> <li>Cotton Moisture Sensor for Realtime Application: A moisture sensing system was developed for use in cotton gins to control drying & rewetting systems, in real-time. The Static & Dynamic modes of operations of the developed system was validated. Two types of sensors were studied: FP32C grain sensor from Agrichem, and custom designed capacitive sensor. Moisture Balance, and Oven were used as standard. Dynamic tests are ongoing.</li> <li>Zhang:</li> <li>Robotic Cotton Picking: The robotic platform was integrated with perception systems, manipulator, control system, and computational systems for lab and field testing. The team developed a perception system for in-field cotton boll detection and segmentation (GELAN-C + SAM), which was compared against and outperformed a modified state-of-the-art FastSAM model, achieving mAP = 86.3%, Precision = 81.7%, Recall = 76.8%, F1-score = 79.2%, and Speed = 27.6 ms/img.</li> <li>Robotic Fresh Market Caneberry Harvesting: Initial testing of robotic harvesting of blackberries at Tifton, GA. Two-year data was collected in Arkansas and Georgia. A preliminary perception system was developed for multi-class blackberry detection in field conditions (YOLOv7, YOLOv8, and YOLOv9), achieving mAP = 92.6%, F1-score = 86.4%, and Speed = 12.6 ms/img using YOLOv7-x. System integration and testing in lab conditions will be carried out next year. During this reporting period, we developed an in-field blackberry detection system using AI-based computer vision technology. We aimed to assess and compare the feasibility, accuracy, and efficiency of a series of state-of-the-art YOLO models in detecting multi-ripeness blackberries in the farm conditions. A total of 1,086 images containing three different ripeness levels of blackberries were collected during the two-year harvesting season, including ripe berries (in black color), berries in the ripening stage (in pink color), and unripe berries (in green color). Eight YOLO models were trained, validated, and tested using randomly selected 809 (74%), 193 (18%), and 84 (8%) images of datasets, respectively. Among all, YOLOv7-x achieved the optimal mean Average Precision (mAP) of 92.6%, F1-score of 86.4%, and inference speed of 12.6 ms per image with 1,024 × 1,024 pixels across all classes of ripeness.</li> </ul>   Pennsylvania <ul> <li>An integrated robotic green fruit thinning system was developed and tested in apple orchards. This project was funded by USDA NIFA, and was ended in June 2024. In this report period, two journal articles were submitted and are under review, and one MS and one PhD students graduated from the project.</li> <li>A robotic apple blossom thinning system was develop with integration of an unmanned ground vehicle. This project was funded by USDA AMS, and was ended in April 2024. In this report period, one journal article was published and another one is under review, and one PhD student graduated from the project. </li> <li>Continued to develop deep learning based models for early apple bud detection and localization, especially for individual branch bud density and distribution measurement. The initial results showed high accuracy of bud detection and density measurement.</li> <li>Continued to on automatic frost management system, an optimal path planning algorithm was developed to determine number of heaters for effective frost protection in various scale orchards. A journal manuscript was submitted and under review.</li> <li>Continued the field tests with the developed precision spraying systems in the apple orchards and vineyard, especially a robotic sprayer was tested and evaluated. A journal article was published for this work.</li> <li>A decision support system was developed for robotic button mushroom harvesting. This system uses a machine vision system to identify optimal picking motion and posture for each individual mushroom.</li> <li>A classification model was developed to identify invasive insects. One such example is the box tree moth, which attacks the ornamental boxwood. This invasive has been spreading in the northeast US and eastern Canada and was just discovered in Pennsylvania. An phone-based app has been developed so that users can upload an image to determine if the insect is a box tree moth or some other similar-looking species.</li> <li>A spectral vision system was developed to detect the asymptomatic diseases on button mushroom and the robot to treat the diseased mushroom is under developing.</li> <li>Various insect identification devices are being developed and tested to identify and classify pollinators and other species, both in the lab and in the field. Automated imaging traps are being tested.</li> </ul> Tennessee <ul> <li>A mobile robot platform was built and revised for Tennessee pot-in-pot nursery production. The chassis and motor control system was upgraded from a skid-steer system to a four-wheel-steer system . Currently, the robot can be remote controlled using a joystick. It includes driving motors, two mechanical grippers to grab the 15-gallon pot, and two linear actuators to lift the pot.</li> </ul> Texas <ul> <li>A deep learning model was developed to detect and classify strawberries based on maturity level (mature, semi-mature, immature). A function is developed for counting strawberries. The results showed mean average accuracy as 95% for classification and counting of strawberries without compromising the precision and satisfying real-time requirements. The developed algorithm can be deployed on edge devices for autonomous monitoring in the greenhouses.</li> <li>An innovative, non-destructive AI-assisted approach to estimate lettuce growth parameters was developed. The AI model showcases exceptional performance in predicting lettuce phenotypic parameters, achieving R2 values of 0.968, 0.953, 0.943, 0.906, and 0.965 for fresh weight, leaf area, dry weight, plant diameter, and plant height, respectively. Using this model, we identified that high nutrient solution temperature (Temp: 30oC, Nitrogen: 150ppm) yielded the highest fresh and dry weights.</li> <li>An integration of image analysis, data imbalance handling, and AI-assisted models were evaluated for early detection of diseases in tomato plants in a greenhouse environment. The evaluation’s indicated that Generative Adversarial Networks (GANs)-based approach for resampling performed best, with an average classification accuracy of 97.69% for the target disease. The developed algorithm can be deployed on edge devices for autonomous disease monitoring and management in the greenhouses.</li> </ul>

