Brief Summary of Minutes of Annual Meeting

S1090: OVERVIEW OF THE GROUP’S OUTPUTS AND ACCOMPLISHMENTS FOR THE YEAR IN-VIEW

            The S1090 multistate project, established in 2021, continues to strengthen collaborative efforts among land-grant universities, non-land-grant institutions, federal partners, industry stakeholders, and international collaborators with a shared focus on Artificial Intelligence (AI) and Digital Agriculture. What began as a regional collaboration among institutions in the southeastern United States has continued to expand into a nationwide interdisciplinary network that promotes collaborative research, technology development, workforce training, stakeholder engagement, and extension activities in digital agriculture and AI-enabled agricultural systems.

            This report summarizes the activities and accomplishments of the S1090 project during the 2025–2026 reporting period, corresponding to the fifth year of the current project cycle. Guided by the three primary objectives outlined in the proposal, participating members continued to foster cross-disciplinary and multistate collaborations integrating expertise in agronomy, engineering, animal science, computer science, robotics, geospatial analytics, environmental science, sensing technologies, and data science to address emerging agricultural challenges related to sustainability, climate resilience, labor shortages, automation, and resource-use efficiency. Members contributed to project objectives both individually and through collaborative multistate and multi-institutional research, extension, and outreach activities involving universities, USDA agencies, commodity organizations, agricultural machinery industries, sensing technology developers, automation companies, and producer networks.

            During the reporting period, participating institutions engaged in collaborative research projects spanning crop production, livestock systems, phenotyping and genotyping, autonomous systems, natural resource monitoring, agricultural robotics, data infrastructure, and precision agriculture technologies. Research activities emphasized the development and application of AI, machine learning, robotics, remote sensing, digital twins, computer vision, automation, and advanced data analytics to support real-time monitoring, predictive modeling, autonomous operations, and data-driven agricultural decision making. Significant efforts also focused on the development of standardized datasets, open-source software tools, benchmark platforms, interoperable analytical frameworks, and publicly accessible decision-support systems designed to strengthen collaborative AI research and technology transfer in agriculture.

            Technology transfer, stakeholder engagement, and workforce development remained central priorities of the S1090 network throughout the reporting period. Participating institutions organized seminars, workshops, conferences, field demonstrations, online educational programs, and extension activities aimed at increasing awareness, accessibility, and adoption of AI-enabled agricultural technologies. Collectively, the project supported the mentorship and training of 20 postdoctoral researchers, 5 visiting scientists, 70 PhD students, 56 MS students, 21 research assistants/associates, and 78 undergraduate students. In addition, extension and outreach activities engaged approximately 1,475 farmers, growers, and aggregators, 637 workshop participants, and 1,165 K–12 students through educational programs, demonstrations, field tours, and stakeholder engagement activities. Across all activities, the S1090 project directly reached approximately 4,900 participants.

            Research productivity and scholarly output remained strong across participating institutions. During the reporting period, project participants collectively produced more than 200 peer-reviewed publications, delivered over 100 conference and professional presentations, and generated more than five patents and intellectual property outputs related to AI, sensing systems, robotics, automation, and digital agriculture technologies. These accomplishments demonstrate the continued growth, scientific leadership, and national impact of the S1090 multistate network in advancing collaborative AI and Digital Agriculture research, strengthening interdisciplinary partnerships, supporting workforce development, and accelerating the adoption of innovative technologies across agricultural and natural resource systems.

MILESTONES AND IMPACT SUMMARY ACCORDING TO PROJECT

OBJECTIVE 1A: AI tools for crop (Agrifood) and animal production

            Researchers within the S1090 multistate project continued to advance artificial intelligence (AI), machine learning (ML), computer vision, robotics, digital twins, and sensor-based technologies to improve crop, livestock, food, and integrated agricultural production systems. Participating institutions developed scalable AI-enabled tools for crop monitoring, precision nutrient and irrigation management, disease and pest detection, autonomous sensing, livestock monitoring, food quality assessment, and climate-resilient agricultural decision support. These efforts collectively demonstrate the growing national capacity for integrating AI-driven technologies into agricultural production systems to improve efficiency, sustainability, resilience, and profitability.

            A major strength of the S1090 network under this objective was the extensive collaboration among land-grant universities, USDA agencies, industry partners, and international collaborators. Multi-institution projects integrated expertise in agronomy, engineering, crop physiology, animal science, computer science, geospatial analytics, and data science to address emerging agricultural challenges. Collaborative efforts included partnerships among LSU and Texas A&M on rice nitrogen use efficiency and pest mitigation systems; LSU, UC Riverside, and USDA collaborators on UAV-based crop monitoring and sugarcane analytics; Purdue University and Texas A&M on digital twin applications; TAMU and SDSU on AI-enabled beef supply chain and livestock decision support systems; University of Kentucky and University of Arkansas on hyperspectral imaging applications for food quality and diagnostics; and international collaborations involving institutions in Ireland, Spain, Canada, and the United Kingdom focused on livestock monitoring, environmental modeling, and agricultural AI systems. Research activities under this objective emphasized the development of AI-enabled crop monitoring and management systems using UAVs, satellites, hyperspectral imaging, thermal sensing, soil sensors, and geospatial analytics. Projects addressed nitrogen management, irrigation scheduling, methane mitigation, crop stress detection, yield forecasting, soil carbon monitoring, pest and disease identification, and climate-smart production strategies across major commodity and specialty crops including rice, soybean, corn, sugarcane, strawberry, peanut, asparagus, chestnut, blueberry, and faba bean. Several projects combined remote sensing with deep learning, explainable AI, mechanistic modeling, and digital twin frameworks to enable real-time, in-season decision support and predictive agricultural management.

            Researchers also advanced AI applications for livestock, poultry, dairy, aquaculture, and food systems through machine vision, behavior analytics, automated monitoring, and intelligent decision-support platforms. Projects focused on cattle wellness monitoring, antibiotic stewardship, feed optimization, methane emission reduction, poultry welfare assessment, swine body condition monitoring, piglet activity tracking, automated poultry inspection systems, dairy cattle thermal stress monitoring, and aquaculture automation. These efforts support the transition toward precision livestock farming systems capable of improving animal health, welfare, productivity, and sustainability while reducing labor demands and operational inefficiencies. Machine vision and agricultural automation represented another major research theme under this objective. Participating institutions developed AI-based systems for weed detection, targeted spraying, automated grading and sorting, robotic harvesting support, crop quality assessment, and agricultural automation. Research efforts included real-time object detection, hyperspectral imaging, multimodal sensing, robotic perception, and autonomous field operations for specialty crops, food processing, and production systems. These technologies contribute to reducing labor dependency, improving operational precision, and enabling scalable automation solutions for modern agriculture. Several institutions also expanded the use of digital twins, hybrid AI-mechanistic models, and climate-smart agricultural systems. Projects focused on environmental sustainability, carbon monitoring, methane mitigation, evapotranspiration-based irrigation management, nutrient monitoring in hydroponic systems, and climate-service ecosystems for farm-level decision support. The integration of AI with mechanistic crop and environmental models demonstrated the increasing maturity of digital agriculture systems capable of supporting predictive and prescriptive agricultural management under variable climatic and production conditions.

OBJECTIVE 1B: AI tools for autonomous system perception, localization, manipulation, and planning for agroecosystems.

            Activities under this objective focused on the advancement of autonomous agricultural technologies through AI-enabled robotics, intelligent sensing platforms, machine vision systems, and autonomous navigation tools designed for crop, livestock, and environmental applications. Participating institutions combined expertise in robotics, agricultural engineering, computer vision, autonomy, sensing systems, and artificial intelligence to develop next-generation solutions for agricultural production, environmental monitoring, and field-scale automation.        Strong collaboration among land-grant universities and multidisciplinary research teams played a central role in advancing autonomous field operations, robotic harvesting, precision application systems, and intelligent monitoring platforms. Collaborative efforts included projects among Purdue University and University of Arkansas on AI-enabled poultry robotics, University of Arkansas and multiple institutional partners on automated food safety and processing systems, and cross-institutional initiatives integrating robotics, machine vision, LiDAR, UAVs, neuromorphic vision, and autonomous navigation technologies.

            Research efforts emphasized autonomous crop monitoring, robotic harvesting, precision spraying, navigation and localization systems, and autonomous environmental sensing technologies. Projects included autonomous platforms for prescribed fire management, precision pest management in grain storage systems, irrigation and soil moisture mapping, invasive species removal, crop harvesting automation in cotton, sugarcane, berries, and sweet potato, as well as autonomous crop scouting and under-canopy monitoring systems. Several studies also advanced robotic manipulation technologies through adaptive grippers, machine vision-guided harvesting, and precision robotic interaction with delicate agricultural products.

            Additional developments included advanced perception and localization systems using LiDAR, simultaneous localization and mapping (SLAM), event-based neuromorphic vision, GPS-integrated sensing, and AI-enabled navigation frameworks for operation in complex agricultural environments. Other projects explored robotic interaction with animals, underground robotic sensing systems, dairy cattle welfare monitoring, and precision sensor implantation and root phenotyping technologies.

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OBJECTIVE 1C: Natural resources scouting and monitoring.

            Efforts under this objective centered on the application of artificial intelligence, spectroscopy, geospatial analytics, remote sensing, and advanced environmental sensing technologies to improve monitoring and management of soil, water, vegetation, and ecosystem health across agricultural and natural landscapes. Participating institutions developed data-driven approaches for soil property estimation, soil moisture prediction, methane emission monitoring, forest biomass assessment, disease surveillance, irrigation management, and carbon dynamics evaluation using machine learning, LiDAR, UAVs, spectroscopy, and multi-sensor fusion techniques. Collaborative projects among Clemson University, University of Georgia, Texas A&M University, Purdue University, University of Kentucky, UW–Madison, Oregon State University, New Mexico State University, and other partners emphasized scalable and interpretable AI frameworks for environmental monitoring under variable field conditions. Activities also included the development of automated web-based soil assessment tools, landscape-scale mapping systems, and real-time sensing platforms to support climate-smart agriculture, natural resource conservation, and sustainable ecosystem management.

OBJECTIVE 1D: Socioeconomic sustainability

Activities under this objective focused on integrating artificial intelligence, economic modeling, geospatial analytics, and controlled environment agriculture (CEA) to improve the long-term economic, social, and environmental sustainability of agricultural systems. Researchers developed decision-support frameworks for dairy breeding optimization and genomic selection economics while also exploring how CEA systems can be integrated into urban infrastructure to address food access, public health, environmental resilience, and community sustainability. Collaborative efforts among Texas A&M University, the University of Arkansas, Harvard University, and other partners emphasized the use of GeoAI, high-performance computing, and spatially targeted analytics to support adaptive crop management, resource-efficient food production, and data-driven interventions for underserved communities. These projects highlight the growing role of AI-enabled socioeconomic analysis and sustainable food system planning in addressing broader agricultural and societal challenges.

OBJECTIVE 1E: Phenotyping and genotyping

Research efforts under this objective leveraged artificial intelligence, UAV-based remote sensing, hyperspectral imaging, LiDAR, and high-throughput phenotyping technologies to enhance crop breeding and genetic improvement programs. Projects supported rapid trait evaluation, biomass and nutrient estimation, forage assessment, seed classification, and cultivar development across crops including sugarcane, winter cereals, wheat, cotton, rice, oats, energy cane, peanuts, forage grasses, and lima beans. Multi-location collaborations among universities, USDA partners, and breeding programs expanded the use of AI-enabled phenotyping platforms and data-driven breeding approaches, while also generating standardized datasets and analytical tools to support crop improvement efforts. These activities contributed to improving the accuracy, scalability, and efficiency of phenotyping and genotyping workflows for next-generation agricultural production systems.

OBJECTIVE 2A: Data curation, management, and accessibility, and security, ethics

Work conducted under this objective emphasized the development, organization, harmonization, and sharing of large-scale agricultural datasets to support artificial intelligence and digital agriculture applications across diverse production systems. Participating institutions focused on building open-source and multimodal datasets integrating RGB, NIR, depth imagery, hyperspectral sensing, climate records, soil measurements, crop model outputs, and field-validated observations for use in machine learning, computer vision, and predictive modeling applications. Collaborative efforts among Mississippi State University, University of Florida, University of California–Davis, Cornell University, University of Illinois, University of Kentucky, LSU AgCenter, industry partners, and other collaborators supported the development of benchmark datasets, data repositories, and standardized data collection workflows for agricultural autonomy, livestock monitoring, crop modeling, irrigation management, and environmental sensing. Activities also addressed challenges associated with temporal data alignment, multimodal data fusion, quality control, explainable AI integration, sparse data regions, and long-term climate and environmental data curation. Several projects additionally emphasized open-access dataset development and public data sharing to accelerate algorithm benchmarking, reproducibility, interoperability, and collaborative AI research within the agricultural community.

OBJECTIVE 2B: Standardization and testbed development – data standardization and software development.

Activities under this objective focused on developing standardized data collection platforms, benchmark datasets, software tools, and interoperable analytical frameworks to support scalable artificial intelligence and digital agriculture research. Participating institutions advanced open-source applications, web-based decision-support systems, machine learning-enabled soil assessment tools, and large-scale benchmark datasets designed to improve data consistency, accessibility, reproducibility, and cross-platform integration. Collaborative efforts among Texas A&M University, UC Davis, University of Florida, University of Georgia, Clemson University, and other partners emphasized the integration of agronomic, hydrologic, environmental, and mechanistic datasets across controlled-environment and multi-site field experiments to support AI-driven predictive modeling and precision management recommendations. Projects also contributed to the development of foundational datasets and software infrastructure for agricultural autonomy, poultry management, soil property estimation, and field-based research data collection, strengthening the national digital agriculture research ecosystem through improved standardization and shared technological resources.