Publications

California <ul> <li>Villacrés, Vougioukas, S. G. (2024). Assessing a Multi-Camera System for Enhanced Fruit Visibility. Computers and Electronics in Agriculture - Special Issue on " AI-driven Agriculture". 224, 109164.</li> <li>Fu, K., Vougioukas, S. G., Bailey, B. (2024). Computer-aided design and optimization of a shake-catch fruit catching and retrieval soft fruit harvester. Computers and Electronics in Agriculture. Computers and Electronics in Agriculture, 225, 109334.</li> <li>Rui, Z. Zhang, M. Zhang, A. Azizi, C. Igathinathane, H. Cen, S. Vougioukas, H. Li, J. Zhang, Y. Jiang, X. Jiao, M. Wang, Y. Ampatzidis, O.I. Oladele, M. Ghasemi-Varnamkhasti, Radi Radi. (2024) High-throughput proximal ground crop phenotyping systems – A comprehensive review. Computers and Electronics in Agriculture, 224, 109108.</li> <li>Peng, C., Wei, P., Fei, Z., Zhu, Y. & Vougioukas, S.G. (2024) Optimization-based motion planning for autonomous agricultural vehicles turning in constrained headlands. Journal of Field Robotics, 1–25.</li> <li>Fei, Z., & Vougioukas, S. G. (2024). A robotic orchard platform increases harvest throughput by controlling worker vertical positioning and platform speed. Computers and Electronics in Agriculture, 218, 108735.</li> <li>van Henten, E., Montenegro, C., Popovic, M., Vougioukas, S. G., Daniel, A., & Han, G. (2023). Embracing Robotics and Intelligent Machine Systems for Smart Agricultural Applications [From the Guest Editors]. IEEE Robotics & Automation Magazine, 30(4), 8-112.</li> <li>Arikapudi R., Vougioukas, S.G. (2023). Robotic Tree-fruit Harvesting with Arrays of Cartesian Arms: A Study of Fruit Pick Cycle Times. Computers and Electronics in Agriculture (211), 108023.</li> <li>Araujo, G. D. M., Kouhanestani, F. K., & Fathallah, F. A. (2023). Ability of youth operators to reach agricultural all-terrain vehicles controls. Journal of safety research, 84, 353-363.</li> <li>Khorsandi, F., De Moura Araujo, G., & Fathallah, F. (2023). A systematic review of youth and all-terrain vehicles safety in agriculture. Journal of agromedicine, 28(2), 254-276.</li> <li>De Moura Araujo, G., Khorsandi Kouhanestani, F., & Fathallah, F. (2023). Forces required to operate controls on agricultural all-terrain vehicles: implications for youth. Ergonomics, 66(9), 1280-1294.</li> <li>Gibbs, J., Sheridan, C., Khorsandi, F., & Yoder, A. M. (2023). Emphasizing safe engineering design features of quad bikes in agricultural safety programs.121-127</li> <li>Sorensen, J. A., Milkovich, P. J., Khorsandi, F., Gorucu, S., Weichelt, B. P., Scott, E., & Johnson, A. (2024). Tractors, Trees, and Rollover Protective Structures: A Cause for Concern. Journal of Agromedicine, 29(2), 162-167.</li> <li>Araujo, G. D. M., Khorsandi, F., & Fathallah, F. A. (2024). Limitations in the field of vision of young operators of utility all-terrain vehicles. Journal of safety research, 88, 303-312.</li> <li>dos Santos, F. F. L., & Khorsandi, F. (2024). Riding into Danger: Predictive Modeling for ATV-Related Injuries and Seasonal Patterns. Forecasting, 6(2), 1-13.</li> <li>Khorsandi, F., Araujo, G. D. M., & dos Santos, F. F. L. (2024). AgroGuardian: An All-Terrain Vehicle Crash Detection and Notification System. Journal of Agricultural Safety and Health, 0. (In-press)</li> <li>Khorsandi, F., Araujo, G. D. M., & dos Santos, F. F. L. (2024). Artificial Intelligence-Driven All-Terrain Vehicle Crash Prediction and Prevention System. Journal of Agricultural Safety and Health, 0. (In-press)</li> <li>Khan, F. A., Khorsandi, F., Ali, M., Ghafoor, A., Raza Khan, R. A., Umair, M., ... & Hussain, Z. (2024). Spray drift reduction management in agriculture: A review. Progress in Agricultural Engineering Sciences. (In-press)</li> <li>Peanusaha, S., Pourreza, A., Kamiya, Y., Fidelibus, M., W., & Chakraborty, M. (2024). Nitrogen retrieval in grapevine (Vitis vinifera L.) leaves by hyperspectral sensing. Remote Sensing of Environment, 302, 113966.</li> <li>Farajpoor, P., Pourreza, A., & Fidelibus, M. W. (2024). Advancing Grapevine Nutrient Sensing through a CNN-Based Multi-Trait Analytical Approach. Presented at the 2024 ASABE Annual International Meeting.</li> <li>H Kamangir, BS Sams, N Dokoozlian, L Sanchez, JM Earles (2024). Large-scale spatio-temporal yield estimation via deep learning using satellite and management data fusion in vineyards. Computers and Electronics in Agriculture 216, 108439</li> <li>Ahmadi A., Kazemi M.H., Daccache A, Snyder R.(2024). SolarET: A generalizable machine learning approach to estimate reference evapotranspiration from solar radiation, Agricultural Water Management, Volume 295, 108779, ISSN 0378-3774.</li> <li>Arman Ahmadi, Andre Daccache, Mojtaba Sadegh, Richard L. Snyder (2023). Statistical and deep learning models for reference evapotranspiration time series forecasting: A comparison of accuracy, complexity, and data efficiency. Computers and Electronics in Agriculture, Volume 215, 108424.</li> <li>Anokye-Bempah, L., Styczynski, T., Teixeira Fernandes, N.A., Gervay-Hague, J., K., Ristenpart, W., & Donis-González, I.R. 2024. How Roast Profile Affects the Dynamics of Titratable Acidity during Coffee Roasting. Scientific Reports 14:8237. Doi: https://doi.org/10.1038/s41598-024-57256-y.</li> </ul> Florida <ul> <li>Kim, D.W., S.J. Jeong, W.S. Lee, H. Yun, Y.S., Chung, Y.-S. Kwon, and H.-J. Kim. 2023. Growth monitoring of field-grown onion and garlic by CIE L*a*b* color space and region-based crop segmentation of UAV RGB images. Precision Agric 24, 1982–2001. https://doi.org/10.1007/s11119-023-10026-8.</li> <li>Zhou, C., W. S. Lee, O. E. Liburd, I. Aygun, X. Zhou, A. Pourreza, J. K. Schueller, Y. Ampatzidis. 2023. Detecting two-spotted spider mites and predatory mites in strawberry using deep learning. Smart Agricultural Technology, Volume 4, 100229. https://doi.org/10.1016/j.atech.2023.100229.</li> <li>Kondaparthi AK, Lee WS, Peres NA. Utilizing High-Resolution Imaging and Artificial Intelligence for Accurate Leaf Wetness Detection for the Strawberry Advisory System (SAS). Sensors. 2024; 24(15):4836. https://doi.org/10.3390/s24154836.</li> <li>Congliang Zhou, Won Suk Lee, Shuhao Zhang, Oscar E. Liburd, Alireza Pourreza, John K. Schueller, Yiannis Ampatzidis. A smartphone application for site-specific pest management based on deep learning and spatial interpolation. Computers and Electronics in Agriculture, Volume 218, 2024, 108726, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2024.108726.</li> <li>imaging robot and deep learning. In the Proceedings of the 14th European Conference on Precision Agriculture (ECPA), July 2-6, 2023, Bologna, Italy.</li> <li>Lee, W. S. 2023. Strawberry plant wetness detection using color imaging and artificial intelligence for the Strawberry Advisory System (SAS). 2023 Annual Strawberry AgriTech Conference, Plant City, FL, May 17, 2023.</li> <li>Zhou, X., Y. Ampatzidis, W. S. Lee, S. Agehara, and J. K. Schueller. 2023. AI-based inspection system for mechanical strawberry harvesters. AI in Agriculture: Innovation and discovery to equitably meet producer needs and perceptions Conference, Orlando, FL, April 17-19, 2023.</li> <li>Zhou, C., W. S. Lee, W. Kratochvil, J. K. Schueller, and A. Pourreza. 2023. A portable imaging device for twospotted spider mite detection in strawberry. ASABE Annual Meeting, Omaha, NE, July 9-12, 2023.</li> <li>Zhou, C., W. S. Lee, N. Peres, B. S. Kim, J. H. Kim, and H. C. Moon. 2023. Strawberry flower and fruit detection based on an autonomous imaging robot and deep learning. 14th European Conference on Precision Agriculture, Bologna, Italy, July 2-6, 2023.</li> <li>Lee, W. S., T. Burks, and Y. Ampatzidis. 2023. Precision agriculture in Florida, USA – The Beginning, Progress, and Future. Chungnam National University, Daejeon-si, Korea. May 24, 2023.</li> <li>Lee, W. S., T. Burks, and Y. Ampatzidis. 2023. Precision agriculture in Florida, USA – The Beginning, Progress, and Future. Department of Agricultural Engineering, Division of Smart Farm Development, National Institute of Agricultural Sciences, Jeonju-si, Korea. May 25, 2023.</li> <li>Lee, W. S., T. Burks, and Y. Ampatzidis. 2023. Precision agriculture in Florida, USA – The Beginning, Progress, and Future. Seoul National University, Seoul, Korea. May 31, 2023.</li> <li>Lee, W. S., Y. Ampatzidis, and D. Choi. 2023. University of Florida 2023 W-3009 Report (presented via Zoom). Cornell AgriTech, Cornell University, Geneva, NY. June 20-21, 2023.</li> <li>Teshome F.T., Bayabil H.K., Schaffer B., Ampatzidis Y., Hoogenboom G., 2024. Improving soil moisture prediction with deep learning and machine learning Models. Computers and Electronics in Agriculture, 226, 109414, https://doi.org/10.1016/j.compag.2024.109414.</li> <li>Ojo I., Ampatzidis Y., Neto A.D.C., Guan H., Batuman O., 2024. Oxytetracycline injection using automated trunk injection compared to manual injection systems for HLB-affected citrus trees. Computers and Electronics in Agriculture, 226, 109430, https://doi.org/10.1016/j.compag.2024.109430.</li> <li>Trentin C., Ampatzidis Y., Lacerda C., Shiratsuchi L., 2024. Tree crop yield estimation and prediction using remote sensing and machine learning: A systematic review. Smart Agricultural Technology, 100556, https://doi.org/10.1016/j.atech.2024.100556.</li> <li>da Cunha V.A.G., Pullock D., Ali M., Neto A.D.C., Ampatzidis Y., Weldon C., Kruger K., Manrakhan A., Qureshi J., 2024. Psyllid Detector: a web-based application to automate insect detection utilizing image processing and artificial intelligence. Applied Engineering in Agriculture, 40(4), 427-438. https://doi.org/10.13031/aea.15826.</li> <li>Teshome F.T., Bayabil H.K., Schaffer B., Ampatzidis Y., Hoogenboom G., Singh A., 2024. Simulating soil hydrologic dynamics using crop growth and machine learning models. Computers and Electronics in Agriculture, 224, 109186, https://doi.org/10.1016/j.compag.2024.109186.</li> <li>Ojo I., Ampatzidis Y., Neto A.D.C., Bayabil K.H., Schueller K.J., Batuman O., 2024. Determination of needle penetration force and pump pressure for the development of an automated trunk injection system for HLB-affected citrus trees. Journal of ASABE, 67, 4, https://doi.org/10.13031/ja.15975.</li> <li>Rui Z., Zhang Z., Zhang M., Azizi A., Igathinathane C., Cen H., Vougioukas S., Li H., Zhang J., Jiang Y., Jiao X., Wang M., Ampatzidis Y., Oladele O.I., Ghasemi-Varnamkhasti M., Raid R., 2024. High-throughput proximal ground crop phenotyping systems – A comprehensive review. Computers and Electronics in Agriculture, 224, 109108, https://doi.org/10.1016/j.compag.2024.109108.</li> <li>Javidan S.M., Banakar A., Rahnama K., Vakilian K.A., Ampatzidis Y., 2024. Feature engineering to identify plant diseases using image processing and artificial intelligence: a comprehensive review. Smart Agricultural Technology, 8, 100480, https://doi.org/10.1016/j.atech.2024.100480.</li> <li>Barbosa Júnior M.D., Moreira B.R.A., Carreira V.S., Brito Filho A.L., Trentin C., Souza F.L.P., Tedesco D., Setiyono T., Flores J.P., Ampatzidis Y., Silva R.P., Shiratsuchi L.S., 2024. Precision Agriculture in the United States: A comprehensive meta-review inspiring further research, innovation, and adoption. Computers and Electronics in Agriculture, 221, 108993, https://doi.org/10.1016/j.compag.2024.108993.</li> <li>Ojo I., Ampatzidis Y., Neto A.D.C., Bayabil K.H., Schueller K.J., Batuman O., 2024. The development and evolution of trunk injection mechanisms – A review. Biosystems Engineering, 240, 123-141, https://doi.org/10.1016/j.biosystemseng.2024.03.002.</li> <li>Ojo I., Ampatzidis Y., Neto A.D.C., Batuman O., 2024. Development of an automated needle-based trunk injection system for HLB-affected citrus trees. Biosystems Engineering, 240, 90-99, https://doi.org/10.1016/j.biosystemseng.2024.03.003.</li> <li>Zhou C., Lee W.S., Zhang S., Liburd O.E., Pourreza A., Schueller J.K., Ampatzidis Y., 2024. A smartphone application for site-specific pest management based on deep learning and spatial interpolation. Computers and Electronics in Agriculture, 218, 108726, https://doi.org/10.1016/j.compag.2024.108726.</li> <li>Javidan S.M., Banakar A., Vakilian K.A., Ampatzidis Y., Rahnama K., 2024. Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning. Multimedia Tools and Applications, 1-19, https://doi.org/10.1007/s11042-024-18214-y.</li> <li>Zhang L., Ferguson L., Ying L., Lyons A., Laca E., and Ampatzidis Y., 2024. Developing a web-based pistachio nut growth prediction system for orchard management. HortTechnology, 34,1, 1-7, https://doi.org/10.21273/HORTTECH05270-23.</li> <li>Liu X., Zhang Z., Igathinathane C., Flores P., Zhang M., Li H., Han X., Ha T., Ampatzidis Y., Kim H-J., 2024. Infield corn kernel detection using image processing, machine learning, and deep learning methodologies. Expert Systems with Applications, 238 (part E), 122278, https://doi.org/10.1016/j.eswa.2023.122278.</li> <li>Mehdizadeh S.A., Noshad M., Chaharlangi M., Ampatzidis Y., 2023. Development of an innovative optoelectronic nose for detecting adulteration in quince seed oil. Foods, 12(23), 4350, https://doi.org/10.3390/foods12234350.</li> <li>Abdulridha J., Bashyal M., Ampatzidis Y., and Kanissery R., 2023. Steam application with paraquat to control goat weed (Scoparia dulcis) in citrus orchards. Smart Agricultural Technology, 6, 100355, https://doi.org/10.1016/j.atech.2023.100355.</li> <li>Vijayakumar V., Ampatzidis Y., Schueller J.K., Burks T., 2023. Smart spraying technologies for precision weed management: a review. Smart Agricultural Technology, 6, 100337, https://doi.org/10.1016/j.atech.2023.100337.</li> <li>da Cunha V.G., A. Hariharan J., Ampatzidis Y., Roberts P., 2023. Early detection of tomato bacterial spot disease in transplant tomato seedlings utilizing remote sensing and artificial intelligence. Biosystems Engineering, 234, 172-186, https://doi.org/10.1016/j.biosystemseng.2023.09.002.</li> <li>Teshome F.T., Bayabil H.K., Schaffer B., Ampatzidis Y., Hoogenboom G., Singh A., 2023. Exploring deficit irrigation as a water conservation strategy: Insights from field experiments and model simulation. Agricultural Water Management, 289, 108490, https://doi.org/10.1016/j.agwat.2023.108490.</li> <li>Teshome F.T., Bayabil H.K., Hoogenboom G., Schaffer B., Singh A., Ampatzidis Y., 2023. Unmanned aerial vehicle (UAV) imaging and machine learning applications for plant phenotyping. Computers and Electronics in Agriculture, 212, 108064, https://doi.org/10.1016/j.compag.2023.108064.</li> <li>Zhou C., Lee W.S., Liburd O.E., Aygun I., Zhou X., Pourreza A., Schueller J.K., Ampatzidis Y., 2023. Detecting two-spotted spider mites and predatory mites in strawberry using deep learning. Smart Agricultural Technology, 100229, https://doi.org/10.1016/j.atech.2023.100229.</li> <li>Javidan S.M., Banakar A., Vakilian K.A., Ampatzidis Y., 2023. Tomato leaf diseases classification using image processing and weighted ensemble learning. Agronomy Journal, http://doi.org/10.1002/agj2.21293.