OBJECTIVE 3: AI adoption (technology transfer) and workforce development

Technology transfer, stakeholder engagement, extension programming, and workforce training remained central components of the S1090 multistate activities aimed at increasing awareness, accessibility, and adoption of artificial intelligence and digital agriculture technologies. Participating institutions delivered seminars, workshops, conferences, online training programs, mobile applications, decision-support systems, and open-source software resources targeting researchers, students, producers, industry professionals, and extension personnel. Outreach initiatives included the AI in Agriculture Monthly Seminar at LSU AgCenter, Louisiana Precision Agriculture Summit, Purdue University’s nationally and internationally recognized Agronomy e-Learning program, climate and crop planning applications, and AI-driven fertilizer recommendation platforms. Several institutions also maintained public GitHub repositories, online datasets, and software tools to improve accessibility and support collaborative learning and technology deployment. Workforce development activities further expanded through interdisciplinary graduate courses, campus-wide AI agrifood initiatives, innovation and entrepreneurship programs, and specialized training in artificial intelligence, livestock systems, remote sensing, precision agriculture, and data analytics. These collective efforts contributed to building a skilled workforce and accelerating the practical adoption of AI-enabled technologies across agricultural and natural resource systems. The S1090 multistate project continued to strengthen workforce development, student training, stakeholder engagement, and technology transfer activities across participating institutions. Project activities provided interdisciplinary training opportunities in artificial intelligence, digital agriculture, sensing technologies, robotics, machine learning, remote sensing, and precision agriculture applications for students, researchers, producers, and industry stakeholders. Collectively, the project supported the training and mentorship of 20 postdoctoral researchers, 5 visiting scientists, 70 PhD students, 56 MS students, 21 research assistants/associates, and 78 undergraduate students. In addition, extension and outreach efforts engaged approximately 1,475 farmers, growers, and aggregators, 637 workshop participants, and 1,165 K–12 students through demonstrations, seminars, workshops, field activities, and educational programs. Across all activities, the S1090 project directly reached approximately 4,900 participants, highlighting the strong emphasis on workforce development, stakeholder engagement, and adoption of AI-enabled agricultural technologies.

List of projects according to the objectives and member institutions involved

OBJECTIVE 1A: AI tools for crop (Agrifood) and animal production

1. Sensor-based biomass prediction systems for monoculture forage crops to support precision livestock management (CLEM)
2. Morphological feature evaluation for automated forage yield prediction (CLEM)
3. Deep learning for automated and cotton fiber cross-section measurement (CLEM)
4. AI-driven tillage tool design using DEM simulation, neural networks & additive manufacturing
5. Characterizing rice N use efficiency with digital agriculture (Research, LSU, Texas A&M)
6. Development of decision tools for mitigating the impact of Mexican rice borer (Eoreuma Loftini) in gulf coast (Research, LSU, Texas A&M)
7. Harnessing UAV and machine learning technologies to promote resilient soybean and corn (Research, LSU, UC Riverside)
8. Sugarcane biomass and sucrose monitoring with UAV and artificial intelligence (Research, LSU, USDA New Orleans)
9. On-farm experimentation evaluating best management practices in corn-soybean-cotton, rice-soybean, rice-crawfish, and sugarcane cropping systems in Louisiana (Research, Extension, LSU)
10. From Leaves to Satellites: AI-Powered Estimation of Photosynthetic Capacity for NASA Missions (LSU)
11. Deep Learning-Enabled Detection of Salt Patches in Coastal Louisiana Farmland Using NASA HLS Data (LSU)
12. Optimizing Sugarcane Breeding Through Applied Genomic Selection, High-Throughput Phenotyping, and Trait-Targeted Nurseries (LSU)
13. Development of AI methods for strawberry disease detection and forecasting (LSU)
14. Artificial Intelligence for Stink Bug Detection in Precision Pest Management. Funded by Louisiana soybean and grain research and promotion board; Involved institutions: LSU AgCenter.
15. Detecting Insect Damaged Soybeans with Artificial Intelligence Solutions. Funded by Louisiana soybean and grain research and promotion board; Involved institutions: LSU AgCenter.
16. Capacity building in crop modeling, genetic coefficient calibration, and nitrogen leaching impact assessment to evaluate how cropland nitrogen dynamics influence Net Primary Production (NPP) of ocean waters and climate change impact scenarios. (MSU)
17. Mississippi Soybean – Climate-smart Irrigation Scheduling Tool (MS‑CIST) (MSU)
18. AI-based geospatial Modeling Approach to develop the Mississippi Carbon Monitoring Dashboard (MCMD) (MSU)
19. AI-enabled time-series temperature and rainfall prediction map across the Mississippi state. (MSU)
20. Evapotranspiration-based Decision Support System to enable climate-resilient sustainable water management strategies for Mississippi farms (MSU)
21. Baseline Methane Data and Mitigation Measures to quantify methane emissions under alternate wetting and drying (AWD) and flooded rice field management using eddy covariance towers, soil data, and remote sensing (MSU)
22. Multispectral machine vision applied to automat sweet potato grading and sorting (MSU, MS State University, NCSU, UIUC, and LSU)
23. Machine vision-based weed detection and control (MSU and University of Arizona)
24. Large-scale imagery dataset for blueberry decision and benchmarked state-of-the-art real-time object detectors.
25. AI-based 3D for detecting harvestable asparagus to support the development of selective asparagus harvesting technology.
26. Machine learning models were developed laser profiling technique for classifying normal and woody breast affected sample.
27. AI models for the recognition of catfish fillets on a conveyor line to support the development of automated catfish singulation technology, (MSU and MSU
28. Developing high-speed machine vision technology with AI for automated grading and sorting of green asparagus.
29. Develop AI models for the detection of on-ground chestnuts toward automated chestnut picking.
30. Develop an AI system to automatically direct birds to desired locations without human involvement. 
31. Piglet activity monitoring to reduce pre-weaning mortality  (Queens University Belfast, Teagasc in the Republic of Ireland, University of Nebraska, North Carolina State University, and Agriculture and Agri-food Canada. 
32. Feed Ration Formulation Tool Development for Sustainability and Methane Emission
33. Growing Fruit Trees in Digital Environments to Improve Resilience Against Labor and Climate Challenges
34. Climate Smart Practices for Agricultural Industry in Michigan to Enhance Environmental Sustainability and Economic Benefits
35. Farmscope: Strengthening the Climate Service Ecosystem for Farm-Level Decision Support.
36. Cross-tree transfer and diameter-based scaling through hybrid framework for optimizing sap flow sensors deployment
37. AIoT-based irrigation management to improve irrigation water use efficiency
38. AI-based approaches are being developed to determine the use of hyperspectral sensors to diagnose sudden death syndrome (SDS) of soybean (NDSU, SIU)
39. Identification of Sudden Death Syndrome using Hyperspectral Imaging and Deep Learning
40. Hyperspectral imaging (data for soybean plants inoculated with Fusarium virguliforme and non-inoculated (i.e., without the fungus) (NDSU)
41. Digital Twins in Agriculture (Purdue University)
42. Rapid detection of genetically modified crops (Purdue University)
43. Digital Twin in Agriculture (Texas A&M University, AgriLife Research)
44. Dairy Cattle Nutrition & Management (Purdue University)
45. Extracting information from video or images to improve dairy cattle nutrition and management. (Purdue University)
46. Integrating data from commercial farms to answer complex biological questions.
47. Soybean quality estimation (Purdue University) using satellite eimagery, AI and big data (Purdue University)
48. Digital twin for Indoor Farming, funded by KeySight Technologies and AI Institute for Food Systems UC Davis, UC ANR
49. Real-time Nutrient Monitoring and Management for Hydroponic Production funded by California Department of Food and Agriculture UC Davis, UC ANR
50. A hybrid model that combines AI with mechanistic modeling trained on a large dataset of leaf data from specialty crops UC Davis, UC ANR
51. The model inputs full range leaf spectral reflectance, and outputs 10 nutrient values plus leaf pigments and other bio-chemical traits UC Davis, UC ANR
52. WeedChat: An AI-Powered Chatbot to Answer Thorny and Weedy Questions UC Davis, UC ANR
53. Monitoring nitrogen stressed corn plants using hyperspectral imagery, custom multispectral imagery and real-time nitrogen side-dressing via a ground based robotic platform. SDSU
54. Estimating Foliar Nitrogen in Corn Using Radiative Transfer Modeling and Deep Learning from Hyperspectral Imagery SDSU
55. Development and Evaluation of Customized Unmanned Aerial System(UAS) for AI-driven Real-time Weed Detection and Variable Rate Targeted Spray Application SDSU
56. Quantifying fine-scale pasture contribution to growth based on utilization SDSU
57. Stakeholder-Driven Integrated Decision Modelling Platform for Blockchain Enabled Resilient Beef Supply Chains in U.S. TAMU, SDSU
58. Machine learning-guided feed additive optimization for dairy cows: Integrating profitability and diet variability into adoption decisions TAMU, SDSU
59. Precision Cattle Wellness Management by Understanding Their Vocal Communication TAMU
60. Artificial Intelligence-Driven Decision Tools for Antibiotic Stewardship in Texan Beef Systems TAMU
61. The Blueprint of BeefCerebro.ai, A Coordinated Innovation Network for AI-Enabled Decision Support in U.S. Beef Production TAMU
62. Development of crop mapping, monitoring, and management systems to support data-driven, in-season crop management decisions TAMU
63. Strategically Timed UAV Remote Sensing for Robust Wheat Yield Prediction: A Multi-year Study on Phenology-Aligned Machine Learning Dynamics LSU Rice Research Station, LSU Sugarcane Station, LSU Dean Lee Research Station, Wheat Breeding Center of Excellence-Amarillo TX, T&M College Station
64. Machine Learning-Based Predictions of Corn Yield and Soil Health Parameters in Cover-Cropped Systems with Variable N Inputs LSU Rice Research Station, LSU Sugarcane Station, LSU Dean Lee Research Station, Wheat Breeding Center of Excellence-Amarillo TX, T&M College Station
65. Characterizing Optimum N Rate in Waterlogged Maize (Zea mays L.) with Unmanned Aerial Vehicle (UAV) Remote Sensing LSU Rice Research Station, LSU Sugarcane Station, LSU Dean Lee Research Station, Wheat Breeding Center of Excellence-Amarillo TX, T&M College Station
66. AI Feces Inspection tool for caged poultry production system (TAMU)
67. Improve AI model generalization ability via background removal and transformer (TAMU)
68. Assess broiler preference on different grooved floor geometries using AI (TAMU)
69. AI-assisted breeding for anthracnose resistance in pepper
70. Developing Automated Machine Vision Systems for Non-Invasive Monitoring of Thermal Stress and Welfare in Dairy Cattle
71. Meat color prediction using hyperspectral imaging (HSI) with advanced machine learning. University of Kentucky and University of Arkansas
72. Developing HSI method for seed dormancy and viability detection with application in barley malting optimization process. University of Kentucky
73. Developing method for HSI data reconstruction from RGB data for food allergens detection. University of Kentucky
74. Epicmare: A Routine Screening Blood Test To Predict Gestational Complications and parturition Date In Mares And Other Farm Animals - Other participants: University of Kentucky, Luissianan State University, Spanish National Research Council (CSIC-León).
75. Portable AI-Assisted Microscopy for Equine Blood Cell Differentials and Gestational Health Prediction - Funding:
76. Machine Learning–Driven Prediction of Biological Aging and Alzheimer’s Risk Modulated by Red Wine Polyphenols - Other participants: University of Kentucky, University of La Rioja, Spain, Institute of Grapevine and Wine Sciences, Spain.
77. Using microbial community - modelling and Machine Learning to understand forest ecological response to environmental damage. University of Kentucky, Campbellsville University/Oregon State University.
78. AI‑assisted site‑specific nitrogen management for corn using satellite, UAV, and calibration‑strip sensing. University of Kentucky
79. Physics‑informed ML for predicting plant‑available soil nutrients (N, P, K) using VNIR/MIR spectroscopy. University of Kentucky
80. Drone‑based applications for cover crop seeding. University of Kentucky
81. Weed tolerance and herbicide persistence using sensing and AI. University of Kentucky
82. Sensor‑based soil carbon and nitrogen dynamics in long‑term tobacco systems. University of Kentucky
83. Develop AI-based model for poultry production management, including bird detection, behavior classification, and disease detection. (University of Arkansas and University of Georgia)
84. AI-based model for swine production management focuses on swine body condition monitoring and feeding optimization. (University of Arkansas)
85. AI-based imaging system for poultry meat myopathies classification texture regression. (University of Arkansas)
86. Strawberry canopy size estimation using YOLOv11 and SAM (University of Florida)
87. Strawba YOLO: A Specialized Mamba-based YOLO Model for Strawberry Detection (University of Florida)
88. Monitoring insect pest movement using mark-release-recapture methods is labor-intensive and inaccurate (University of Florida)
89. To address common challenges in agricultural automation tracking—such as visual similarity, occlusions, and spatial overlap—a novel architecture was developed that jointly optimizes detection, segmentation, and identity association, achieving state-of-the-art performance with 55.3% HOTA on AppleMOTS and 86.3% HOTA on LettuceMOTS benchmark datasets. (University of Florida)
90. Development of computer vision systems for broiler behavior recognition (University of Tennessee)
91. Development of computer vision models for stored product insect pest recognition (University of Tennessee)
92. Stored Product Insect-Pest Detection (University of Tennessee)
93. Peanut maturity mapping for precision harvest (Virginia Tech)
94. Precision crop management techniques for new crop: Faba beans