</li> <li>Hariharan J., Ampatzidis Y., Abdulridha J., Batuman O., 2023. An AI-based spectral data analysis process for recognizing unique plant biomarkers and disease features. Computers and Electronics in Agriculture, 204, 107574, https://doi.org/10.1016/j.compag.2022.107574.</li> <li>Momeny M., Neshat A.A., Jahanbakhshi A., Bakhtoor M.., Ampatzidis Y., Radeva P., 2023. Grading and fraud detection of Saffron via learning-to-augment incorporated inception-v4 CNN. Food Control, 109554, https://doi.org/10.1016/j.foodcont.2022.109554.</li> <li>Panta S., Zhou B., Zhu L., Maness N., Rohla C., Costa L., Ampatzidis Y., Fontainer C., Kaur A., Zhang, L., 2023. Selecting non-linear mixed effect model for growth and development of pecan nut. Scientia Horticulturae, 309, 111614, https://doi.org/10.1016/j.scienta.2022.111614.</li> <li>Poudyal C., Sandhu H., Ampatzidis Y., Odero D.C., Arbelo O.C., Cherry R.H., Costa L., 2023. Prediction of morho-physiological traits in sugarcane using aerial imagery and machine learning. Smart Agricultural Technology, 100104, https://doi.org/10.1016/j.atech.2022.100104.</li> <li>Vijayakumar V., Ampatzidis Y., Costa L., 2023. Tree-level Citrus Yield Prediction Utilizing Ground and Aerial Machine Vision and Machine Learning. Smart Agricultural Technology, 100077, https://doi.org/10.1016/j.atech.2022.100077.</li> <li>Javidan S.M., Banakar A., Vakilian K.A., Ampatzidis Y., 2023. Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning. Smart Agricultural Technology, 100081, https://doi.org/10.1016/j.atech.2022.100081.</li> <li>Dutt, N., & Choi, D. (2024). A Computer Vision System for Mushroom Detection and Maturity Estimation using Depth Images. 2024 ASABE Annual International Meeting.</li> <li>Ilodibe, U., & Choi, D. (2024). Evaluating The Performance of a Mite Dispensing System for Biological Control of Chilli Thrips in Strawberry Production in Florida. 2024 ASABE Annual International Meeting.</li> <li>Mirbod, O., & Choi, D. (2023). A Strategy for Rapid Development of Machine Vision Systems for Strawberry Farms Through Digital Twins and Synthetic Data, Computer and Electronics in Agriculture. Under Review.</li> <li>Mirbod, O., & Choi, D. (2023). Synthetic Data-Driven AI Using Mixture of Rendered and Real Imaging Data for Strawberry Yield Estimation. In 2023 ASABE Annual International Meeting. American Society of Agricultural and Biological Engineers.</li> <li>Mirbod, O., Choi, D., Heinemann, P. H., Marini, R. P., & He, L. (2023). On-tree apple fruit size estimation using stereo vision with deep learning-based occlusion handling. Biosystems Engineering, 226, 27-42.</li> <li>Mahmud, M. S., He, L., Zahid, A., Heinemann, P., Choi, D., Krawczyk, G., & Zhu, H. (2023). Detection and infected area segmentation of apple fire blight using image processing and deep transfer learning for site-specific management. Computers and Electronics in Agriculture, 209, 107862.</li> <li>Mahmud, M. S., He, L., Heinemann, P., Choi, D., & Zhu, H. (2023). Unmanned aerial vehicle based tree canopy characteristics measurement for precision spray applications. Smart Agricultural Technology, 4, 100153.</li> <li>Liu W., Ampatzidis Y., Wilkinson B., 2024. LiDAR-based point cloud classification and tree extraction for citrus crops. ASABE Annual International Meeting, Anaheim, California, USA, July 28-31, 2024.</li> <li>Cho Y., Yu Z., Ampatzidis Y., Wu S., Zhang C., 2024. Blockchain innovation for transparent forest carbon markets. ASABE Annual International Meeting, Anaheim, California, USA, July 28-31, 2024.</li> <li>Ojo I., Neto A.D.C., Ampatzidis Y., Batuman O., 2024. Needle-based, automated trunk injection system for HLB-affected citrus trees. ASABE Annual International Meeting, Anaheim, California, USA, July 28-31, 2024.</li> <li>Vijayakumar V., Neto A.D.C., Ampatzidis Y., 2024. A robotic precision smart sprayer based on machine vision and PI-controlled spraying system for specialty crops. ASABE Annual International Meeting, Anaheim, California, USA, July 28-31, 2024.</li> <li>Zhou C., Pullock D., Ampatzidis Y., Weldon C., Manrakhan A., 2024. Citrus pest detection using computer vision and deep. ASABE Annual International Meeting, Anaheim, California, USA, July 28-31, 2024.</li> <li>Ampatzidis Y., 2024. Can AI and automation transform specialty crop production? 16th International Conference on Precision Agriculture (ICPA), International Symposium on robotics and Automation, Manhattan, Kansas, USA, July 21-24.</li> <li>Zhou C. and Ampatzidis Y., 2024. AI-enabled 3D vision system for rapid and accurate tree trunk detection and diameter estimation. 16th International Conference on Precision Agriculture (ICPA), Manhattan, Kansas, USA, July 21-24.</li> <li>Zhou C., Ampatzidis Y., Guan H., and Neto A.D.C., Kunwar S., Batuman O., 2024. Agrosense: AI-enabled sensing for precision management of tree crops (poster). 16th International Conference on Precision Agriculture (ICPA), Manhattan, Kansas, USA, July 21-24.</li> <li>Banakar A., Javidan S.M., Vakilian K.A., Ampatzidis Y., 2024. Detection of spectral signature and classification of Alternaria alternata and Alternaria solani diseases in tomato plant by analysis of hyperspectral images and support vector machine. AgEng International Conference of EurAgEng, Agricultural Engineering Challenges in Existing and New Agrosystems, Athens, Greece, July 1-4, 2024.</li> <li>Lacerda C., and Neto A.D.C., Ampatzidis Y., 2024. Agroview: enhance satellite imagery using super-resolution and generative AI for precision management in specialty crops. AgEng International Conference of EurAgEng, Agricultural Engineering Challenges in Existing and New Agrosystems, Athens, Greece, July 1-4, 2024.</li> <li>Ampatzidis Y., Ojo I., Neto A.D.C., Batuman O., 2024. Automated needle-based trunk injection system for HLB-affected citrus trees. AgEng International Conference of EurAgEng, Agricultural Engineering Challenges in Existing and New Agrosystems, Athens, Greece, July 1-4, 2024.</li> <li>Ampatzidis Y., Vijayakumar V., Pardalos P., 2024. AI-enabled robotic spraying technology for precision weed management in specialty crops. Optimization, Analytics, and Decision in Big Data Era Conference (in honor of the 70th birthday of Dr. Panos Pardalos), Halkidiki, Greece, June 16-21.</li> <li>Cho Y., Yu, Z., Ampatzidis Y., Nam J., 2024. Blockchain-enhanced security and data management in smart agriculture. 6th CIGR International Conference, Jeju, Korea, May 19–23, 2024.</li> <li>Vijayakumar V., Ampatzidis Y., 2024. Development of a machine vision and spraying system of a robotic precision smart sprayer for specialty crops. 3rd Annual AI in Agriculture and Natural Resources Conference, College Station, TX, April 15-17, 2024.</li> <li>Zhou C., Ampatzidis Y., Pullock D., 2024. Detecting citrus pests from sticky traps using deep learning. 3rd Annual AI in Agriculture and Natural Resources Conference, College Station, TX, April 15-17, 2024.</li> <li>Trentin C., Lacerda C.M.F., Shiratsuchi L., Ampatzidis Y., 2024. AI in orchard: improving sustainability through predictive yield in trees. 3rd Annual AI in Agriculture and Natural Resources Conference, College Station, TX, April 15-17, 2024.</li> <li>Cho Y., Yu, Z., Ampatzidis Y., 2024. Blockchain-Enhanced Data Management in AI-Driven Agriculture: A Pathway to Efficiency and Transparency. 3rd Annual AI in Agriculture and Natural Resources Conference, College Station, TX, April 15-17, 2024.</li> <li>Tulu B.B., Bayabil H.K., Ampatzidis Y., 2024. Enhancing agricultural water management: A desktop application integrating UAV imagery and ground sensing for precision irrigation. 3rd Annual AI in Agriculture and Natural Resources Conference, College Station, TX, April 15-17, 2024.</li> <li>Ojo I., Neto A.D.C., Ampatzidis Y., Batuman O., Albrecht U., 2024. Needle-based, automated trunk injection system for HLB-affected citrus trees. International Research Conference on Huanglongbing VII, Riverside, CA, March 26-29, 2024.</li> <li>Tulu B.B., Bayabil H.K., Ampatzidis Y., 2024. IrrigSense: A decision support tool to streamline precision irrigation. UF/ABE Poster Competition, Gainesville, FL, March 6, 2024.</li> <li>Javidan S.M., Ampatzidis Y., Vakilian K.A., Mohammadzamani D., 2024. A novel approach for automated strawberry fruit varieties classification using image processing and machine learning. 10th International Conference on Artificial Intelligence and Robotics, IEEE-QICAR2024 Qazvin Islamic Azad University, February 29, 2024, https://doi.org/10.1109/QICAR61538.2024.10496652</li> <li>Tulu B.B., Bayabil H.K., Ampatzidis Y., 2024. Streamlining precision irrigation: Developing a web-based decision support tool for sensor data processing. 9th biennial UF Water Institute Symposium, Gainesville, FL, February 20-21, 2024.</li> <li>Ampatzidis Y., 2024. Agroview and Agrosense for AI-enhanced precision orchard management. SE Regional Fruit and Vegetable Conference, Savannah, GA, January 11-14, 2024</li> <li>Ampatzidis Y., 2023. Emerging and advanced technologies in agriculture. Link (Linking Industry Networks through Certifications; High School Teachers Training) Conference, Daytona Beach, FL, October 10-12, 2023.</li> <li>Ampatzidis Y., 2023. AI and Extension. Possibilities and Challenges. 2023 SR-PLN Middle Managers Conference, Next Generation: Evolving the Extension Enterprise, Orlando, FL, August 22-24.</li> <li>Ampatzidis Y., 2023. AI-Enhanced Technologies for Precision Management of Specialty Crops. Sustainable Precision Agriculture in the Era of IoT and Artificial Intelligence, Bard Ag-AI Workshop, Be’er Sheva, Israel, July 18-20, 2023.</li> <li>Ojo I., de Oliveira Costa Neto A., Ampatzidis Y., 2023. Automated Injection System for Therapeutic Materials Using Nonpassive, Needle-Based Trunk Injection to Treat HLB-affected Citrus Trees. ASABE Annual International Meeting, Omaha, Nebraska, USA, July 8-12, 2023.</li> <li>Vijayakumar V., Ampatzidis Y., Silwal A., Kantor G., 2023. 2023. Development of a machine vision and spraying system of an autonomous robotic sprayer for specialty crops. ASABE Annual International Meeting, Omaha, Nebraska, USA, July 8-12, 2023.</li> <li>Kunwar S., Babar M. A., Ampatzidis Y., Mcbreen J., Khan N., Acharya J., Adewale S., Costa L., and Cunha V., 2023. Determining yield, harvest index and associated complex biomass partitioning traits in wheat using UAV-based hyperspectral sensor and machine learning. Annual Meeting of the Western Crop Science Society (WCSSA), Honolulu, Hawaii, June 26-28, 2023.</li> <li>Ampatzidis Y., 2023. Solutions to critical issues facing field and specialty crop production. Integrative Precision Agriculture – Local Solutions Through Global Advances International Conference, Athens, Georgia, May 18-19, 2023.</li> <li>Hariharan J., Ampatzidis Y., Abdulridha J., Batuman O., 2023. An AI-Based Spectral Data Analysis Process for Recognizing Unique Plant Biomarkers and Disease Features. AI in Agriculture Conference: Innovation and Discovery to Equitably meet Producer Needs and Perceptions, Orlando, FL, April 17-19, 2023.</li> <li>Kunwar S., Babar Md. A., Ampatzidis Y., 2023. Potential use of UAV-based remote sensing tools for indirect assessment of harvest index and associated complex biomass partitioning traits in wheat. AI in Agriculture Conference: Innovation and Discovery to Equitably meet Producer Needs and Perceptions, Orlando, FL, April 17-19, 2023.</li> <li>Vijayakumar V., Ampatzidis Y., Silwal A., Kantor G., 2023. Specialty crop-specific robotic precision smart sprayer based on machine vision and PWM-controlled spraying system. AI in Agriculture Conference: Innovation and Discovery to Equitably meet Producer Needs and Perceptions, Orlando, FL, April 17-19, 2023.</li> <li>Costa L. and Ampatzidis Y., 2023. Building reliability: development of a prototype to production for a smart citrus tree sprayer using sensor fusion and artificial intelligence. AI in Agriculture Conference: Innovation and Discovery to Equitably meet Producer Needs and Perceptions, Orlando, FL, April 17-19, 2023.</li> <li>Zhou X., Ampatzidis Y., Lee W.S., Agehara S., Schueller J.K., Crane C., 2023. AI-based Inspection System for Mechanical Strawberry Harvesters. AI in Agriculture Conference: Innovation and Discovery to Equitably meet Producer Needs and Perceptions, Orlando, FL, April 17-19, 2023.</li> <li>Lacerda C., Costa L, Ampatzidis Y., 2023. The process of optimizing a cloud based software infrastructure: Agroview, a case of study. AI in Agriculture Conference: Innovation and Discovery to Equitably meet Producer Needs and Perceptions, Orlando, FL, April 17-19, 2023.</li> <li>Andrade Gontijo da Cunha V., de Oliveira Costa Neto A., Costa L., Ampatzidis Y., 2023. ACP Detection System on Sticky Traps Images Utilizing Artificial Intelligence. AI in Agriculture Conference: Innovation and Discovery to Equitably meet Producer Needs and Perceptions, Orlando, FL, April 17-19, 2023.</li> <li>Liu W. and Ampatzidis Y., 2023. Mapping citrus orchards utilizing aerial imagery with Agroview and Lidar. AI in Agriculture Conference: Innovation and Discovery to Equitably meet Producer Needs and Perceptions, Orlando, FL, April 17-19, 2023.</li> <li>Ojo I., Lucas F., de Oliveira Costa Neto A., Ampatzidis Y., 2023. AI-based Operator-assisted Positioning of Automated Trunk Injection Mechanism using Sensor Fusion. AI in Agriculture Conference: Innovation and Discovery to Equitably meet Producer Needs and Perceptions, Orlando, FL, April 17-19, 2023.</li> <li>Liu W., Costa L., Ampatzidis Y., 2023. Precisely Mapping Citrus Orchards utilizing UAV-based LiDAR and Imagery with a Cloud-based AI system Agroview. ABE/UF Poster Competition. Gainesville, FL, March 9, 2023.</li> <li>Ampatzidis Y., 2023. “Agrifood systems in a Circular Economy Framework: Unlocking the Future”. 11th Agrotechnology Conference, American Hellenic Chamber of Commerce, Thessaloniki, Greece, February 17, 2023.</li> <li>Kunwar S., Babar M. A., and Ampatzidis Y., 2023. Potential use of UAV-based remote sensing tools for indirect assessment of harvest index, biomass partitioning dynamics and associated complex traits in wheat. PSC Symposium (poster), January 30-31, 2023.</li> <li>Ampatzidis Y., 2023. AI-enhanced smart machinery for precision scouting and spraying. Annual Meeting and Ag Expo of the National Alliance of Independent Crop Consultants (NAICC), Nashville, TN, January 23-27, 2023.</li> <li>Hou, J., X. Wang, B. Park. C. Li. 2024. A multiscale computation study on bruise susceptibility of blueberries from mechanical impact. Postharvest Biology and Technology. 208: 112660.</li> <li>Li, Z., Li, C., & Munoz, P. Blueberry yield estimation through multi-view imagery with YOLOv8 object detection. ASABE Annual International Meeting Paper No 2300883. Omaha, Nebraska July 9-12, 2023.</li> <li>Perkins-Veazie, P., C. Li, H. Oh, M. Iorizzio. Fruit bruising, firmness, and estimation of cell membrane damage across blueberry genotypes. ASHS Conference. July 31-August 4, 2023, Orlando, Florida.