OBJECTIVE 1B: AI tools for autonomous system perception, localization, manipulation, and planning for agroecosystems.Bottom of Form

1. Hardware and sensor development for autonomous ground platforms for real-time crop monitoring (CLEM)
2. Autonomous vehicle following black-line burn paths for prescribed fire management in grassland and rangeland ecosystems. Smaller, more maneuverable, and more affordable than current market solutions (KSU)
3. Autonomous precision spray platform for integrated pest management in post-harvest grain storage and agricultural warehouse environments. Reduces chemical usage through targeted delivery. (KSU)
4. Screw-drive autonomous vehicle for real-time water quality monitoring in agricultural streams. (KSU)
5. Reconfigurable robot with ground-penetrating radar for precision soil moisture mapping and irrigation management. (KSU)
6. Wheelchair-sized autonomous robot for targeted removal of invasive eastern red cedar threatening prairie and rangeland ecosystems. (KSU)
7. A 5-DOF end‑effector that adapt to fruit orientation, apply multiple harvesting strategies (bending, twisting, pulling, or combinations), and safely transfer harvested fruit into a collection duct. (MSU)
8. A high‑speed, low‑energy robotic arms tailored for berry harvesting to increase efficiency while minimizing fruit damage and plant disturbance. (MSU)
9. Robotic sweetpotato harvester that will replace bucket crew people (MSU)
10. Infusing autonomy in sugarcane harvesting process (MSU)
11. Infusing autonomy in cotton harvesting process (MSU)
12. Integrate an autosteer system onto an electric tractor for small-scale producers (MSU)
13. Purdue AgBot: Autonomous Mobile Robot for In-row and Under-canopy Crop Monitoring and Physical Sampling (Purdue University)
14. P-AgNAV: LiDAR range view-based autonomous navigation system
15. P-AgSLAM: Simultaneous localization and mapping framework for cornfields
16. Animal Robot Interaction : Enabling robots to meaningfully interact with animals, paving new way for agriculture, conservation, and animal studies.
17. Neuromorphic Vision: Event camera-based ultra-low latency operation in degraded and austere environments.
18. Drain Tile robot: This wheg-based robot with neuromorphic control is designed to sample the drain tile environment, providing a portal to the Subterranean soil microbiome.
19. Developing Automated Machine Vision Systems for Non-Invasive Monitoring of Thermal Stress and Welfare in Dairy Cattle (University of Georgia)
20. Vision-Guided Plant Surgical Robotics for Precision Miniaturized Sensor Implantation in Maize (University of Georgia)
21. ROBO-Root3D: Miniaturized Robot for Underground Nondestructive 3D Root Phenotyping (University of Georgia)
22. Project 4: AI-enabled robotics development for chicken rehanging process. Purdue University and University of Arkansas)
23. Project 5: AI-enabled robotics for poultry processing facility environmental sample collection (University of Arkansas, GTRI, UNL, FVSU).
24. Imitation Learning Based Customized Dual-Jaw Gripper Control for Manipulation of Delicate, Irregular Bioproducts (University of Arkansas)
25. Robotic food safety swabbing system (University of Arkansas)
26. Agrosense: Sensing for Citrus Orchard Management provides tree counting with integrated depth, canopy classification using weighted tree moving sequences, height estimation validated with UAV and LiDAR ground truth, location mapping via GPS, IMU, and time stamps compared to UAV data, and fruit counting. (University of Florida)
27. AI-Enabled Robotic Sprayer (University of Florida)

OBJECTIVE 1C: Natural resources scouting and monitoring.

1. Deep learning models for sub-daily soil water tension prediction to optimize irrigation scheduling (Clemson University and University of Georgia)
2. To develop machine learning models aiming to predict sugarcane yellow leaf virus infection status (LSU)
3. developed new spectroscopy-based sensing technology can be used to identify wetland soils (MSU)
4. Predicting soil moisture using machine learning algorithms under multiple depths and environmental variable scenarios.
5. Developing an automated, web-based soil property estimation tool using mid-infrared (MIR) spectroscopy and machine learning. UW-Madison and Oregon State University.
6. SpectrAnd: Spectroscopy for rapid identification of Andic soil properties. Building robust ML/AI models with extremely sparse samples by incorporating expert knowledge (knowledge-guided). UW-Madison ,OSU and NMSU.
7. Advanced sensing technologies for soil health assessment in sustainable hop cultivation. UW-Madison
8. Develop a hybrid AI model by incorporating the DayCent model and ML models to understand soil C dynamics under different management practices in the seed grass production system and support C measurements, reporting, and verification. UW-Madison
9. Interactive mapping of soil and surrounding environment in Cascade Siskiyou National Monument. UW-Madison
10. SCOUT: The in-vivo methane sensor for realtime rumen methane emissions. Purdue University
11. Embodied Skin: : Mechano-compute realized through precise, learnable control of rigidity (Purdue University)
12. Monitoring emerging plant disease threats by analyzing spatial and temporal patterns at the population level (Purdue University)
13. Hybrid GeoAI Framework for Nitrogen Monitoring in Pasture-Based Systems TAMU
14. Development of the Intelligent System for Integrating Global Human & Animal Health Technology’s (INSIGHT) Large Language Model (Llama) TAMU
15. AI-Driven Forest Biomass Modeling for the Southeastern USA Using Terrestrial and Airborne Lidar (TAMU, The Jones Center at Ichauway, Newton, GA)
16. Physics‑informed and data‑driven ML integrating VNIR/MIR spectroscopy under variable field conditions (University of Kentucky)
17. Gamma‑ray and multi‑sensor fusion approaches for scalable soil bulk density and carbon stock estimation (University of Kentucky)
18. No‑tillage impacts on soil carbon dynamics (University of Kentucky)
19. Drone‑based applications for cover crop seeding and landscape‑scale monitoring (University of Kentucky)
20. Chen, P.§, Clingensmith, C.®, Ye, C., Grunwald, S., and Mizuta, K.† (2025). R Package: Machine Learning Models for Soil Properties (MLSP), The Comprehensive R Archive Network. https://doi.org/10.32614/cran.package.mlsp.
21. Computer vision models to support automated harvesting of bamboo culms (University of Florida)
22. Nationwide soil property estimation tool with a goal to develop an automated, user-friendly, web-based portal for estimating soil properties and soil health indicators from MIR spectra (University of Wisconsin)

OBJECTIVE 1D: Socioeconomic sustainability

1. Economics of genomic selection and breeding optimization for dairy herds: a web based decision model ( TAMU)
2. Reposition controlled environment agriculture (CEA) as a component of urban infrastructure in urban systems, rather than a venture capital-driven, high-tech industry, and use CEA to deliver co-benefits for food justice, public health, and environmental resilience. We propose using geospatial artificial intelligence (GeoAI) and high-performance computing (HPC) as tools to enable adaptive crop management, site-specific decision support, and identify underserved communities for targeted interventions (Texas A&M, U of Arkansas, Harvard)

OBJECTIVE 1E: Phenotyping and genotyping

1. Supervised and unsupervised training to identify seed coat color and pattern classes in lima beans (CLEM)
2. Supporting sugarcane variety development with UAV remote sensing (LSU, USDA New Orleans)
3. Supporting winter cereals variety development with UAV remote sensing (LSU)
4. Advancement and scaling of UAS-based high-throughput phenotyping (HTP) across multiple crops, including wheat, cotton, rice, oats, energycane, and peanuts Texas A&M AgriLife Research and Extension Centers at Corpus Christi, Weslaco, Beaumont, Amarillo, Stephenville
5. Improving Cultivar Development Efficiency and Accuracy Using AI-Enhanced Modeling TAMU
6. HT3P for Plant Breeding: Provide ready-to-use data sets to breeding programs
7. AI‑based monitoring of tall fescue abundance and toxicity risk affecting grazing livestock (University of Kentucky)
8. Deep‑learning phenotyping for differentiating tall fescue and Italian ryegrass for precision pasture and wheat system (University of Kentucky)
9. Biomass and nutrient estimation using aerial hyperspectral and lidar data (University of Florida)

OBJECTIVE 2A: Data curation, management, and accessibility, and security, ethics

1. Automated detection of roseau cane scale using computer vision models. Funded by the USDA-APHIS-PPQ; Involved institutions: LSU AgCenter.
2. Development of NRSP: Approval of multistate project that will develop (1) large, comprehensive, open-source datasets that make benchmark data available to researchers nationwide and span a wide range of agricultural-autonomy applications, and (2) AI algorithms and architectures that account for the fusion of heterogeneous (a.k.a. multimodal) data on different geospatial and temporal scales, which take advantage of feature engineering to maximize algorithm efficiency and data that are practically available to growers and agronomic consultants. (Mississippi State University, University of Florida, University of California-Davis, Cornell University, and University of Illinois.
3. Challenges in harmonizing crop model outputs with oceanic and atmospheric datasets, limited ground-truth NPP data to validate linkages between land and ocean systems. Additionally, we are working on processing multi-modal sensor data (ET, soil moisture, thermal IR) in temporal alignment, along with handling high-dimensional hyperspectral and multispectral datasets and curating field-sampled SOC and LULC labels for ML training.
4. Management challenges in the integration of explainable AI outputs for interpretability and time-series curation of long-term climate datasets (gridded, station-based), along with quality control for outliers and missing data in sparse regions.
5. To alleviate this, we have been collecting many field data for calibration and validation in 2023-2025. We are now seeking federal funding to sustain the efforts to develop a repository of genetic and crop coefficient use for modeling and irrigation water management.
6. Two machine vision datasets on sweetpotato grading and volume estimation were released to the public to support computer algorithm development and benchmarking (MSU)
7. Development of imagery (RGB + NIR + Depth) dataset for broiler behavior monitoring (3 trails, 50+ pens, 3 different housing environments) (UK, NRCS Owensboro KY, Persistent Data Mining Inc., Spectral Evolution Inc.)
8. Development of imagery (RGB + NIR + Depth) dataset for feces assessment (1 trails, 48 cages, 28 days/cage) (UK, NRCS Owensboro KY, Persistent Data Mining Inc., Spectral Evolution Inc.)
9. Development of imagery (RGB + NIR + Depth) dataset for sows housed in farrowing rooms (2 trails, 8 sows, 2 months of data collection) (UK, NRCS Owensboro KY, Persistent Data Mining Inc., Spectral Evolution Inc.)