</li> <li>Uthman, Q.O.G, D.M. Kadyampakeni, J.A. Leiva, J.D. Judy and P. Nkedi-Kizza. 2024. Sorption and</li> <li>degradation processes of imidacloprid in Florida soils. PLoS ONE 19(9): e0305006. https://doi.org/10.1371/journal.pone.0305006</li> <li>Chuang, A.p, D. Kadyampakeni, T. Liesenfelt, C. Vincent, M. Dewdney, and L.</li> <li>2024. Comparison of tools to support healthy young citrus plantings in a region with endemic huanglongbing, CLas, and Asian citrus psyllid (Diaphorina citri). Crop Protection, https://doi.org/10.1016/j.cropro.2024.106871</li> <li>Donovan, M.P, C. Chanes, D. Gholson, D.M. Kadyampakeni, M.E. Swisher, and T. Connor</li> <li>Managing agricultural water resources in the southern region: perspectives of crop growers. Water 16(13), 1841; https://doi.org/10.3390/w16131841</li> <li>Atta, A.A.P, K.T. Morgan, S. Hamido, and D.M. Kadyampakeni. 2024. Irrigation optimization enhances water management and tree performance in commercial citrus groves on sandy soil. Irri. Sci. https://doi.org/10.1007/s00271-024-00938-2</li> <li>Agyin-Birikorang, S., D.M. Kadyampakeni, A.R.A. Fuseini; R. Chambers, I. Tindjina, and R. Adu-</li> <li>2024. Sulfur availability minimizes nitrate leaching losses in vulnerable agricultural soils. J. Plant Nut. 47(15):2389-2405, https://doi.org/10.1080/01904167.2024.2354171</li> <li>Brewer, M.P, D.M. Kadyampakeni, R. Kanissery, and S. KwakyeP. 2024. Evaluation of the nitrogen uptake efficacy of daikon radish under greenhouse conditions on sandy soils. Agrosyst. Geosci. Environ., https://doi.org/10.1002/agg2.20508</li> <li>Uthman, Q.O.G, M. Vasconez, D.M. Kadyampakeni, Y. Wang, D. Athienitis, and J.A. Qureshi. 2024. Imidacloprid uptake and leaching in the critical root zone of a Florida Entisol. Agrochem. 3:94–106. https://doi.org/10.3390/agrochemicals3010008</li> <li>Agyin-Birikorang, S., C. Boubakry, D.M. Kadyampakeni, R. Adu-Gyamfi, R.A. Chambers, I. Tindjina, and A.R.A. Fuseni. 2024. Synergism of sulfur availability and agronomic nitrogen use efficiency. Agron. J. 116(2):753-764. https://doi.org/10.1002/agj2.21535</li> <li>Chinyukwi, T.G, D.M. Kadyampakeni, and L. Rossi. 2024. Optimization of macronutrient and micronutrient concentrations in roots and leaves for Florida HLB-affected sweet orange trees. J. Plant Nutr. 47(2):226–239. https://doi.org/10.1080/01904167.2023.2275068</li> <li>Agyin-Birikorang, S., R. Adu-Gyamfi, D.M. Kadyampakeni, R.A. Chambers, I. Tindjina, and H.W. Dauda. 2024. Lime Microdosing: A new liming strategy for increased productivity in acid soils. Soil Sci. Soc. Am. J. 88(1):136-151, https://doi.org/10.1002/saj2.20610</li> <li>Fenn, R.A.G, D.M. Kadyampakeni, R. Kanissery and J. Judy. 2024. Citrus phosphorus uptake dynamics with glyphosate under greenhouse conditions. J. Plant Nutr. 47(5):776-785. https://doi.org/10.1080/01904167.2023.2280154</li> <li>Timilsina, N.g, O. Batuman, F. Alferez, D. Kadyampakeni, R. Tiwari and R. Kanissery, 2023. Nontarget effects of preemergence herbicide Diuron in Hamlin and Valencia sweet orange (Citrus sinensis L. Osbek) in Florida. HortSci. 58(12):1492–1497. https://doi.org/10.21273/HORTSCI17359-23</li> <li>Brewer, M.G, R.G. Kanissery, S.L. Strauss, and D.M. Kadyampakeni. 2023. Impact of cover cropping on temporal nutrient distribution and availability in the soil. Horticult. 2023, 9, 1160. https://doi.org/10.3390/horticulturae9101160</li> <li>Kadyampakeni, D.M., T. ChinyukwiG, S. KwakyeP and L. Rossi. 2023. Varied macro- and micronutrient fertilization rates impact root growth and distribution and fruit yield of huanglongbing-affected Valencia orange trees. HortSci. 58(12):1498–1507. https://doi.org/10.21273/HORTSCI17372-23</li> <li>Fenn, R.A.G, D.M. Kadyampakeni, R.G. Kanissery, J. Judy, and M. Bashyalp. 2023. Phosphorus and glyphosate adsorption and desorption trends across different depths in sandy soil. Agrochem. 2:503–516. https://doi.org/10.3390/agrochemicals2040028</li> <li>Hallman, L.M.g, D.M. Kadyampakeni, R.S. Ferrarezi, A.L. Wright, M.A. Ritenour and L. Rossi. 2023. Uptake of micronutrients in severely HLB-affected grapefruit trees grown on Florida Indian River flatwood soils. J. Plant Nutr. 46(17):4110–4124. https://doi.org/10.1080/01904167.2023.2221287</li> <li>Ghoveisi, H.P, D.M. Kadyampakeni, J. Qureshi, and L. Diepenbrock. 2023. Water use efficiency in young citrus trees on metalized UV reflective mulch compared to bare ground. Water 15, 2098. https://doi.org/10.3390/w15112098</li> <li>Kwakye, S.P and D.M. Kadyampakeni. 2023. Impact of deficit irrigation on growth and water relations of HLB-affected citrus trees under greenhouse conditions. Water 15, 2085. https://doi.org/10.3390/w15112085</li> <li>Hussain, M.P, S. Iqbal, M. Shafiq, R.M. Balal, J. Chater, D. Kadyampakeni, F. Alferez, A. Sarkhosh and M.A. Shahid. 2023. Silicon-induced hypoxia tolerance in citrus rootstocks associated with modulation in polyamine metabolism. Sci. Hort. 318:112118, https://doi.org/10.1016/j.scienta.2023.112118</li> <li>Atta, A.A.P, K.T. Morgan M.A. Ritenour and D.M. Kadyampakeni. 2023. Nutrient management impacts on HLB-affected ‘Valencia’ citrus tree growth, fruit yield, and postharvest fruit quality. Hortsci. 58(7):725–732. https://doi.org/10.21273/HORTSCI17110-23  </li> <li>Santiago, J.M.g, D.M. Kadyampakeni, J.P. Fox, A.L. Wright, S.M. Guzmán, R.S. Ferrarezi, and L. Rossi. 2023. Grapefruit root and rhizosphere responses to varying planting densities, fertilizer concentrations and application methods. Plants 12, 1659. https://doi.org/10.3390/plants12081659</li> </ul> Massachusetts <ul> <li>Higgins, G. 2023. Winter High Tunnel Spinach Variety Trial Results, 2022-23. Vegetable Notes. August 17, 2023. Vol. 35:18</li> <li>Higgins, G. 2024. Improving Production & Yield of Winter Spinach in the Northeast. Vegetable Notes. January 2024. Vegetable Notes 2024 Vol. 36:1</li> <li>Higgins, G. 2023. Improving Germination and Stand in Winter High Tunnel Spinach. Vegetable Notes. September 14, 2023. Vegetable Notes 2023 Vol. 35:21</li> </ul> Michigan <ul> <li>Xu, J., Lu, Y., 2024. Prototyping and evaluation of a novel machine vision system for real-time, automated quality grading of sweetpotatoes. Computers and Electronics in Agriculture 219, 108826.</li> <li>Xu, J., Lu, Y., 2024. Design and preliminary evaluation of automated sweetpotato sorting mechanisms. AgriEngineering 6 (3), 3058-3069.</li> <li>Ahmed, T., Wijewardane, N., Lu, Y., Jones, D., Kudenov, M., Williams, C., Villordon, A., Kamruzzaman, M., 2024. Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence. Computers and Electronics in Agriculture 220, 108855.</li> <li>Deng, B., Lu, Y., Xu, J., 2024. Weed database development: An updated survey of public weed datasets and cross-season weed detection adaptation. Ecological Informatics, 102546.</li> <li>Xu, J., Lu, Y., Deng, B., 2024. OpenWeedGUI: an open-source graphical tool for weed Imaging and YOLO-based weed detection. Electronics 13 (9), 1699.</li> <li>Deng, B., Lu, Y., Stafne, E., 2024. Fusing spectral and spatial features of hyperspectral reflectance imagery for differentiating between normal and defective blueberries. Smart Agricultural Technology, 100473.</li> <li>Li, J., Lu, Y., Lu, R., 2024. Identification of early decayed oranges using structured-illumination reflectance imaging coupled with fast demodulation and improved image processing algorithms. Postharvest Biology and Technology 207, 112627.</li> <li>Dong, Y., Werling, B., Cao, Z., Li, G., (2024). Implementation of an In-Field IoT Systems for Precision Irrigation Management. Frontiers in Water. 6, 1353597.</li> <li>Dong, Y., Hansen, H., (2024). Design of an Internet of Things (IoT)-Based Photosynthetically Active Radiation (PAR) Monitoring System. AgriEngineering. 6 (1), 773-785.</li> <li>Dong, Y., Sloan, G., Chappuies, J., (2024). Open-source time-lapse thermal imaging camera for canopy temperature monitoring. Smart Agricultural Technology. 100430.</li> <li>Mane, S., Das, N., Singh, G., Cos, M., Dong, Y., (2024). Advancements in dielectric soil moisture sensor Calibration: A comprehensive review of methods and techniques. Computers and Electronics in Agriculture. 218, 108686.</li> <li>Xu, J., Lu, Y., Deng, B., 2024. Design, prototyping, and evaluation of a machine vision-based automated sweetpotato grading and sorting System. ASABE Annual International Meeting Paper 2400102.</li> <li>Deng, B., Lu, Y., Brainard, D., 2024. Development and Preliminary Evaluation of a Vision-Guided Smart Sprayer Prototype towards Precision Vegetable Weeding. ASABE Annual International Meeting Paper 2400089.</li> <li>Deng, B., Lu, VanderWeide, J., 2024. Development and preliminary evaluation of a deep learning-based fruit counting mobile application for high-bush blueberries. ASABE Annual International Meeting Paper 2401022.</li> <li>Lu, Y., Mohammadi, P., Xu, J., 2024. Automated asparagus harvesting technology: a review of the past 60 years of research and developments in the United States and beyond. ASABE Annual International Meeting Paper 2401062.</li> <li>Xu, J., Lu, Y, 2024. Design and preliminary evaluation of a machine vision-based automated sweetpotato sorting system. Sensing for Agriculture and Food Quality and Safety XVI Proceedings Volume PC13060.</li> <li>Deng, B., Lu, Y., 2024. Weed image augmentation by controlNet-added stable diffusion. SPIE Defense + Commercial Sensing, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II</li> </ul> Missouri <ul> <li>Rifat, S. M., Zhou, J., & Thomas, A. 2024. The Effects of Shaking Frequency and Amplitude on Vibratory Harvesting of American Elderberry. In 2024 ASABE Annual International Meeting. American Society of Agricultural and Biological Engineers.</li> </ul> Mississippi <ul> <li>Karkee, M.*, Zhang, Q., Bhattarai, U., & Zhang, X. (2024). Chapter 19 – Advances in the use of robotics in orchard operations. In Advances in Agri-Food Robotics (van Henten, E., & Edan, Y. ed.), Springer Book Series: Agriculture Automation and Control. (http://dx.doi.org/10.19103/AS.2023.0124.23)</li> <li>Zhang, X.* (2023). Robotics and Automation Technologies: Plant-machine interface. In Encyclopedia of Smart Agriculture Technologies (Zhang, Q. ed.), Springer. (https://doi.org/10.1007/978-3-030-89123-7_124-1)</li> <li>Zhang, X.*, Thayananthan, T., Usman, M., Liu, W., & Chen, Y. (2023, June). Multi-ripeness level blackberry detection using YOLOv7 for soft robotic harvesting. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII (Vol. 12539, pp. 85-96). SPIE. (https://doi.org/10.1117/12.2663367)</li> <li>He, L.*, Zhang, X., & Zahid, A. (2023). Chapter 2 – Mechanical management of modern planar fruit tree canopies. In Advanced Automation for Tree Fruit Orchards and Vineyards (Vougioukas, S. G., & Zhang, Q. ed.), Springer Book Series: Agriculture Automation and Control. (https://doi.org/10.1007/978-3-031-26941-7_2)</li> <li>Chakraborty, M., Pourreza, A.*, Zhang, X., Jafarbiglu, H., Shackel, K. A., & DeJong, T. (2023). Early almond yield forecasting by bloom mapping using aerial imagery and deep learning. Computers and Electronics in Agriculture, 212, 108063. (https://doi.org/10.1016/j.compag.2023.108063)</li> <li>He, Z., Khanal, S. R., Zhang, X., Karkee, M.*, & Zhang, Q. (2023). Real-time strawberry detection based on improved YOLOv5s architecture for robotic harvesting in open-field environment. arXiv. (https://doi.org/10.48550/arXiv.2308.03998)</li> <li>Azizkhani, M., Gunderman, A. L., Qiu, A. S., Hu, A. P., Zhang, X., & Chen, Y.* (2023). Design, modeling, and redundancy resolution of soft robot for effective harvesting. arXiv. (https://doi.org/10.48550/arXiv.2303.08947)</li> <li>Divyanth, L. G., Rathore, D., Senthilkumar, P., Patidar, P., Zhang, X., Karkee, M., Machavaram, R., & Soni, P.* (2023). Estimating depth from RGB images using deep-learning for robotic applications in apple orchards. Smart Agricultural Technology, 6, 100345. (<a href="https://doi.org/10.1016/j.atech.2023.100345">https://doi.org/10.1016/j.atech.2023.100345</a>)</li> </ul> Pennsylvania <ul> <li>Mu, X., Hussain, M., He, L., Heinemann, P., Schupp, J., Karkee, M., & Zhu, M. (2023). An advanced robotic system for precision chemical thinning of apple blossoms. Journal of the ASABE 66(5).</li> <li>Hua, W., He, L., Heinemann, P., & Yuan, W. (2023). CFD simulation of porous canopy heat transfer in apple orchard frost protection. Journal of the ASABE 66(4), 825-837.</li> <li>Kang, C., He, L., & Zhu, H. (2024). Assessment of spray patterns and efficiency of an unmanned sprayer used in planar growing systems. Precision Agriculture, July, 2024.</li> <li>Hua, W., Heinemann, P.H., & He, L. (2024). Canopy protection cyber-physical system (CPCPS) for smart agricultural management of frost damage in apple orchards. Computers and Electronics in Agriculture, 217, 108611.</li> <li>Mao, W., Murengami, B., Jiang, H., Li, R., He, L., & Fu, L. (2024). UAV-Based High-Throughput Phenotyping to Segment Individual Apple Tree Row Based on Geometrical Features of Poles and Colored Point Cloud. Journal of the ASABE, 67(5), 1231-1240.</li> <li>He, L., Han, K., & Peter, K. (2023). Smartphone-Assisted Apple Diseases Identification and Quantification using Artificial Intelligence. Pennsylvania Fruit News. February 2024.</li> <li>Arthur, L., He, L., & Brunharo, C. (2024). An Overview of Advanced Weed Management Technologies for Orchards. Penn State Extension.</li> <li>Kang, C. & He, L. (2024). Introduce and Evaluate an Unmanned Ground Sprayer for Vineyards and Orchards. Penn Stage Extension.</li> <li>Magni Hussain (2023). Robotic Green Fruit Thinning for Apple Production. PhD Dissertation. The Pennsylvania State University.</li> <li>Xinyang Mu (2023). Advanced Robotic Approaches to Precision Apple Crop Load Management in Blossom Thinning Stage. PhD Dissertation. The Pennsylvania State University.</li> <li>Weiyun Hua (2024). Development of A Precision Heating Strategy for Smart Frost Management in Apple Orchards. PhD Dissertation. The Pennsylvania State University.</li> <li>Juan Arguijo (2024). Vision System for the Detection of Apples in the Green Fruit Stage. MS Thesis. The Pennsylvania State University.</li> </ul> Texas <ul> <li>Majeed, Y., Ojo, M. O., and Zahid, A. 2024. Standalone edge AI-based solution for Tomato diseases detection, Smart Agricultural Technology, 100547</li> <li>Ojo, M. O., Zahid, A., and Masabni, J. 2024. Estimating hydroponic lettuce phenotypic parameters for efficient resource allocation, Computers and Electronics in Agriculture, 218, 108642</li> <li>Ahamed M. S., Sultan, M., Monfet, D., Rahman, M.S., Zhang, Y., Zahid, A., Aleem, M., Achour, Y., and Ahsan, T. M. A. 2023. Thermal environment controls and sustainability challenges in indoor vertical farming, Journal of Cleaner Production, 425, 138923</li> <li>Bashir, A., Majeed, Y., and Zahid, A. 2024. Development of an End-effector for Robotic Harvesting of Hydroponic Lettuce, In 2024 ASABE Annual International Meeting, Paper Number: 2400401</li> </ul>