OBJECTIVE 2B: Standardization and testbed development – data standardization and software development

1. Field Book is an open-source Android app used to collect data on field research plots (Supported by AFRI, SCRI, Department of State, Cotton Incorporated) (CLEM)
2. Developing an automated, web-based soil property estimation tool using mid-infrared (MIR) spectroscopy and machine learning (CLEM)
3. Explore how agronomic, hydrologic, and mechanistic data from controlled-environment and multi-site field trials can be integrated through AI/ML models into a cross-environment decision support system that predicts crop responses to surfactant treatments and generates site-specific management and economic recommendations deployable through commercial precision agriculture platforms that producers already use (Texas A&M, UC-Davis, U of Florida)
4. Building the first million-level benchmark dataset and foundation models for cost-effective poultry management (UGA)

OBJECTIVE 3: AI adoption (technology transfer) and workforce development

1. AI in Agriculture Monthly Seminar at LSU AgCenter
2. Louisiana Precision Agriculture Summit
3. Purdue University’s Agronomy e-Learning program offers fully online courses for working professionals, including Agronomy Essentials, Precision Agriculture (with a new Digital Agriculture version launching in 2025), and Nutrient Management—which have served over 2,600 individuals from 463 companies across all 50 states and 48 countries.
4. Leaf Monitor combines a mobile app for in-field data collection and real-time crop monitoring via smartphone-based imaging and sensors with a web app for centralized data visualization, analysis, and decision support across fields and seasons; more information is available at digitalaglab.com .
5. Workshop implementing Bayesian models in livestock production research – NANP
6. https://national-animal-nutrition-program.github.io/2025ASAS_Parsons/index.html
7. Maintaining active GitHub repositories for data and code.
8. https://sdsu-cottonwood-precision-ranch.github.io/C_Lock_API/
9. Training Data Repository
10. https://github.com/irap93/Bayesdemo
11. Development of Online Platform for Automated Fertilizer Prescription Calculation Driven by (AI and) Remote Sensing-Based Calibration Strip Technology (TAMU, UT, UC Davis)
12. Development of Cheap and Rapid Soil Testing Service Using Spectroscopy (TAMU, UT, UC Davis)
13. Development of Campus-wide AI Agrifood Institute (TAMU, UT, UC Davis)
14. Precision Management for Agriculture and Natural Resources Conservation 2025 fall (TAMU, UT, UC Davis)
15. Development of a graduate course ‘Applied Artificial Intelligence in Livestock Systems’. Train students to develop and use AI technologies in agricultural field (Being taught fall & spring semester from 2024 to current. (TAMU)
16. National Science Foundation Innovation Corps (I-Corps™) Program: EpicMare - Epigenetic gestational clock predictive diagnostic tool that identifies pregnancy complications (University of Kentucky)
17. Portable AI-Assisted Microscopy for Equine Blood Cell Differentials and Gestational Health Prediction (University of Kentucky)
18. Agroclimate viewer and planner App (Virginia Tech)