Impact Statements

California - The strawberry yield maps were correlated with remote sensing data. The results illustrate the potential use of variable fumigation rates to decrease the quantity applied and the associated costs of fumigation without compromising the yield of strawberries and variable fumigation rates for the $3 Bn farm value industry
California - The almond yield maps can be used as decision-aiding tools by the growers to implement precise nutrition management for the $3.5 Bn farm value industry.
California - Hosted and co-hosted five events focused on applying engineering controls to improve worker safety and health.
California - Delivered 28 presentations on worker safety and health to various stakeholders, including farmers, industry professionals, academia, and government representatives.
California - This model allows for precise, site-specific fertilizer applications, reducing over-fertilization and environmental impacts. It optimizes vineyard management, leading to cost savings, improved crop quality, and potential applications in other specialty crops.
California - Worked with large commercial grape producer to test and validate on very large yield ground-truth dataset (~4-5 million grapevine plants over 4-year time period).
California - Using solar radiation as the sole input for estimating ETo reduced the need for multi-sensor weather stations and maintenance of green areas, leading to significant cost savings and alleviating the demand for dense weather station networks.
California - Demonstrated that advanced machine learning techniques can significantly enhance ETo forecasting accuracy for up to six months, helping growers, water managers, and policymakers manage irrigation and water resources effectively.
California - The impact of roast profile on the dynamics of TA during coffee roasting is a significant area of study within the coffee industry. Roasters can influence the development of titratable acidity in coffee beans by manipulating the roast profile, ultimately affecting the flavor profile of the final product. Understanding how different roast profiles impact the TA dynamics is crucial for achieving consistency and quality in coffee. Our research provides valuable insights for coffee roasters in California and the United States.
Connecticut - The results from this project will generate a new capability to monitor insect stress on agricultural crops using drone and spectral sensor technology. Drones outfitted with imaging sensors can provide digital scouting for early detection of crop infestation, quick mapping of pest location and quality data for pest management decisions. The anticipated impact will be a significant increase in knowledge for growers and IPM researchers with many potential applications in potato leafhopper IPM and for detection of pests in other crops. This project has enabled cross-disciplinary training for a graduate student at the PhD level. The student is obtaining graduate training in entomology, integrated pest management, remote sensing and spectroscopy.
Florida - Mushroom System: This project reduces manual labor dependency in mushroom farming, increases efficiency, and extends the shelf life of harvested mushrooms. The system has the potential to transform commercial mushroom harvesting through automation.
Florida - Mite Dispensing System: The system offers an effective, environmentally sustainable alternative to chemical pesticides, promoting the adoption of biological pest management strategies.
Florida - Strawberry Yield Estimation: The introduction of synthetic data in strawberry yield estimation accelerates the development of precision agriculture technologies, reducing data collection costs while improving yield prediction accuracy.
Florida - Blueberry phenotyping system: The system enables the measurement of detailed plant-level and cluster-level fruit traits for each individual plant/plot with high throughput and autonomous robot data collection. The system could help accelerate precision breeding for blueberries with higher yield and better traits for mechanical/robotic-harvestability.
Massachusetts - During the reporting period we reached 210 growers and ag service providers directly, increasing knowledge of spinach production and disease management. Growers who attend our workshops on average increase knowledge by 32%, increase confidence by 96%, and are 88% likely to adopt a new practice they learned about on their farms.
Massachusetts - We also increased knowledge through publication of three new articles on spinach varieties with resistance to DM, improving spinach germination, and spinach production efficiency and improving spinach profitability which reached 3,080 individuals.
Michigan - Developed sensing and automation systems to reduce labor dependence and improve efficiency of specialty crop production and postharvest processes.
Michigan - To improve sweet potato grading and sorting accuracy to 95% or higher by implementing advanced optical technology and optimized computer algorithms. To improve weed recognition reliability in real vegetable field conditions and weed targeting accuracy. To develop next-generation in-field apple technology and develop new selective asparagus harvesting technology
Missouri - Reached out more than 100 farmers and provided education program about mechanical and automation technology to improve harvest efficiency and save labor
Missouri - Conducted professional presentations to technical community
Mississippi - The research of “Cotton Moisture Sensor for Realtime Application” is sponsored by the USDA Agricultural Research Service (USDA-ARS) under reference # 58-6066-9-038. PI: Filip To.
Mississippi - The research of “Robotic Cotton Picking” is sponsored by the Cotton Incorporated under the Grant No. 23-889. Project title: Enabling a Boll Orientation-Aware Cotton-Picking Robot in the Fields. PI: Xin Zhang.
Mississippi - The research of “Robotic Fresh Market Caneberry Harvesting” is sponsored by the USDA NIFA under the Grant No. 2024-67021-41439. Project title: Collaborative Research: NRI: Perception-Aware Soft Robot Manipulation and Bipedal Locomotion for Fresh Market Caneberry Harvesting. PI: Xin Zhang.
Mississippi - Presentations: (1) Zhang, X., Thayananthan, T., Usman, M., Liu, W., & Chen, Y. Multi-ripeness level blackberry detection using YOLOv7 for soft robotic harvesting. SPIE DCS210, Orlando, FL (4/30/2023–5/4/2023); (2) Zhang, X. Autonomous Systems Symposium: Inspiring Innovation across Land, Sea, and Air: “Robotic harvesting systems for cotton and fresh market blackberries”. MSU Office of Research and Economic Development (ORED), Starkville, MS (11/14/2023)
Mississippi - Media reports: (1) MSU MAFES Discovers: "Berry Picking Bots: MSU scientists have an “eye” on blackberry harvesting" (5/2024) (https://www.mafes.msstate.edu/discovers/article.php?id=322); (2) MSU Newsroom/Starkville Daily News: "MSU scientists use $1M grant to ‘get a grip’ on automated blackberry harvesting" (8/2024) (https://www.msstate.edu/newsroom/article/2024/08/msu-scientists-use-1m-grant-get-grip-automated-blackberry-harvesting)
Mississippi - Others: (1) Thevathayarajh Thayananthan, a Ph.D. student at MSU, won the SPIE “Autonomous Air and Ground Sensing Best Student Paper Award”; (2) Blackberry commercial farm owners from California and Mexico reached out for further collaborations, validating the importance of our work.
Pennsylvania - A series studies were conducted on precision and robotic crop load management at Penn State, advanced machine vision algorithms, AI models, robotic manipulators, and integrated systems were developed and tested in orchard environments. These studies provided solid foundation for developing automated crop load management systems for tree fruit crops.
Pennsylvania - With the research outcomes from precision chemical thinning and robotic spraying, two USDA-NIFA projects were newly funded, and we will develop field scale robotic systems for precision chemical blossom thinning and disease management for various fruit crops.
Pennsylvania - With the research outcome from the spectral vision system to detect mushroom disease, the PDA project was funded to develop the treatment robot.
Pennsylvania - We organized the first precision agricultural technology field day on June 6 at Penn State Fruit Research and Extension Center (FREC). In total, 30 growers and other stakeholders attended the field day. The workshop was well received, and valuable comments were obtained from some participating growers.
Pennsylvania - In-field automated imaging traps will allow for rapid monitoring of invasive insects, which will be particularly useful for nurseries to make control decisions and for monitoring agencies (such as state and federal ag departments) to track the spread of invasives and establish quarantines.
Tennessee - The projects may reduce the labor and financial costs of pollination processes for various fruits and vegetables, improve the production of fruits and vegetables and farmers’ well-being, and ultimately ensure sustainable food production systems.
Tennessee - The projects may mitigate the labor shortage issues in the nursery production and improve working conditions for nursery workers.
Texas - The deployment of AI-based computer vision models for strawberries could improve the performance, efficiency, and remote interactivity of autonomous yield estimation and robotic harvesting operation, leading to reduced labor requirements and production costs. AI-assisted computer vision-based solution in combination with crop growth model could assist growers in decision-making for optimizing management processes for indoor strawberry production, improving resource use efficiency.
Texas - Greenhouse lettuce production cost is significantly higher. The developed AI-assisted predictive analytics could optimize resource usage, reducing costs and improving the sustainability of US greenhouse industry. The AI-based crop growth monitoring approach holds promise for efficient data aggregation from multiple sensors and predictive analytics to assist growers in decision-making for resource optimization.
Texas - The aim of the non-destructive sensing is to develop a site-specific crop disease management system. The work will enable the development of future precision autonomous system to apply spraying chemical in correct amount and the right place, which will reduce environmental pollution, and serve general public interests. The work provides a general workflow of a disease detection system, which can be expanded to other diseases and crop stresses. The developed AI-assisted system could assist farmers in decision-making, ultimately boosting profitability and promoting sustainability.

Date of Annual Report: 01/01/1970

Report Information

Annual Meeting Dates: 08/22/2025 - 08/22/2025
Period the Report Covers: 10/01/2024 - 09/30/2025

Participants

State Institute Representative(s)/Presenter(s)
Arizona UA Pedro Andrade-Sanchez; Haiquan Li; Mark Siemens
California UC Davis Stavros Vougioukas
Connecticut UConn Ana Legrand’s student Bivek
Florida UF Daniel Lee; Davie Kadyampakeni
Georgia UGA Md Sultan Mahmud
Kentucky UK Joe Dvorak
Michigan Michigan State Yuzhen Lu
Missouri Mizzou Jianfeng Zhou
Mississippi Mississippi State Alex Thomasson
New York Cornell Yu Jiang
North Dakota NDSU Xin Sun
Pennsylvania PSU Long He
Tennessee UTK Hao Gan

Brief Summary of Minutes

Annual Meeting Minutes:

Meeting Started 11:00 am on August 22, 2025 via Zoom.

Faculty introduction

Hao Gan presented a summary of the 2024 annual meeting, which was held in Hawaii.

Committee Term Length Discussion: The committee discussed whether to adopt a 2-year or 1-year term for officers. Dr. Stavros advocated for a 2-year term, citing benefits for continuity, and Dr. Heinemann requested rationale for the change. Dr. Jiang supported the idea for smoother transitions with 2-year term, while Dr. Siemens opposed it. A vote was held: Objections to 2-year term: 8; Support for 2-year term: 5; Outcome: The committee will retain the 1-year term for officers.

Workload management: Siemens emphasized distributing responsibilities among officers to reduce workload for the host of meetings.

Proposal writing was clarified: typically done by the committee chair or PI. Dr. Stavros had written the current proposal.

New secretary selection: Dr. Yu Jiang proposed Dr. Dong Chen name as secretary. Dr. Chen was accepted by the members of the W4009 multi-state project.

New officers for 2025-26: Chair: Dr. Yuzhen Lu, Vice-Chair: Dr. Md Sultan Mahmud, Secretary: Dr. Dong Chen

Next meeting host and location: Dr. Rex Sun expressed interest in hosting the 2026 meeting in Fargo, North Dakota, highlighting the presence of specialty crops such as grape, strawberry, and cranberry. He remained open to alternative locations but showed strong interest in hosting. Dr. Thomasson supported the idea of holding the meeting in Fargo. A motion to host the 2026 meeting in Fargo was moved by Dr. Daniel Lee and seconded by Dr. Yu Jiang. The W4009 multi-state project team agreed: The 2026 meeting will be held in Fargo, North Dakota.

Meeting location and host for the future: Dr. Alex Thomasson expressed willingness to host a meeting in Mississippi in the future, possibly in 2027. Dr. Hao Gan also showed interest in hosting a future meeting in Tennessee and suggested discussing this meeting location section topic a regular agenda item for future meetings.

Combining meetings discussion: Dr. Gan proposed the idea of combining the committee’s meeting with other multi-state projects (e.g., 1098) at the AI in Agriculture conference or other similar conference. Dr. Thomasson noted challenges in hosting joint meetings, emphasizing the need for leadership coordination between both projects due to overlapping interests. The group agreed that occasional joint meetings may be feasible, but separate meetings are still necessary. Cost concerns were raised regarding travel and hosting logistics. Dr. Heinemann suggested scheduling meetings before or after the ASABE conference to help reduce costs. Dr. Daniel Lee recommended small group meetings (separately for this group), which could allow visits to different states or universities.

Discussion on research impact training: Dr. Alex Thomasson and Dr. Joe Dvorak discussed past impact training sessions, including one led by a USDA representative, Ms. Sara Delheimer (Delheimer@colostate.edu). The most notable session was an in-person workshop by Sara Delheimer in 2019 in Kentucky, prior to the pandemic. Details were limited due to the time being elapsed, but Dr. Dvorak shared Sara Delheimer’s contact information and mentioned she is likely still with the Multistate Group. The committee discussed the idea of officers reaching out to request a 30-minute workshop on research impact. Dr. Gan and Dr. Thomasson supported the idea of contacting USDA for future training opportunities. It was suggested that this topic be considered for future meeting agendas. The following links were shared by Dr. Dvorak

Multistate Research Fund Impacts | Impact Writing Workshops

Automation for Specialty Crops

https://www.mrfimpacts.org/single-post/automation-for-specialty-crops

Annual Report: Dr. Gan will send details about the Annual Report via email to the committee. The report must be submitted within 60 days following the annual meeting.

Business meeting adjourn: 12:15 pm

Accomplishments

Arizona: <ul> <li>The commercial scale steam applicator for thermally killing weed seed and soilborne pathogens prior to planting developed in this project was evaluated in carrot and spinach. Trial results showed that steam treatment provided very good weed control (75-92%, 4 trials) and significantly reduced hand weeding times and cost (45-65%, $140-400/acre) as compared to the untreated control. Work rates and machine costs were marginally acceptable, at 0.2 ac/hr and $1,139/ac respectively.</li> <li>An artificial intelligence pipeline was developed to recognize weeds in lettuce and spinach fields without requiring manual image segmentation, enabling automatic segmentation and classification. The model achieved an F1 score of over 85% at the bounding-box level for weed detection in lettuce fields, and over 75% at the pixel level, evaluated on approximately 10% of 939 annotated images. Preliminary results in spinach yielded an F1 score of 87.5% at the bounding-box level.</li> <li>An on-the-go multi-sensor platform integrated soil and plant sensors and was successfully deployed in a lettuce field 30 days after planting in Yuma AZ. The integrated device collected geo-referenced active spectral data over plant canopy in 670 nm (red), 730 nm (red edge), and 780 nm (NIR) wavelengths; as well as electrical conductivity, temperature, and Red and IR spectroscopy signals in the top 15 cm of the soil profile. Data acquisition incorporated RTK-level GNSS positioning data logged at 5 Hz. These data are being analyzed using machine learning techniques as part of a multiscale approach to smart nitrogen fertilization.</li> </ul>   California: <ul> <li>A prototype fruit-catching arm was built for shake-catch harvesting. The arm can penetrate a tree’s canopy and catch fruits that fall during shaking. A system model with multiple layers of such arms was developed and used in simulation for design and optimization.</li> <li>Thirty instrumented strawberry-picking carts were used by farm workers for six months, twice a week, to collect data that was used to calculate yield maps in one field in Salinas, CA.</li> <li>A fully automated almond yield mapping system developed at UCD was used to collect data in an almond orchard in Madera, CA, for two weeks.</li> <li>Develoiped a phenologically-aware, strawberry yield forecasting model that uses time-series of ground-based imagery to create yield forecasts several weeks in advance.</li> <li>Developed the first U.S. ATV safety test station that includes static, dynamic, and rollover simulators, providing a comprehensive platform for experimental testing and operator training. The ATV rollover simulator is also used for hands-on safety demonstrations and extension activities.</li> <li>Developed accurate multi-body finite element (FE) models to simulate rollover incidents and initiated several projects focused on safety in autonomous and robotic agricultural systems.</li> <li>Developing a phone application for predicting heat-related illnesses among farmworkers.</li> <li>Developed of a multi-trait predictive model and a multi-resolution remote sensing framework for assessing grapevine nutrient status. We successfully built a leaf-level model using hyperspectral reflectance (400–2500 nm) integrated with laboratory-measured traits, including major and micronutrients, chlorophyll, LMA, and EWT. The model achieved strong performance with an average R² of 0.73 and NRMSE of 0.07, confirming its ability to capture complex inter-trait relationships. Additionally, we simulated over 2,000 synthetic vineyard scenes using the HELIOS 3D ray-tracing framework on AWS to support canopy-level modeling and system scalability.</li> </ul> <ul> <li>Collaboratively initiated a project to introduce ergonomic risk into crop-transport robot scheduling algorithms to balance work efficiency and ergonomic risk. The project focuses on developing a dual-objective optimization approach to compute the schedules of crop-transport robots, which increases the work efficiency of harvest workers while restricting workers’ back fatigue from prolonged stooping. Performance and muscular fatigue data were collected from several farmworkers performing field strawberry harvesting in Salinas. Currently analyzing the data and preparing a paper for publication.</li> <li>Continued the study exploring an ultrasound-based system combined with an AI-enabled image pattern recognition system to detect farmworkers’ spine health status while performing labor-intensive tasks.  Collected data on phantom samples to test various simulated spinal discs under varying water content. Collaborated with an orthopedic surgeon to provide gold standard MRI images and general guidance on the clinical significance of the project.</li> <li>Continued to explore workers’ compensation records to investigate the role of emerging automated and robotic systems in injuries to farmworkers.  The AI-enabled approach to extract information from injury descriptions has been developed, and a paper has been prepared for submission, which demonstrates the novel approach.</li> <li>Evaluated the potential of deep learning (DL) models for ETo forecasting, particularly emphasizing the efficacy of a global learning scheme compared to traditional local learning. Global learning involves training forecasting models on pooled data from multiple time series, tested over new instances. We compared the performance of statistical models and advanced DL models, demonstrating significant accuracy enhancements in global learning schemes. We also explored automatic hyperparameter optimization for these models to achieve state-of-the-art forecasting accuracy, yielding RMSE values below 10 mm/month for one-year ahead forecasts on new, unseen stations.</li> </ul> <ul> <li>Inspired by the advancement of physics-informed neural networks (PINN), we Evaluated the integration of both empirical and semi-physical models into a DNN, particularly in the loss function for reference evapotranspiration estimation. The PINN improved ETo estimation performance, surpassing the fully-data driven model and the semi-physical models in all metrics. The optimal balance between physics and data-driven approaches was determined using the CMA-ES under various scenarios.</li> <li>Developed a Convolutional Neural Network (CNN) designed to compute crop canopy cover autonomously. We deployed a low cost IoT system with an RGB camera and we harnessed a dataset comprising 283,000 images, each with dimensions of 256 × 256 pixels, for network training. Optimizing the neural network was achieved through a Genetic Algorithm (GA), employing an objective function focused on minimizing parameter density while maintaining accuracy. The results indicate that our CNN, with fewer than 200,000 parameters, achieved an R2 of 98 %, an MSE of 0.0024, and an MAE of 0.038.</li> <li>Two workshops were held to advance workforce development in agricultural technology. The first engaged 10 educators, and the second involved 20 growers, advisors, and industry professionals from Western states in hands-on sessions on AI, IoT, drones, and robotics. Participants received stipends to support their participation. These workshops served as a catalyst for transferring emerging technologies from research to on-farm applications.</li> <li>Coffee professionals know that the roast profile—the temperature over time—significantly affects coffee flavor and quality. Key factors include perceived sourness, measured as titratable acidity (TA), and color. Most research has focused on lab roasters with limited control, leaving the relationship between commercial roast profiles and TA and color unclear. Our study examined 5-kg commercial roasts with the same duration but different internal dynamics. We found titratable acidity (TA) peaks during the first crack and returns to initial levels by the second crack, with little variation in peak TA across profiles. Additionally, we identified a universal color curve that follows a predictable pattern. These insights help in controlling and achieving desired coffee qualities during roasting. </li> <li>From a purely extension perspective, I also authored a book chapter relevant to the work carried out by this multistate group, specifically focusing on postharvest quality factors and their assessment.</li> <li>  A computer model of a shake-catch harvester, a harvest simulator, and a prototype fruit-catching arm.</li> <li>Approximately fifty precision yield maps were generated for strawberries and two tree-level yield maps for almond trees.</li> <li>Strawberry yield forecasts were made at 4 to 7 day intervals across multiple growing seasons at a commercial farm (about 40+ ground-based scans using RGB sensing system)</li> <li>96 presentations related to ATV safety across various events, including workshops and training programs. This includes more than 68 California Occupational Safety and Health Administration (Cal/OSHA) training sessions for safety officers, managers, and inspectors, as well as numerous hands-on demonstrations and field activities.</li> <li>The integration of the multi-trait model into canopy-level simulations established a robust foundation for large-scale vineyard nutrient assessment. The simulated datasets showed high similarity to real-world multispectral imagery, enabling reliable model validation and training. Furthermore, a mobile application prototype—the Leaf Monitor App—was developed to provide real-time predictions from user-input spectral data, making nutrient assessment more accessible and efficient for researchers and growers. The web application of this tool can be found here: https://www.digitalaglab.com/LeafMonitorApp.</li> <li>Improved the accuracy of deep learning models for reference evapotranspiration forecasting in the Central Valley of California using global learning .</li> <li> Described and developed a novel approach for reference evapotranspiration estimation using Physics informed neural network</li> <li>Developed a low-cost IoT platform for crop canopy cover estimation for better yield prediction and accurate irrigation management</li> <li>Survey results from the two workshops to advance workforce development in agricultural technology showed high participant satisfaction, with an average rating of 4.6 out of 5 and a 4.5 out of 5 likelihood of recommending the workshops. Participants reported significant knowledge gains in AI, IoT, drones, robotics, and precision irrigation, with educators planning to update curricula and industry participants intending to apply these technologies in their operations.</li> <li>Book chapter specifically focusing on postharvest quality factors and their assessment.  The extension publication significantly advances the understanding of postharvest quality assessment, control, and measurement, providing valuable insights that can improve quality control processes and extend the shelf life of produce. It also contributes to future research and practical applications in postharvest science, quality assessment, and sensing</li> <li>We compiled the most comprehensive, state-by-state list of regulated noxious weeds in the United States, including 1,000+ species from state lists and 112 species from the federal list, and systematically searched each by name on Amazon and eBay to document their online availability. This effort produced the first quantitative nationwide assessment of e-commerce pathways for invasive plant introduction. We also built and launched a publicly accessible online platform (https://regulatedplants.unu.edu/) that allows users to explore regulated species through interactive maps, keyword search, and integrated species data via GBIF.</li> <li>Our screening revealed that approximately 38% of state-listed noxious weeds are available on Amazon and eBay, despite their regulated status. For federally listed species, 7% were found on Amazon and 33% on eBay, indicating a major inconsistency in enforcement across platforms. Amazon showed partial compliance through shipping restrictions, while eBay had no similar mechanism in place. These findings were shared with researchers and California state officials and the online database is now being used as a reference tool for monitoring listings and informing potential enforcement action.</li> </ul>   Florida: <ul> <li>Dr. Lee’s team developed an end-to-end pipeline for strawberry production, integrating fruit detection, canopy analysis, yield forecasting, and hardware deployment. Their lightweight detection models achieved high accuracy across ripening stages, while a novel segmentation and depth estimation system enabled precise 3D canopy volume measurements from single-view images—surpassing traditional and manual methods in both scalability and detail.</li> <li>To enhance yield forecasting, the team introduced a multi-modal model that fused visual and weather data, significantly reducing prediction errors and improving alignment with actual yields across two cultivars and seasons. Field testing of four AgriBerry Vision (ABV) modules mounted on sprayers demonstrated real-time image capture and model inference capabilities, though further optimization is needed for seamless integration into large-scale operations.</li> <li>In another project, a high-accuracy leaf wetness detection system was developed for strawberries using a hybrid ConvLSTM model. This approach combined CNN-based spatial droplet detection with LSTM-driven temporal analysis, enabling robust classification of wet versus dry surfaces under varying conditions. With attention mechanisms and Focal Loss enhancing performance, the model reached 97% validation accuracy, empowering growers with timely moisture insights for precise fungicide application and improved crop health.</li> <li>Dr. Kadyampakeni presented 3 seminars at the Citrus Show, Citrus Institute, and Citrus and Specialty Crops Expo covering topics on automated irrigation and precision fertilizer application.</li> <li>Dr. Kadyampakeni was Guest Editor for two special issues in Irrigation Science on the topics of machine learning, artificial intelligence and irrigation automation and microirrigation.</li> <li>Dr. Li developed a customized deep learning model to rapidly quantify berry fruit internal bruising, given that evaluating fruit internal bruising manually is a tedious and time-consuming process. His group evaluated blueberries from 61 cultivars of soft to firm types over a three-year period. A web-app was developed and shared with the public.</li> <li>Dr. Li developed a robotic blueberry phenotyping system, called MARS-Phenobot, that collects data in the field and measures fruit-related phenotypic traits such as fruit number, maturity, and compactness. The work provides a promising solution for automated in-field blueberry fruit phenotyping, potentially replacing labor-intensive manual sampling. Furthermore, this approach could advance blueberry breeding programs, precision management, and mechanical/robotic harvesting.</li> </ul>   Georgia: <ul> <li>In the period between (10/01/2024 – 09/30/2025), one of the key milestones accomplished was an RGB-LiDAR integrated system for measuring trunk diameter and canopy volume in ornamental trees (e.g., oak), enabling maturity assessment and precision spraying. This work led to a publication in Smart Agricultural Technology. An AI-guided and large language model (LLM)-powered web application was also developed for real-time diagnosis of dogwood leaf spot severity, combining image analysis with large language models to provide severity scores and management advice. Users of this app can obtain real-time detection and area calculation results, along with AI analysis about the disease, by simply uploading a picture of the leaf of dogwood and describing the disease using a smartphone or computer. This study is under consideration for publication in Computers and Electronics in Agriculture. Ongoing efforts include a robotic system for real-time scouting and mapping of Japanese maple scale using machine vision and AI. In onion research, we developed an early version prototype delta robotic laser weeding system with a 60W blue diode laser to kill weeds in onion fields. The preliminary evaluation showed that 3 s and 2 s treatments killed 90% and 60% of weeds, while 1 s and 0.5 s exposures only killed 20% and 10%. The robotic system had a positioning accuracy of 0.85 millimeters, showing potential to reduce herbicide use and labor costs. Additionally, a remote sensing framework was initiated to monitor pecan bacterial leaf scorch, aiming to understand disease spread and inform precision management. Outputs include three manuscripts (three published, one under review), a functional AI app, robotic prototypes, and preliminary datasets. These tools offer measurable benefits such as improved disease detection, reduced chemical inputs, and enhanced decision-making. In the coming year, the team will focus on field validation, system optimization, stakeholder training, and expanded disease and pest monitoring using ground robots, UAVs and remote sensing.</li> </ul>   Kentucky: <ul> <li>With the sudden explosion of AI tools and systems, a key concern has been if they can accurately assess detailed agricultural information. This is a key consideration for creating agentic AI systems that solve real world agricultural problems. Vision systems are readily available that will process an image and accurately describe what is in it. However, an open question was whether an AI system could take those descriptions and retrieve additional information to make an informed assessment. As a general test of this ability, we investigated the ability of LLM embeddings and generative models to analyze, cluster and describe all the in-depth technical research presentations submitted to ASABE’s Annual International Meeting. If LLM-based tools can appropriately categorize and generate responses using this complex dataset, they should likewise be able to sort through and craft appropriate responses to general agricultural concerns. This experiment was successful and generated several websites where the outputs can be explored.</li> <li>ASABE AIM 2025 Presentation and Session Similarity Exploration Tool https://asabe-aim-2025-similarity-exploration-at-conference-app.streamlit.app/</li> <li>Automated Session Creation Visualization Tool - AIM 2025 (All Communities) https://sessioncreation-aim2025-all.streamlit.app/</li> <li>Presentation Similarity Exploration Tool https://asabeaimsimilarity2025.streamlit.app/</li> </ul>   Massachusetts: <ul> <li>We built a low-power impedance sensing circuit to replace commercial potentiostats, using multi-frequency sweeping (200 KΩ–1 MΩ) to distinguish signals from different nutrients inside the xylem of the plants.</li> </ul>   <ul> <li>We evaluated multiple long-range communication protocols (LoRa, LoRa Backscatter, NB-IoT, LTE-M) to allow the sensors to transmit data to the base station (farmer houses) before relaying them to the cloud. We identified that LoRa for its robustness and support. Collision mitigation was explored through enhanced chirp modulation and concurrent interference cancellation. We are evaluating the performance of LoRa sensor nodes in the field. The line-of-sign conditions work perfectly but we are evaluating the performance of the communication protocols with a more practical settings with more blockages.</li> <li>To maintain zero-maintenance operation, we have investigated and tested multiple wind-based energy harvesting structures, selecting the most efficient configuration through field trials. The current prototype operated for couple of weeks continuously on a field without issues. We are still testing this functionality.</li> <li>The machine learning model to convert the captured signals into nutrient levels should be running on the devices to support in time water and fertilizer management. So, to enable this capability, we deployed an ARM Cortex-M4 MCU (MAX78000) with CNN acceleration to run intermittent computing and ML-based nutrient inference on-chip, enabling edge intelligence without external power or servers.</li> <li>Prototype nutrient sensors, low-power impedance circuits, LoRa-based communication modules, wind energy harvesters, ML-enabled MCU platform, and field data from real-world deployments.</li> <li>Demonstrated battery-free, in situ nutrient sensing with continuous operation.</li> <li>Reduced infrastructure and maintenance costs for remote farm monitoring.</li> <li>Enabled long-range wireless reporting and low-cost IoT scalability.</li> <li>Validated robust operation in real world environments.</li> <li>Plant sensor array development and validation.</li> <li>Low-power impedance sensing circuit completed.</li> <li>LoRa integration with interference mitigation.</li> <li>Energy harvesting deployment validated.</li> <li>Plans for Next Year: Expand sensing to additional ions, integrate hybrid solar-wind harvesting, scale to multi-node networks, and deploy pilot studies with growers to demonstrate agronomic impact.</li> </ul>   Michigan: <ul> <li>Short-term Outcomes: 1) developed computer algorithms for asparagus perception and conducted a preliminary test of two pneumatic actuator prototypes for selective harvesting asparagus, 2) developed a new multispectral vision-based automated sweetpotato grading and sorting system, 3) developed an improved version of a smart sprayer for precision vegetable weeding with both indoor and on-farm testing conducted and performed a on-farm demonstration to visitors, 4) assessed hyperspectral imaging for the detection of spotted wing drosophila infestation in blueberries, 5) developed a machine vision prototype for full canopy imaging of blueberries and a smartphone apple for blueberry detection to support precision management and established a comprehensive benchmark of real-time detectors for blueberry detection, and 6) build a preliminary version of a vision-based system for automated apple grading.</li> <li>Outputs: published 11 peer-reviewed journal articles, presented 6 conference papers, published a new multi-season open-access weed dataset, and released a smartphone application for blueberry detection. </li> <li>Activities: Applied artificial intelligence and machine vision methods to fruit quality grading, asparagus perception for selective harvesting, precision vegetable weeding, and in-orchard fruit detection. AI and the Internet of Things were leveraged for irrigation and disease management of specialty crops.</li> </ul>   Mississippi: <ul> <li>At Mississippi State University, PI Dong Chen is leading research on artificial intelligence (AI) and automation for harvesting of delicate U.S. specialty crops, addressing a critical need in the industry, particularly for crops like blueberries and strawberries. His team has developed an open-source blueberry image dataset containing more than 600 annotated images, supporting the development of AI-based detection and yield estimation algorithms. Research outcomes have been disseminated through two conference presentations, including the 2025 AI in Agriculture Conference (Starkville, MS, March 31 through April 2) and the American Society for Horticultural Science (ASHS) Conference 2025 (New Orleans, LA, July 28 through August 1), and three journal manuscripts currently in preparation.</li> <li>In addition to research accomplishments, the team has played a major role in student training and workforce development. PI Chen serves as faculty advisor for two international student robotics competitions, the Farm Robotics Challenge and the ASABE Robotics Student Design Competition. Through these initiatives, over ten graduate and undergraduate students from Agricultural and Biological Engineering, Electrical and Computer Engineering, and related programs have gained hands-on experience in applying robotics and AI to solve real-world agricultural problems. Notably, the student teams have designed and prototyped a soft robotic system for strawberry harvesting, integrating AI-based perception modules for fruit detection and ripeness estimation. The system demonstrated a clear advantage over traditional rigid robotic systems, offering improved adaptability, gentler handling of delicate fruits, and enhanced operational safety during the Farm Robotics Challenge. These interdisciplinary efforts have enhanced student technical competencies and fostered partnerships with industry stakeholders focused on advancing agricultural automation and smart farming technologies.</li> <li>Also, PIs Dong Chen and Alex Thomasson are leading a project on high-accuracy high-precision phenotyping, involving collaborative robotics between a remote-sensing drone and a ground-based robot serving as a calibration reference for reflectance, crop height, and canopy temperature.  This project includes one graduate student and is being conducted collaboratively with colleagues at Kangwon National University in Korea.</li> </ul>   New York: <ul> <li>The second-gen Phytopatholobot (PPB) has been finalized and distributed to all major grape breeding programs across the US to support high throughput plant phenotyping for disease resistance studies and cultivation.</li> <li>A new data processing pipeline based on large multimodal model has been deployed to analyze image datasets from PPB v2 to quantify disease infection severity for grape downy and powdery mildews. This has been adopted by all major grape breeding programs in the US.</li> <li>A laboratory hyperspectral imaging system (400 to 1000 nm) with streamlined processing pipeline has been deployed at Cornell AgriTech to support a wide range of applications in plant science, entomology, postharvest quality assessment, and food science and processing.</li> </ul>   Pennsylvania: <ul> <li>Develop a robotic crop load management system for apples at flower bud stage. In this year, we have focused on developing a machine vision system to count flower bud numbers on a branch and also estimate the bud distribution and provide bud thinning decisions.</li> <li>Continued working on decision support system for robotic button mushroom harvesting, and a vacuum based mushroom picking end-effector was designed and tested at Penn State Mushroom Research Center. The decision support system provides the coordinates of each mushroom and also the picking sequence of mushrooms in a test tub.</li> <li>An integrated chemical thinning system was tested in both research and commercial apple orchards. The system included a machine vision system to count the number of green fruitlets on trees, and then a precision spraying system was used to apply chemical thinner based on the crop load at individual tree level. The results showed that the system saved 18% of chemical thinner while maintained similar thinning performance by comparing to the conventional spraying system.</li> <li>An unmanned ground robotic system was tested in a vineyard and a raspberry field. The tests showed that the system achieved autonomous spraying for pest management in these fields, while chemical usage and performance of pest control are still under analysis.</li> <li>A large amount of images was acquired for apple fruits with various surface defects, including insect damage, disease damage, and nutrient deficiency. A deep learning based algorithm was developed to identify these defects, which will be used for quantifying the quality of individual fruits for robotic selective harvesting.</li> <li>A deep learning based weed detection algorithm was developed to detect and quantify the weed density in apple orchards, especially the weeds under trees. A target spraying system is under development to precisely apply herbicides to these detected weeds.</li> <li>To effectively pick fruits from complex canopies, two types of picking mechanism with soft fingers/grippers were developed, and tested in both peaches and apples.</li> <li>A classification model was developed and tested to identify the invasive box tree moth, which attacks the ornamental boxwood. This invasive has been spreading in the northeast US and eastern Canada and was recently discovered in Pennsylvania. A phone-based app has been developed so that users can upload an image to determine if the insect is a box tree moth or some other similar-looking species, and a pheromone-lure prototype automated monitoring trap was designed and tested.</li> <li>A spectral vision system was developed to detect the asymptomatic diseases on button mushroom and the robot to treat the diseased mushroom is under developing.</li> <li>Develop a Robotic System for Automated Apple Tree Leaf Detection and Plant Phenotyping</li> <li> </li> <li>A machine learning algorithm was developed to detect tomato defense proteins in hyperspectral images when corn earworm attacks tomato plants. The treatments included damage from naturally occurring (wild) earworms, a mutant earworm, and mechanically-induced, compared to a healthy plant control.</li> <li>Two types of automated pollinator monitoring insect traps were tested. “InsectEye” was field tested to determine its ability to classify different families of pollinators. BeeEye was tested in the lab to determine its ability to classify different bee species.</li> </ul>   Tennessee: <ul> <li>A robotic platform named Amiga was purchased as the base for a pot-in-pot nursery tree harvester.</li> <li>Multiple end-effector has been designed and tested to securely lift the plastic pots for tree pots.</li> <li>Computer vision algorithms is being developed for tree recognition and localization.</li> </ul>   Texas: <ul> <li>Harvesting is one of the most labor-intensive operations for greenhouse production. We designed a robotic end-effector prototype for harvesting lettuce with a pair of shear blades and a two-finger gripper mechanism operated using a single linear actuator. Cutting and holding mechanisms work together using the first-class lever technique, where applied and acting forces are connected at the pivot point. The end-effector prototype was integrated into a linear arm connected to a two-directional linear manipulator system operated using an embedded controller. Validation experiments were conducted, and the results indicated a 95.15% success rate in cutting and a 94.45% success rate in securely holding the lettuce during harvest. The developed robotic end-effector serves as a pivotal component in lettuce harvesting automation.</li> <li>Crop disease scouting is the first critical step for controlling plant diseases. We employed lightweight deep-learning models to detect tomato diseases using digital images (bacterial spot, early blight, healthy, late blight, leaf mold, septoria leaf spot, two-spotted spider mites, target spot, and yellow leaf curl virus). Models were deployed on low-cost and low-power consumption Edge devices to investigate their performance capabilities as standalone Edge-AI solutions. Our lightweight models achieved accuracies of up to 98.25% in detecting tomato diseases. Edge device (Raspberry Pi) with AI accelerator (Google Coral) achieved the best cost/Frame per second (FPS) performance of 0.14 compared to other Edge devices NVIDIA Jetson AGX Orin and NVIDIA Jetson Nano Orin with cost/FPS of 0.7 and 0.26, respectively. These results showed the potential of standalone Edge-AI solutions using low-cost and low-power-consuming software and hardware resources for early tomato disease detections.</li> <li>Energy consumption per unit area can be reduced by increasing the crop yield per unit area by optimizing planting density. We developed an AI model (Ensemble Seg) that combines Mask RCNN (instance segmentation) and DeepLabV3 (semantic segmentation) to adjust the spacing between NFT channels for optimal greenhouse space utilization. Images were collected daily throughout the growth cycle, capturing the full development period of the lettuce plants. Our model resulted in a segmentation accuracy of 0.95. To assess plant overlap, segmented masks of each plant and its neighboring plants were identified based on the distances between each plant’s center. Overlapping was then determined by pixels with identical coordinates in an image across the segmented masks. The developed model can be used for intelligent decision support to optimize space use in greenhouse hydroponic production.</li> </ul>