All Station Referred Journals/Book Chapters

1. Singh, J., Koc, A. B., Aguerre, M. J., & Chastain, J. P. (2025). Real-time forage biomass estimation using IMU sensor-based systems. Smart Agricultural Technology, 12, 101424.
2. Manimozhian, A., & Chandel, A. K. (2026). Thermal infrared technologies for precision agriculture: A ROSES-guided systematic evidence synthesis on platforms, calibration, and digital analytics. Agricultural Environment and Sustainability, 100014. https://doi.org/10.1016/j.ages.2026.100014
3. Sarr, A., Chandel, A. K., Diop, L., Soro, Y. M., Tossa, A. K., Hota, S., & Manimozhian, A. (2026). Agroclimatic sensing, communication, and computational systems-based methods and technologies for precision irrigation management: Current state and prospects. Computers, 15(2), 137. https://doi.org/10.3390/computers15020137
4. Nkwocha, C. L., & Chandel, A. K. (2025). Towards an end-to-end digital framework for precision crop disease diagnosis and management based on emerging sensing and computing technologies: State over past decade and prospects. Computers, 14(10), 443. https://doi.org/10.3390/computers14100443
5. Sahayaraj, S. R. E., Chandel, A. K., Balota, M., Chappell, M., & Sridhar, V. (2025). Leveraging stacked generalization for peanut maturity mapping using aerial multispectral imagery and growing degree days. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (Vol. 13475, pp. 202–212). SPIE.
6. Jjagwe, P., Chandel, A., Balota, M., & Raman, R. (2025). Faba bean crop plant identification using aerial multispectral imagery and convolutional neural network-based deep learning models. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (Vol. 13475, pp. 227–236). SPIE.
7. Nkwocha, C. G., & Chandel, A. K. C. (2025). Initial prototyping of a low-cost unoccupied ground vehicle platform for crop problem risk and severity mapping in agricultural fields. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (Vol. 13475, pp. 156–166). SPIE.
8. Manimozhian, A. P., Jjagwe, P. G., & Chandel, A. K. C. (2026). Mapping cotton boll opening at field-scale (% open bolls) using UAV imagery. VCE Publications, BSE-385NP.
9. Jjagwe, P. G., Flessner, M., Holshouser, D., Chandel, A. K. C., & Hota, S. (2025). Potential of controlled traffic farming for enhanced soil health and productivity. VCE Publications, BSE-374NP.
10. Chandel, A. K. C., Konduru, L. V., Kanakamedala, V. C., Maurya, A. P., & Hota, S. (2025). Agroclimate Viewer & Planner App. VCE Publications, BSE-372NP.
11. Chandel, A. K. C., Konduru, L. V., Kanakamedala, V. C., Maurya, A. P., & Hota, S. (2025). Agroclimate Viewer & Planner App (NDVI only). VCE Publications, BSE-371NP.
12. Kunwar, S., Babar, A., Jarquin, D., Ampatzidis, Y., Khan, N., Acharya, J. P., McBreen, J., Adewale, S., & Brown-Guedira, G. (2026). Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: Kernel-based and machine learning approaches. Frontiers in Plant Science, 17, 1740337. https://doi.org/10.3389/fpls.2026.1740337
13. Huang, Z., Lee, W. S., Ampatzidis, Y., Agehara, S., & Peres, N. A. (2026). PheMuT: A phenology-informed, multi-modal time-series model for strawberry yield forecasting. Computers and Electronics in Agriculture, 244, 111526. https://doi.org/10.1016/j.compag.2026.111526
14. Jaihuni, M., Gan, H., Moyle, J., Tabler, T., Qi, H., & Zhao, Y. (2025). Developing deep learning models for automated mating-related behavior detection in broiler breeders in lab setting. Animal Science Proceedings, 16(4), 735–737.
15. Zhou, S., Amin, N., Yang, X., Thornton, T., Gan, H., Tabler, T., & Zhao, Y. (2025). Impact of stocking density on activity index, stretching and preening behaviors of broilers. Animal Science Proceedings, 16(4), 559–560.
16. Nasiri, A., Zhao, Y., & Gan, H. (2025). Automated video recognition system for monitoring broiler breeders’ mating behavior in commercial farms. Animal Science Proceedings, 16(4), 725–727.
17. Liu, X., Javidan, S. M., Ampatzidis, Y., & Zhang, Z. (2026). A hybrid model of deep feature extraction and weighted ensemble classifiers for accurate barley disease detection. Journal of the ASABE, 69(1), 13–24. https://doi.org/10.13031/ja.16542
18. Ilodibe, U., Choi, D., Lahiri, S., Li, C., Hofstetter, D., & Ampatzidis, Y. (2026). Explainable deep learning and edge inference for chilli thrips severity classification in strawberry canopies. Agriculture, 16(2), 252. https://doi.org/10.3390/agriculture16020252
19. Rui, Z., Zhang, Z., Hao, C., Jia, Y., Ye, Z., Hussain, S., Abdelhamid, M. A., Igathinathane, C., & Ampatzidis, Y. (2026). Advancement and field evaluation of a honeysuckle harvesting robot with integrated clamping and air-suction mechanisms. Computers and Electronics in Agriculture, 243, 111400. https://doi.org/10.1016/j.compag.2025.111400
20. Neto, A. D. C., Hariharan, J., Ampatzidis, Y., Canon, M. A., Villamarin, P., & Revynthi, A. M. (2026). Early detection of spider mite stress in hibiscus using hyperspectral imaging and machine learning framework: Toward application-oriented multispectral solutions. Computers and Electronics in Agriculture, 243, 111369. https://doi.org/10.1016/j.compag.2025.111369
21. Cao, W., Zhang, Z., Wang, Z., Wei, A., Chen, Q., Igathinathane, C., Zhang, F., Abdelhamid, M. A., Ye, D., & Ampatzidis, Y. (2026). GLS-YOLOv8n: A lightweight ‘Guiqi’ mango detection model via RGB-depth-thermal image fusion. Computers and Electronics in Agriculture, 242, 111355. https://doi.org/10.1016/j.compag.2025.111355
22. Tulu, B. B., Teshome, F. T., Ampatzidis, Y., Li, C., Pavam, W., Golmohammadi, G., & Bayabil, H. K. (2026). RGB-to-synthetic-thermal image translation using generative AI to support crop water stress assessment. Computers and Electronics in Agriculture, 241, 111273. https://doi.org/10.1016/j.compag.2025.111273
23. Vijayakumar, V., Neto, A. D. C., Ampatzidis, Y., Schueller, J. K., Lee, W. S., & Burks, T. (2026). Design and evaluation of a PI-controlled robotic sprayer for precision herbicide applications with multi-nozzle integration. Precision Agriculture, 27(2). https://doi.org/10.1007/s11119-025-10304-7
24. Gu, H., Javidan, S. M., Ampatzidis, Y., & Zhang, Z. (2025). Explainable AI for predicting latent period and infection stage progression in tomato fungal diseases. Horticulturae, 11(11), 1376. https://doi.org/10.3390/horticulturae11111376
25. Vijayakumar, V., Neto, A. D. C., & Ampatzidis, Y. (2025). AI-powered autonomous smart sprayer for precision weed management: Advancing sustainable agriculture through machine vision, automation, and control systems. IFAC-PapersOnLine, 59(23), 40–43. https://doi.org/10.1016/j.ifacol.2025.11.760
26. Ma, G., Zuo, C., Javidan, S. M., Zhang, Z., Ampatzidis, Y., Vakilian, K. A., Banakar, A., Rahnama, K., & Mhamed, M. (2025). A tomato leaf fungal disease image dataset and a metaheuristic-based framework for optimizing machine learning classification. Smart Agricultural Technology, 101568. https://doi.org/10.1016/j.atech.2025.101568
27. Teshome, F. T., Bayabil, H. K., Schaffer, B., & Ampatzidis, Y. (2025). Estimating crop evapotranspiration using drone imagery, ground canopy temperature, and machine learning techniques. Remote Sensing Applications: Society and Environment, 39, 101661. https://doi.org/10.1016/j.rsase.2025.101661
28. Vijayakumar, V., Ampatzidis, Y., Lacerda, C., Burks, T., Lee, W. S., & Schueller, J. K. (2025). AI-driven real-time weed detection and robotic smart spraying for optimized performance and operational speed in vegetable production. Biosystems Engineering, 259, 104288. https://doi.org/10.1016/j.biosystemseng.2025.104288
29. Zhou, C., Ampatzidis, Y., Guan, H., Liu, S., Liu, W., Neto, A. D. C., Kunwar, S., & Batuman, O. (2025). Agrosense: Accelerating precision orchard management through an AI-enabled monitoring system. Precision Agriculture, 26(4), 73. https://doi.org/10.1007/s11119-025-10268-8
30. Zhou, C., Ampatzidis, Y., Guan, H., Liu, S., Liu, W., Neto, A. D. C., Kunwar, S., & Batuman, O. (2025). Agrosense: Accelerating precision orchard management through an AI-enabled monitoring system. Precision Agriculture, 26(4), 73. https://doi.org/10.1007/s11119-025-10268-8