Publications

Arizona: <ul> <li>Kocher, M.F., Smith, J.A., Arnett, G., Werning, J.L., Siemens, M.C., & Hanna, M.H. (2025). ASABE S658 Planter test standard: 1. Row unit test. Appl. Eng. Agric. (In press)</li> <li>Parmar, S., Lou, W., McGinnis, E., Didan, K., Siemens, M.C. & Haiquan, L. (2025). Integrating box-based deep learning with zero-shot segmentation for improved weed and crop classification in lettuce fields. Agric., MDPI (Submitted) doi: 10.20944/preprints202506.0642.v1</li> <li>Kocher, M.F., Smith, J.A., Arnett, G., Werning, J.L., Siemens, M.C., & Hanna, M.H. (2025). ASABE S658 Planter test standard: 2. Monitor system test. Appl. Eng. Agric. (Accepted)</li> </ul> California: <ul> <li>Anokye-Bempah, L., Styczynski, T., Ristenpart, W.D., & Donis-González, I.R. 2025. A universal color curve for roasted arabica coffee. Scientific Report 15 (24192). doi: <a href="https://doi.org/10.1038/s41598-025-06601-w">https://doi.org/10.1038/s41598-025-06601-w</a>.</li> <li>Anokye-Bempah, L., Styczynski, T., Teixeira Fernandes, N.A., Gervay-Hague, J., K., Ristenpart, W., & Donis-González, I.R. 2024. How Roast Profile Affects the Dynamics of Titratable Acidity during Coffee Roasting. Scientific Reports 14:8237. Doi: <a href="https://doi.org/10.1038/s41598-024-57256-y">https://doi.org/10.1038/s41598-024-57256-y</a>.</li> <li>Arman Ahmadi, Andre Daccache, Minxue He, Peyman Namadi, Alireza Ghaderi Bafti, Prabhjot Sandhu, Zhaojun Bai, Richard L. Snyder, Tariq Kadir (2025). Enhancing the accuracy and generalizability of reference evapotranspiration forecasting in California using deep global learning, Journal of Hydrology: Regional Studies, Volume 59,102339.</li> <li>Chouaib El Hachimi, Salwa Belaqziz, Saïd Khabba, Andre Daccache, Bouchra Ait Hssaine, Hasan Karjoun, Youness Ouassanouan, Badreddine Sebbar, Mohamed Hakim Kharrou, Salah Er-Raki, Abdelghani Chehbouni (2025). Physics-informed neural networks for enhanced reference evapotranspiration estimation in Morocco: Balancing semi-physical models and deep learning, Chemosphere, Volume 374, 2025,144238</li> <li>de Oca, A., Magney, T., Vougioukas, S.G., Racan, D., Torrez-Orozco, A., Fennimore, S. Martin, F., Earles, M.  (2024) Strawberry fruit yield forecasting using image-based time-series plant phenological stages sequences. Computers and Electronics in Agriculture. 237, Part B, 110516.</li> <li>Donis-González, I.R., Zakharov, F., Bruhn, C.M., Cantwell, M.I., Reid, M.S., Slaughter, D.C., & Kader, A. 2025. Book chapter: Postharvest Technology of Horticultural Crops, 4th Edition.  Kader, A., Thompson, J.F. & Salveit, M. (ed) – ANR publication 21658. Quality Factors and Their Evaluation, Vol. 4, Davis, CA. pp. 61.</li> <li>F. Puig, R. Gonzalez Perea, A. Daccache, M.A. Soriano, J.A. Rodríguez Díaz, Convolutional neural networks for accurate estimation of canopy cover, Smart Agricultural Technology, Volume 10, 2025, 100750, ISSN 2772-3755,</li> </ul> <ul> <li>Farajpoor, P., Pourreza, A., Narimani, M., El-kereamy, A., & Fidelibus, M. W. Leaf spectral reflectance prediction using multihead attention neural networks. Proceedings Volume 13475, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X; 134750V (2025) https://doi.org/10.1117/12.3061298</li> </ul> <ul> <li>Fu, K., Zhu, Y., Frehner, E., Rizzo, K., Vougioukas, S. G., Bailey, B. (2025). Analysis of the Potential Improvement of Mechanical Fresh-Market Fruit Harvesting Efficiency Utilizing a Dynamically Adjustable Multi-Level Fruit Catching and Retrieval System. Smart Agricultural Technology, 12, 101228.</li> <li>Karkee, M., Vougioukas, S. G., Devadoss, S., Bhusal, S. (2025) Mechanization Efforts in Fruit Tree Pruning and Thinning. Choices, 2nd Quarter 40(2), 10-15</li> <li> Khan, F. A., Khorsandi, F., Ali, M., Ghafoor, A., Khan, R. A. R., Umair, M., Shahzaib, Rehman, A., & Hussain, Z. (2025). Spray drift reduction management in agriculture: A review. Journal Name, 20(1), 1–36.</li> <li> Khorsandi, F., Farhadi, P., Denning, G., Grzebieta, R., Gibbs, J., Godler, Y., Heydinger, G., Hicks, D., Jennissen, C., Lundqvist, P., McIntosh, A., Rechnitzer, G., Simmons, K., & Yoder, A. (2025). Advancing all-terrain vehicle safety in agriculture: Insights and innovations from global experts. Journal Name, 31(3), 173–202.</li> <li>Khorsandi, F., Wong, J., & de Moura Araujo, G. (2025). Is it safe for children to ride youth-sized all-terrain vehicles? Journal Name, 94, 216–228.</li> <li>Lincoln, J., Gorucu, S., Khorsandi, F., Aby, G. R., Elliott, K. C., Shutske, J., & Issa, S. (2025). Occupational safety research needs in the field of robotics and automated equipment in agriculture. Journal Name, 31(3), 217–230.</li> </ul> <ul> <li>Peanusaha, S., Pourreza, A., Kamiya, Y., Fidelibus, M. W., & Chakraborty, M. (2024). Nitrogen retrieval in grapevine (Vitis vinifera L.) leaves by hyperspectral sensing. Remote Sensing of Environment, 302, 113966.</li> <li>Pourreza, A., Kamiya, Y., Peanusaha, S., Jafarbiglu, H., Moghimi, A., & Fidelibus, M. W. (2025). Nitrogen retrieval in grapevine canopy by hyperspectral imaging. Computers and Electronics in Agriculture, 229, 109717.</li> </ul> <ul> <li>Shutske, J., Issa, S. S., Johnson, T., Gorucu, S., Lincoln, J., Khorsandi, F., Pate, M., Smith, E., Versweyveld, J., Aaron, M. A., & Aby, G. R. (2025). SAFERAG – Risk assessment, standards, and regulation: Needs and recommendations. Journal Name, 31(1), 1–13.</li> <li>Vougioukas, S. G., Karkee, M., Devadoss, S., Gallardo, K., Charlton, D. (2025) Mechanization Efforts in Fruit Harvesting. Choices, 2nd Quarter 40(2), 3-9</li> <li>Wei, P., Peng, C., Lu, W., Zhu, Y., and Vougioukas, S.G. (2025) Efficient and Safe Trajectory Planning for Autonomous Agricultural Vehicle Headland Turning in Cluttered Orchard Environments. IEEE Robotics and Automation Letters 10(3) 2574-2581.</li> <li>Wong, C., & Moghimi, A. (2025). Stakeholder mapping of precision weeding commercialization ecosystem in California. Agricultural Systems, 222, 104152. <a href="https://doi.org/10.1016/J.AGSY.2024.104152">https://doi.org/10.1016/J.AGSY.2024.104152</a></li> <li>Mesgaran, M. (2024, December). Web of Weeds: Tracking Online Sales of Noxious Weed in the US. In AGU Fall Meeting Abstracts (Vol. 2024, pp. IN24A-01)</li> <li>Mesgaran, M., P. Robeck, L. Blank. 2024. Digital Menace: Quantifying the Online Sale of Noxious Weeds in the US and EU. Weed Day Workshop, June 20204, Davis.</li> </ul>   Florida: Journal Publications <ul> <li>Chen, Y., A. Shu, Z. Liu, Y. Chen, W.S. Lee, and Y. Zhang. 2025. SP-RTSD: A lightweight real-time strawberry detection on edge devices for onboard robotic harvesting. Journal of Field Robotics, 2025; 1-19. <a href="https://onlinelibrary.wiley.com/doi/10.1002/rob.22582">https://onlinelibrary.wiley.com/doi/10.1002/rob.22582</a>.</li> <li>Tapia, R., W.S. Lee, V.M. Whitaker, and S. Lee. 2025. Multiple methods for predicting strawberry powdery mildew severity from field canopy reflectance data. PhytoFrontiers <a href="https://doi.org/10.1094/PHYTOFR-06-24-0063-SC">https://doi.org/10.1094/PHYTOFR-06-24-0063-SC</a>.</li> <li>Kim, J.-H., Y.-H. Cho, K.-M. Kim, C.-Y. Lee, W. S. Lee, and J.-S. Nam. 2025. Sweet potato farming in the USA and South Korea: A comparative study of cultivation pattern and mechanization status. Journal of Biosystems Engineering (2025) 50:210–224. <a href="https://doi.org/10.1007/s42853-025-00260-5">https://doi.org/10.1007/s42853-025-00260-5</a>.</li> <li>Huang, Z., W. S. Lee, P. Yang, Y. Ampatzidis, S. Agehara, and N. A. Peres. 2025. Advanced canopy size estimation in strawberry production: a machine learning approach using YOLOv11 and SAM. Computers and Electronics in Agriculture 236 (2025), 110501. <a href="https://doi.org/10.1016/j.compag.2025.110501">https://doi.org/10.1016/j.compag.2025.110501</a>.</li> <li>Huang, Z., W. S. Lee, P. Zhang, H. Jeon, and H. Zhu. 2025. SASP: segment any strawberry plant, an end-to-end strawberry canopy volume estimation. Smart Agricultural Technology 11 (2025) 101017. <a href="https://doi.org/10.1016/j.atech.2025.101017">https://doi.org/10.1016/j.atech.2025.101017</a>.</li> <li>Pardo-Beainy, C., C. Parra, L. Solaque, and W. S. Lee. 2025. Deep learning and georeferenced RGB-D imaging for hydroponic strawberry yield mapping. Smart Agricultural Technology 12 (2025) 101293. <a href="https://doi.org/10.1016/j.atech.2025.101293">https://doi.org/10.1016/j.atech.2025.101293</a>.</li> <li>Liu, S., Ampatzidis, Y., Zhou, C., & Lee, W. S. 2025. AI-driven time series analysis for predicting strawberry weekly yields integrating fruit monitoring and weather data for optimized harvest planning. Computers and Electronics in Agriculture, 233, 110212.</li> <li>Uthman, Q.O., R. Muñoz-Carpena, A. Ritter, and D.M. Kadyampakeni. 2025. Differential water and imidacloprid transport under unsaturated Florida citrus field conditions. Vadose Zone J. https://doi.org/10.1002/vzj2.70043</li> <li>Fort, J., V. Sharma, M. Dukes, S. Agehara, D. Kadyampakeni, and C.A. Chase. 2025. Micro-irrigation systems for water conservation during establishment and freeze protection in Florida strawberry production. Sci. Hortic. https://doi.org/10.1016/j.scienta.2025.114368</li> <li>De Souza Junior, J.P., and D.M. Kadyampakeni. 2025. Differential nutrient dynamics in cowpea and sunn hemp under organic orange peel powder fertilization. Nutr. Cycl. Agroecosyst. <a href="https://doi.org/10.1007/s10705-025-10432-6">https://doi.org/10.1007/s10705-025-10432-6</a></li> <li>Barbosa, I.J., J.P. Souza Junior, M. Garcia Costa, J.C. Barbosa Lúcio, D.M. Kadyampakeni, P.L. Gratão, L. Bianco de Carvalho, R. de Mello Prado, S. Bianco. 2025. Silicon-enhanced non-enzymatic antioxidant defense mechanisms in young orange trees under glyphosate-induced stress. BMC Plant Biology <a href="https://doi.org/10.1186/s12870-025-07054-z">https://doi.org/10.1186/s12870-025-07054-z</a></li> <li>Kadyampakeni, D.M., E. Scudiero, and R. Shrestha. 2025. Advances in precision irrigation management in the twenty-first century. Irrigation Science 43, XXX-XXX. <a href="https://doi.org/10.1007/s00271-025-01038-5">https://doi.org/10.1007/s00271-025-01038-5</a></li> <li>Costa, M.G., R. de Amorim Cordeiro, J.K. Rodrigues das Merces, L.S. de Medeiros, A.M. Cardoso, R. de Mello Prado, D.M. Kadyampakeni, M.T. Siqueira Lacerda, and J.P. de Souza JuniorP. 2025. Silicon can attenuate glyphosate-induced stress in young Handroanthus albus by improving photosynthetic efficiency and decreasing cellular electrolyte leakage. Scientific Reports 15:23077. <a href="https://doi.org/10.1038/s41598-025-07527-z">https://doi.org/10.1038/s41598-025-07527-z</a></li> <li>Sambani, D., A.A. Atta and D.M. Kadyampakeni. 2025. Evaluation of various fertilizer products for improved performance of HLB-affected citrus trees. J. Plant Nutr. 48(16):2882-2897. https://doi.org/10.1080/01904167.2025.2502146</li> <li>Kadyampakeni, D.M., R.G. Anderson, and M.P. Schmidt. 2025. Advancing salinity and nutrient management for irrigation science. Irrig. Sci. 43:321–327, <a href="https://doi.org/10.1007/s00271-025-01023-y">https://doi.org/10.1007/s00271-025-01023-y</a></li> <li>De Souza Junior, J.P., D.M. Kadyampakeni, M.A. Shahid, R. de Mello Prado, and J.L. Prieto FajardoG. 2025. Mitigating abiotic stress in citrus: the role of silicon for enhanced productivity and quality. Plant Stress 16:100837,</li> <li><a href="https://doi.org/10.1016/j.stress.2025.100837">https://doi.org/10.1016/j.stress.2025.100837</a></li> <li>Januarie, C.J. and D.M. Kadyampakeni. 2025. Nitrogen fertilization dynamics on one-year-old Dendrocalamus asper (Schult. & Schult.f.) Backer bamboo in Florida, Adv. Bamboo Sci. 11:100150. <a href="https://doi.org/10.1016/j.bamboo.2025.100150">https://doi.org/10.1016/j.bamboo.2025.100150</a>.</li> <li>Bautista, A.S., A. Agenjos, A. Martínez, A.I. Escudero, P. García-Arizo, R. Simeón, C. Meyer, and D.M. Kadyampakeni. 2025. Osmolyte regulation as an avocado crop management strategy for improving productivity under high temperature. Horticult. 11, 245. <a href="https://doi.org/10.3390/horticulturae11030245">https://doi.org/10.3390/horticulturae11030245</a>.</li> <li>Brewer, M., S.L. Strauss, R. Kanissery, and D.M. Kadyampakeni. 2025. The impacts of legume and non-legume cover crops on the performance of HLB-affected citrus trees. Journal of Plant Nutrition, 48(13):2235-2249. <a href="https://doi.org/10.1080/01904167.2025.2474031">https://doi.org/10.1080/01904167.2025.2474031</a>.</li> <li>Sambani, D., T. Vashish, D.B. Bright, and D.M. Kadyampakeni. 2025. The influence of soil pH on citrus root morphology and nutrient uptake efficiency. HortSci. 60(5):657–666. <a href="https://doi.org/10.21273/HORTSCI18486-25">https://doi.org/10.21273/HORTSCI18486-25</a></li> <li>Brewer, M., S.L. Strauss, C. Chase, B. Sellers, D.M. Kadyampakeni, E. van Santen, and R. Kanissery. 2025. Effects of cover crops on weed suppression in the inter-row of citrus orchards. Weed Sci. 73(e15):1-11. https://doi.org/10.1017/wsc.2024.72</li> <li>Atta, A.A., K.T. Morgan, S. Hamido, and D.M. Kadyampakeni. 2025. Irrigation optimization enhances water management and tree performance in commercial citrus groves on sandy soil. Irri. Sci. 43:329–346, <a href="https://doi.org/10.1007/s00271-024-00938-2">https://doi.org/10.1007/s00271-024-00938-2</a></li> <li>Liu, D., Li, Z., Wu, Z., and C. Li. 2024. Digital Twin/MARS-CycleGAN: Improved object detection for MARS phenotyping robot.  Journal of Field Robotics. <a href="https://doi.org/10.1002/rob.22473">https://doi.org/10.1002/rob.22473</a></li> <li>Jiang, L., L. Fu, and C. Li. 2024. Apple tree architectural trait phenotyping with organ-level instance segmentation from point cloud. Computers and Electronics in Agriculture. 229, 109708.</li> <li>Rodriguez-Sanchez, J., J. Snider, K. Johnsen, and C. Li. 2024. Spatiotemporal registration of terrestrial laser scanning data for time-series field phenotyping. Frontiers in Plant Science. 15: 1436120.</li> <li>Petti, D., S. Li, and C. Li. 2024. Graph neural networks for lightweight plant organ tracking. Computers and Electronics in Agriculture. 225: 109294.</li> <li>Tan, C., J. Sun, A. Paterson, H. Song, C. Li. 2024. Three-View Cotton Flower Counting through Multi-Object Tracking and RGB-D Imagery. Biosystems Engineering. 246: 233-247</li> <li>Chen, Y., Xiao, Z., Pan, Y., Zhao, L., Dai, H., Wu, Z., Li, C., Zhang, T., Li, C., Zhu, D. and Liu, T., 2024. Mask-Guided Vision Transformer for Few-Shot Learning. IEEE Transactions on Neural Networks and Learning Systems. <a href="https://doi.org/10.1109/TNNLS.2024.3418527">https://doi.org/10.1109/TNNLS.2024.3418527</a>.</li> <li>Hou, J., X. Wang, B. Park. C. Li. 2024. A multiscale computation study on bruise susceptibility of blueberries from mechanical impact. Postharvest Biology and Technology. 208: 112660.</li> <li>Ye Chu, Josh Clevenger, Kendall Lee, Jing Zhang, Changying Li. Genetic breeding to improve freeze tolerance in blueberries, a review. Horticulturae. 11(6).</li> <li>Monica Borghi, C. Li, et al. 2025. Enhancing entomophilous pollination for sustainable crop production. Horticultural Research. 122(4), e70234.</li> <li>Tan, C., C. Li, P. Perkins-Veazie, H. Oh, R. Xu, M. Iorizzo. 2025. High throughput assessment of blueberry fruit internal bruising using deep learning models. Frontiers in Plant Science. 16, 1575038.</li> <li>Li, Z., R. Xu, C. Li, L. Fu. 2025. Visual Navigation and Crop Mapping of a Phenotyping Robot MARS-PhenoBot in Simulation. Smart Agricultural Technology. 11:100910.</li> <li>Li, Z., R. Xu, C. Li, P. Munoz, F. Takeda, and B. Leme. 2025. In-field blueberry fruit phenotyping with a MARS-PhenoBot and customized BerryNet. Computers and Electronics in Agriculture. 232, 110057.</li> </ul>   Conference Papers/Presentations/Posters <ul> <li>Huang, Z., and W. S. Lee. 2025. A state space model with tree topology for strawberry detection. AI in Agriculture Conference: The Role of AI in Autonomous Agricultural Systems and Socioeconomic Effects. Starkville, MS, March 31 to April 2, 2025.</li> <li>Liu S., Ampatzidis Y., Lee W.S., Zhou C., 2025. Optimizing strawberry harvest planning through machine vision and AI-enabled predictive analytics. AI in Agriculture Conference: The Role of AI in Autonomous Agricultural Systems and Socioeconomic Effects. Starkville, MS, March 31 to April 2, 2025.</li> <li>Huang, Z., W.S. Lee, and M. Le. 2025. AI-driven plant tracking and segmentation for precise canopy estimation in strawberry field. ASABE Paper No. 202500347. St. Joseph, MI.: ASABE.</li> <li>Kamsikiri, K., A. Atta and D.M. Kadyampakeni. 2025. Comparison of conventional drip and microspinkler irrigation in citrus production systems on Florida sandy soils. ASABE Annual Meeting Conference Proceedings, Toronto, Canada. pp 1-13. <a href="https://doi.org/10.13031/aim.202500888">https://doi.org/10.13031/aim.202500888</a></li> <li>Kamsikiri K., A. Atta, and D.M. Kadyampakeni. 2025. Comparison of Conventional and Drip Irrigation Systems for Young HLB-Affected Citrus Trees. 136th Florida State Horticultural Society (FSHS) Annual Meeting, June 8-10, 2025. Bonita Springs, FL. (volunteered)</li> <li>Bonda L.F. and D.M. Kadyampakeni. 2025. Impact of nitrogen rates on growth and biomass accumulation of young macadamia trees. 136th FSHS Annual Meeting, June 8-10, 2025. Bonita Springs, FL. (volunteered)</li> <li>Thompson, T.R. and D.M. Kadyampakeni. 2025. Impacts of nutrient ratios of calcium and zinc on citrus growth and root development. 136th FSHS Annual Meeting, June 8-10, 2025. Bonita Springs, FL. (volunteered)</li> <li>de Souza Junior, J.P. and D.M. Kadyampakeni. 2025. Comparative analysis of stabilized and non-stabilized silicon sources on photosynthetic performance of young orange trees. 136th FSHS Annual Meeting, June 8-10, 2025. Bonita Springs, FL. (volunteered)</li> <li>Ritenour, M.A., A.A. Atta, C. Hu, A. Beany, and D.M. Kadyampakeni. 2025. Tree nutrition effects on postharvest fruit quality and shelf life of ‘Hamlin’ sweet orange. 136th FSHS Annual Meeting, June 8-10, 2025. Bonita Springs, FL. (volunteered)</li> <li>Iqbal, S., D. Kadyampakeni and M.A. Shahid. 2025. Developing site-specific recommendations on nitrogen application rates and timing for satsuma mandarin production in north Florida. 136th FSHS Annual Meeting, June 8-10, 2025. Bonita Springs, FL. (volunteered)</li> <li>Atta A., K.T. Morgan, M.A. Ritenour, M.A. Shahid, A. Wright, K. Morgan, M. Zekri, C. Oswalt, D. Williams and D.M. Kadyampakeni. 2025. The impact of phosphorus rates on HLB-affected tree health and performance in sandy soils. 136th FSHS Annual Meeting, June 8-10, 2025. Bonita Springs, FL. (volunteered)</li> <li>Basar N.U., M.A. Shahid and D.M. Kadyampakeni. 2025. Impact of variable nitrogen rates on the growth and yield of HLB-affected sweet orange trees. 136th FSHS Annual Meeting, June 8-10, 2025. Bonita Springs, FL. (volunteered)</li> <li>Prieto Fajardo, J.L.G, M. Shahid, W. Hammond, L. Diepenbrock and D.M. Kadyampakeni. 2025. Exploring the potential of silicon nanoparticles to mitigate water stress in citrus. 136th FSHS Annual Meeting, June 8-10, 2025. Bonita Springs, FL. (volunteered)</li> <li>Basar, N.U., M.A. Shahid and D.M. Kadyampakeni. 2025. Evaluating the impact of biostimulants on young sweet orange trees grafted onto different rootstocks. 17th SWES Research Forum, Gainesville, FL. Feb. 10, 2025. (volunteered)</li> <li>Peddapuli, M., A. AttaP and D.M. Kadyampakeni. 2025. Optimizing nitrogen and phosphorus management for HLB-affected sweet orange. 17th SWES Research Forum, Gainesville, FL. Feb. 10, 2025. (volunteered)</li> <li>Agunbiade, L.GM. Nunes and D.M. Kadyampakeni. 2025. Assessing the effects of varying rates of irrigation and potassium fertilization on the growth of Dendrocalamus asper bamboo in Florida. 17th SWES Research Forum, Gainesville, FL. Feb. 10, 2025. (volunteered)</li> <li>Sambani, D., T. Vashisth and D.M. Kadyampakeni. 2025. The influence of soil pH on citrus root morphology and nutrient uptake efficiency. 17th SWES Research Forum, Gainesville, FL. Feb. 10, 2025. (volunteered)</li> <li>Prieto, J. and D.M. Kadyampakeni. 2025. Exploring the potential of silicon nanoparticles to mitigate water stress in citrus. 17th SWES Research Forum, Gainesville, FL. Feb. 10, 2025. (volunteered)</li> <li>Saleem, Y., S. Agehara and D.M. Kadyampakeni. 2025. Effects of reclaimed water on blueberry seedling growth and root morphology. 17th SWES Research Forum, Gainesville, FL. Feb. 10, 2025. (volunteered)</li> <li>Kamsikiri, K. and D.M. Kadyampakeni. 2025. Optimizing molybdenum fertilization for young HLB-affected citrus trees. 17th SWES Research Forum, Gainesville, FL. Feb. 10, 2025. (volunteered)</li> <li>Thompson, T. and D.M. Kadyampakeni. 2025. Impacts of nutrient ratios of calcium and zinc on citrus growth and root development. 17th SWES Research Forum, Gainesville, FL. Feb. 10, 2025. (volunteered)</li> <li>Januarie, C.J., L. Sharma, J. Vendramini, and D.M. Kadyampakeni. 2025. Variable rate fertilization of phosphorus in young Dendrocalamus asper bamboo in Florida. 17th SWES Research Forum, Gainesville, FL. Feb. 10, 2025. (volunteered)</li> </ul> <ul> <li>Petti, D. and C. Li. Active Learning for Real-Time Flower Counting with a Ground Mobile Robot. ASABE Annual International Meeting. Paper Number: 202400607. July 28-31, 2024, Anaheim, California.</li> <li>Muller. J, D. Petti, C. Li, S. Gorucu, M. Pilz, and B. Weichelt. Investigating the use of large language models in agricultural injury surveillance. ASABE Annual International Meeting. Paper Number: 202400572. July 28-31, 2024, Anaheim, California</li> </ul>   Georgia: <ul> <li>Rayamajhi, A., Lu, G., Tollner, E. W., Williams-Woodward, J., & Mahmud, M. S. (2025). Assessing ornamental tree maturity and spray requirements using depth sensing and LiDAR technologies. Smart Agricultural Technology, 101120.</li> <li>Rayamajhi, A., Jahanifar, H., & Mahmud, M. S. (2024). Measuring ornamental tree canopy attributes for precision spraying using drone technology and self-supervised segmentation. Computers and Electronics in Agriculture, 225, 109359.</li> <li>Rayamajhi, A. (2024). Precision Sprayer Technologies for Enhanced Ornamental Crop Management (Master's thesis, University of Georgia).</li> </ul>   Kentucky: <ul> <li>Dvorak, J. (2025). AI Tools and Text Embedding for Session Organization at ASABE’s Annual International Meeting. Journal of the ASABE, (in press). https://doi.org/10.13031/ja.16328</li> <li>Dvorak, J. (2024). Utilizing Super Capacitors to Improve Battery Performance in Electric Mobile Machinery. Applied Engineering in Agriculture, 40(6), 669–677. https://doi.org/10.13031/aea.16062</li> <li>Dvorak, J., Smith, B.*. (2024). Powering In-Field Continuous Robotic Systems Using Solar Energy Systems. 67(3): 617-630. Journal of the ASABE. doi: 10.13031/ja.15579</li> </ul>   Michigan: Journal Publications <ul> <li>Xu, J., Lu, Y., 2025. 3D vision-based perception and length estimation of green asparagus for selective harvesting. Journal of the ASABE 68 (2), 239-256.</li> <li>Lu, Y., Mohammadi, P., 2025. Automated asparagus harvesting technology: A review of research and developments since the 1950s in the United States and beyond. Computers and Electronics in Agriculture 237, 110744</li> <li>Deng, B., Lu, Y., Vander Weide, J., 2025. Development and preliminary evaluation of a YOLO-based fruit counting and maturity evaluation mobile application for blueberries. Applied Engineering in Agriculture 41(3), 391-399.</li> <li>Deng, B., Lu, Y., 2025. Weed image augmentation by ControlNet-added stable diffusion for multi-class weed detection. Computers and Electronics in Agriculture 232, 110123.</li> <li>Mu, X., Y. Lu, 2025. Non-destructive detection of spotted wing Drosophila infestation in blueberry fruit using hyperspectral imaging technology. Agricultural Communications 3(3), 100096.</li> <li>Xu, J., Lu, Y., Deng, B., 2024. Design, prototyping, and evaluation of a new machine vision-based automated sweetpotato grading and sorting system. Journal of the ASABE 67 (5), 1369-1380.</li> <li>Deng, B., Lu, Y., Li, Z., 2024. Detection, counting, and maturity assessment of blueberries in canopy images using YOLOv8 and YOLOv9. Smart Agricultural Technology 9, 100620.</li> <li>Wade, C., Check, J., Chilvers, M., Dong, Y., (2025). Monitoring leaf wetness dynamics in corn and soybean fields using an IoT (Internet of Things)-based monitoring system. Smart Agricultural Technology. 11, 100919.</li> <li>Spafford, J., Hausbeck, M., Werling, B., Tucker, S., Dong, Y., (2025). Development of an Internet of Things (IoT)-Based Disease Forecaster to Manage Purple Spot On Asparagus Fern. Smart Agricultural Technology. 11, 100941.</li> <li>Kumari, S., Ali, N., Dagati, M., Dong, Y. (2025). IoT-Enabled Soil Moisture and Conductivity Monitoring Under Controlled and Field Fertigation Systems. AgriEngineering. 7 (7). 207.</li> <li>Dong, Y., Tucker, S., Singh, G., Ali, N., Yazdanpanah, N., Vander Wide, J., Sears, M., (2025). Optimizing Soil Moisture Sensor Placement Through Spatial Variability Analysis in Orchards. Smart Agricultural Technology. 10. 101273</li> </ul>   Conference Papers/Other Publications <ul> <li>Mu, X., Lu, Y., Deng, , 2025. A comparative benchmark of real-time detectors for blueberry detection towards precision orchard management. arXiv preprint: 2509.20580.</li> <li>Xu, J., Lu, Y., 2025. Development and evaluation of a multispectral vision-based automated sweet potato sorting system. Sensing for Agriculture and Food Quality and Safety XVII, 1348402. </li> <li>Deng, B., Lu, Y., Brainard, D., 2025. Semi-Supervised Weed Detection in Vegetable Fields: In-domain and Cross-domain Experiments. arXiv preprint:2502.17673. Paper presented at the 2025 AgriControl Conference.</li> <li>Deng, B., Lu, Y., 2025. Weed image augmentation by IP-adapter-based stable diffusion for multiclass weed detection. Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications III; 1345909.</li> <li>Deng, B., Lu, Y., Brainard, Improvements and Evaluation of a Smart Sprayer Prototype for Weed Control in Vegetable Crops. 2025 ASABE Annual International Meeting 2500323</li> <li>Singh, N., Lu, Y., 2025. Development and laboratory assessment of cutting and snapping mechanisms for green asparagus harvesting. 2025 ASABE Annual International Meeting 2500323.</li> </ul>   Mississippi: <ul> <li>Li, Jiajia, Xinda Qi, Seyed Hamidreza Nabaei, Meiqi Liu, Dong Chen, Xin Zhang, Xunyuan Yin, and Zhaojian Li. "A survey on 3D reconstruction techniques in plant phenotyping: from classical methods to neural radiance fields (NeRF), 3D Gaussian splatting (3DGS), and beyond." arXiv preprint arXiv:2505.00737 (2025).</li> <li>Chen, Dong, and Yanbo Huang. "Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm." Computers and Electronics in Agriculture (2025).</li> <li>A Comparative Study of Deep Reinforcement Learning for Crop Production Management Joseph Balderas, Chen, Dong, Yanbo Huang, Li Wang, Ren-Cang Li, Smart Agriculture Technology, 2025</li> <li>Seyed Hamidreza Nabaei, Ryan Lenfant, Viswajith Govinda Rajan, Dong Chen, Michael P Timko, Bradford Campbell, Arsalan Heydarian, “Detecting Plant VOC Traces Using Indoor Air Quality Sensors”, Indoor Air (2025)</li> </ul>   New York: <ul> <li>Kanaley, K., Murdock, M.J., Qiu, T. et al. Maui: modular analytics of UAS imagery for specialty crop research. Plant Methods 21, 65 (2025). <a href="https://doi.org/10.1186/s13007-025-01376-7">https://doi.org/10.1186/s13007-025-01376-7</a></li> <li>Liu, E., Gold, K. M., Cadle‐Davidson, L., Kanaley, K., Combs, D., & Jiang, Y. (2025). PhytoPatholoBot: Autonomous Ground Robot for Near‐Real‐Time Disease Scouting in the Vineyard. Journal of Field Robotics.</li> <li>Kanaley, K., Combs, D. B., Paul, A., Jiang, Y., Bates, T., & Gold, K. M. (2024). Assessing the capacity of high-resolution commercial satellite imagery for grapevine downy mildew detection and surveillance in New York state. Phytopathology®, 114(12), 2536-2545.</li> <li>Kanaley, K., Murdock, M., Liu, E., Romero, F., Paul, A., Chadwick, D., ... & Gold, K. (2024, December). Leveraging Thermal, Multi-and Hyperspectral UAS Data to Monitor Disease Impacts on Grapevine Physiology. In AGU Fall Meeting Abstracts (Vol. 2024, pp. GC32A-01).</li> <li>Yu, J., Jakubowski, R., Bates, T., Jiang, Y., & Chen, C. (2024). Integrating Hyperspectral Imaging and Machine Learning for Non-Destructive Damage Detection of Grapes. In 2024 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.</li> </ul>   Pennsylvania: Journal Articles: <ul> <li>Pawikhum, K., Yang, Y., He, L., & Heinemann, P. (2025). Development of a machine vision system for apple bud thinning in precision crop load management. Computers and Electronics in Agriculture 236, 110479.</li> <li>Hua, W., Heinemann, P., & He, L. (2025). Frost management in agriculture with advanced sensing, modeling, and artificial intelligent technologies: A review. Computers and Electronics in Agriculture 231, 110027.</li> <li>Li, J., Lammers, K., Yin, X., Yin, X., He, L., Sheng, J., Lu, R., & Li, Z. (2025). MetaFruit meets foundation models: Leveraging a comprehensive multi-fruit daaset for advancing agricultural foundation models. Computers and Electronics in Agriculture 231, 109908.</li> <li>Kang, C., Mu, X., Seffrin, A.N., Di Gioia, F., & He, L. (2025). A recursive segmentation model for bok choy growth monitoring with Internet of Things (IoT) technology in controlled environment agriculture. Computers and Electronics in Agriculture 230, 109866.</li> <li>Hua, W., He, L., Heinemann, P., & Zhu, M. (2025). Precision heating strategy based dynamic heater path planning for frost protection in apple orchards. Biosystems Engineering 250: 117-132.</li> <li>Yang, Y., Mali, P., Arthur, L., Molaei, F., Atsyo, S., Geng, J., He, L., & Ghatrehsamani, S., (2025). Advanced technologies for precision tree fruit disease management: A review. Computers and Electronics in Agriculture 229, 109704.</li> <li>Pawikhum, K., He, L., Heinemann, P., & Bock, R.G. (2025). Design of End-Effectors for Thinning Apple in the Green Fruit Stage. Journal of the ASABE 68(2), 465-476.</li> <li>Majeed, Y., Fu, L., & He, L. (2024). Artificial intelligence-of-things (AIoT) in precision agriculture. Frontiers in Plant Science 15, 1369791.</li> <li>Kang, C., He, L., & Zhu, H. (2024). Assessment of spray patterns and efficiency of an unmanned sprayer used in planar growing systems. Precision Agriculture 25(5), 2271-2291.</li> <li>Hussain, M., He, L., Schupp, J., Lyons, D., & Heinemann, P. (2024). Green fruit‐stem pairing and clustering for machine vision system in robotic thinning of apples. Journal of Field Robotics 2024: 1-28.</li> <li>Hua, W., Heinemann, P., & He, L. (2024). Heat transfer modeling with fixed and mobile heaters for frost protection in apple orchards. Computers and Electronics in Agriculture 227, 109525.</li> <li>Mu, X., He, L., Heinemann, P., Schupp, J., Karkee, M., & Zhu, M. (2024). UGV‐based precision spraying system for chemical apple blossom thinning on trellis trained canopies. Journal of Field Robotics 2024: 1-12.</li> </ul> Extension Articles: <ul> <li>Kang, C., He, L., and Peter, K. (2024). A low-cost microclimate monitoring system for orchard disease management. Pennsylvania Fruit News. </li> <li>Arthur, L., Brunharo, C., Hussain, M., and He, L. (2024). Precision weed management in tree fruit orchards. Pennsylvania Fruit News.</li> <li>Mahnan, S., He, L., and Pecchia, J. (2025). Overview of button mushroom harvesting technologies. Penn State Extension.</li> </ul> Thesis and Dissertation: <ul> <li>Basnet, Akash. (2024). Design of machine vision system and an end-effector for robotic apple harvesting in orchards. MS Thesis. The Pennsylvania State University.</li> <li>Yang, Yanqiu. (2025). Innovative integrated pest management: Data-driven approaches and noninvasive technologies for enhanced monitoring and decision-making in specialty crops. PhD Dissertation. The Pennsylvania State University.</li> <li>Geng, Jiarui. (2025). Multi-system framework for insect monitoring: From laboratory imaging to field applications. MS Thesis. The Pennsylvania State University.</li> </ul>   Texas: <ul> <li>Kuruppuarachchi, Chamika, Fnu Kulsoom, Hussam Ibrahim, Hamid Khan, Azlan Zahid, and Mazhar Sher. 2024. “Advancements in Plant Wearable Sensors.” Computers and Electronics in Agriculture 229: 109778.</li> <li>Majeed, Yaqoob, Mike O. Ojo, and Azlan Zahid. 2024. “Standalone Edge AI-Based Solution for Tomato Diseases Detection.” Smart Agricultural Technology 9: 100547.</li> <li>Ikram, Muhammad, Sikander Ameer, Fnu Kulsoom, Mazhar Sher, Ashfaq Ahmad, Azlan Zahid, and Young Chang. 2024. “Flexible Temperature and Humidity Sensors of Plants for Precision Agriculture: Current Challenges and Future Roadmap.” Computers and Electronics in Agriculture 226:109449.</li> <li>Bashir, Al, Yaqoob Majeed, and Azlan Zahid. 2024. “Development of an End-Effector for Robotic Harvesting of Hydroponic Lettuce.” In 2024 ASABE Annual International Meeting, Paper Number: 2400401 doi:10.13031/aim.202400401</li> <li>Ojo, Mike O., Azlan Zahid, and Joseph G. Masabni. 2024. “Estimating Hydroponic Lettuce Phenotypic Parameters for Efficient Resource Allocation.” Computers and Electronics in Agriculture 218: 108642.</li> </ul>