31. Ma, G., Javidan, S. M., Ampatzidis, Y., & Zhang, Z. (2025). A novel hybrid technique for detecting and classifying hyperspectral images of tomato fungal diseases based on deep feature extraction and Manhattan distance. Sensors, 25(14), 4285. https://doi.org/10.3390/s25144285
32. Trentin, C., Ampatzidis, Y., Tasioulas, S., & Tsouvaltzis, P. (2025). Optimizing tomato yield prediction using phenologically timed UAV-based spectral data and machine learning. Smart Agricultural Technology, 101158. https://doi.org/10.1016/j.atech.2025.101158
33. Li, X., Huang, F., Sun, H., He, J., Javidan, S. M., Ampatzidis, Y., & Zhang, Z. (2025). A bio-inspired framework for apple leaf disease detection: Integrating lesion localization, ant colony optimization, and machine learning. Smart Agricultural Technology, 101141. https://doi.org/10.1016/j.atech.2025.101141
34. Lacerda, C. F., Ampatzidis, Y., Neto, A. D. C., & Partel, V. (2025). Cost-efficient high-resolution monitoring for specialty crops using AgI-GAN and AI-driven analytics. Computers and Electronics in Agriculture, 237, 110678. https://doi.org/10.1016/j.compag.2025.110678
35. Kunwar, S., Babar, A., Jarquin, D., Ampatzidis, Y., Khan, N., Acharya, J. P., McBreen, J., Adewale, S., & Brown-Guedira, G. (2025). Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi-kernel genomic prediction models integrating secondary traits and environmental covariates. The Plant Genome, 18(2), e70052. https://doi.org/10.1002/tpg2.70052
36. McBreen, J., Babar, A., Jarquin, D., Ampatzidis, Y., Khan, N., Kunwar, S., Acharya, J. P., Adewale, S., & Brown-Guedira, G. (2025). Leveraging multi-omics data with machine learning to predict grain yield in small vs. big plot wheat trials. Agronomy, 15(6), 1315. https://doi.org/10.3390/agronomy15061315
37. Shi, X., Javidan, S. M., Ampatzidis, Y., & Zhang, Z. (2025). AI-driven identification of grapevine fungal spores via microscopic imaging and feature optimization with Cuckoo search algorithm. Smart Agricultural Technology, 11, 101029. https://doi.org/10.1016/j.atech.2025.101029
38. Huang, Z., Lee, W. S., Yang, P., Ampatzidis, Y., Shinsuke, A., & Peres, N. A. (2025). Advanced canopy size estimation in strawberry production: A machine learning approach using YOLOv11 and SAM. Computers and Electronics in Agriculture, 236, 110501. https://doi.org/10.1016/j.compag.2025.110501
39. Thomasson, J. A., Ampatzidis, Y., Bhandari, M., Ferreyra, R. A., Gentimis, T., McReynolds, E., Murray, S. C., Peterson, M. B., Rodriguez Lopez, C. M., Strong, R. L., Tedeschi, L. O., Vitale, J., & Ye, X. (2025). AI in Agriculture: Opportunities, Challenges, and Recommendations. The Council for Agricultural Science and Technology (CAST). https://cast-science.org/publication/ai-in-agriculture-opportunities-challenges-and-recommendations
40. 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. https://doi.org/10.1016/j.compag.2025.110212
41. Tulu, B., Teshome, F. T., Ampatzidis, Y., Hailegnaw, N. S., & Bayabil, H. K. (2025). AgriSenAI: Automating UAV thermal and multispectral image processing for precision agriculture. SoftwareX, 30, 102083. https://doi.org/10.1016/j.softx.2025.102083
42. Mehdizadeh, S. A., Noshad, M., Chaharlangi, M., & Ampatzidis, Y. (2025). AI-driven non-destructive detection of meat freshness using a multi-indicator sensor array and smartphone technology. Smart Agricultural Technology, 10, 100822. https://doi.org/10.1016/j.atech.2025.100822
43. Javidan, S. M., Ampatzidis, Y., Banakar, A., Vakilian, K. A., & Rahnama, K. (2025). An intelligent group learning framework for detecting common tomato diseases using simple and weighted majority voting with deep learning models. AgriEngineering, 7(2), 31. https://doi.org/10.3390/agriengineering7020031
44. McBreen, J., Babar, A., Jarquin, D., Ampatzidis, Y., Khan, N., Kunwar, S., Acharya, J. P., Adewale, S., & Brown-Guedira, G. (2025). Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments. The Plant Genome, 18(1). https://doi.org/10.1002/tpg2.20554
45. Vijayakumar, V., Costa Neto, A., Ampatzidis, Y., Schueller, J., Lee, W. S., & Burks, T. (2025). Design and evaluation of a PI-controlled robotic smart sprayer for precision herbicide applications with multi-nozzle integration. Precision Agriculture, 27(2). https://doi.org/10.1007/s11119-025-10304-7
46. Pardo-Beainy, C., Parra, C., Solaque, L., & Lee, W. S. (2025). Deep learning and georeferenced RGB-D imaging for hydroponic strawberry yield mapping. Smart Agricultural Technology, 12, 101293. https://doi.org/10.1016/J.ATECH.2025.101293
47. Vijayakumar, V., Ampatzidis, Y., Lacerda, C., Burks, T., Lee, W. S., Schueller, J., Burks, T. F., & Schueller, J. K. (2025). AI-driven real-time weed detection and robotic smart spraying for optimized performance and operational speed in vegetable production. Biosystems Engineering, 259, 104288. https://doi.org/10.1016/J.BIOSYSTEMSENG.2025.104288
48. Chen, Y., Shu, A., Liu, Z., Chen, Y., Lee, W. S., & Zhang, Y. (2025). SP-RTSD: A lightweight real-time strawberry detection on edge devices for onboard robotic harvesting. Journal of Field Robotics, 42(7), 3361–3379. https://doi.org/10.1002/ROB.22582
49. Kwak, M.-J., Pandey, S., Zhai, Y., Choi, B., Sutthanonkul, T., Lee, W. S., & Jeong, K. C. (2025). Understanding the impact of soil microbiome on strawberry growth and nutritional profiles. Frontiers in Microbiology, 16. https://doi.org/10.3389/fmicb.2025.1654776
50. Huang, Z., Lee, W. S., Yang, P., Ampatzidis, Y., Shinsuke, A., & Peres, N. A. (2025). Advanced canopy size estimation in strawberry production: A machine learning approach using YOLOv11 and SAM. Computers and Electronics in Agriculture, 236, 110501. https://doi.org/10.1016/J.COMPAG.2025.110501
51. Tapia, R., Lee, W. S., Whitaker, V. M., & Lee, S. (2025). Multiple methods for predicting strawberry powdery mildew severity from field canopy reflectance data. PhytoFrontiers, 5(3), 283–289. https://doi.org/10.1094/PHYTOFR-06-24-0063-SC
52. Huang, Z., Lee, W. S., Zhang, P., Jeon, H., & Zhu, H. (2025). SASP: Segment any strawberry plant, an end-to-end strawberry canopy volume estimation. Smart Agricultural Technology, 11, 101017. https://doi.org/10.1016/J.ATECH.2025.101017
53. 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. https://doi.org/10.1016/J.COMPAG.2025.110212
54. Kim, J.-H., Cho, Y.-H., Kim, K.-M., Lee, C.-Y., Lee, W. S., & Nam, J.-S. (2025). Sweet potato farming in the USA and South Korea: A comparative study of cultivation pattern and mechanization status. Journal of Biosystems Engineering, 50(2), 210–224. https://doi.org/10.1007/S42853-025-00260-5
55. Bampasidou, M., & Fields, J. (2025). Is labor shortage pushing towards automation and mechanization of the US nursery industry? Choices, 40(1).
56. Bampasidou, M., et al. (2025). Navigating emerging technologies in specialty crops: Production, labor and ethical considerations. Choices, 40(1).
57. Gentimis, T., Bampasidou, M., & Sehgal, V. (2025). Water-energy-food nexus: AI ethics in agricultural curricular and outreach strategies. In AI and the Water-Energy-Food Nexus: Innovations for Sustainable Resource Management (Accepted).
58. Alimardani, R., Adedeji, A. A., & Narimani, M. (2025). A review of IoT applications in food processing and related fields. Journal of Food Processing and Preservation, 1–15. https://doi.org/10.1155/jfpp/3064441
59. Thomasson, J. A., Ampatzidis, Y., Bhandari, M., Ferreyra, R. A., Gentimis, T., McReynolds, E., Murray, S. C., Peterson, M. B., Rodriguez Lopez, C. M., Strong, R. L., Tedeschi, L. O., Vitale, J., & Ye, X. (2025). AI in Agriculture: Opportunities, Challenges, and Recommendations. CAST.
60. Clay, D., Brugler, S., & Mizuta, K. (2026). Precision Agriculture Basics (2nd ed.). ASA, SSSA, CSSA. (In print).
61. Biswas, A., Gebbers, R., Adamchuk, V., Jha, G., Mizuta, K., et al. (2026). Chapter 12 – Proximal soil sensing for agriculture. (In print).
62. Yang, C., Bachina, S., Biswas, A., Gebbers, R., Adamchuk, V., Jha, G., Mizuta, K., et al. (2026). Chapter 13 – Proximal plant sensing for agriculture. (In print).
63. Chen, P., Clingensmith, C., Ye, C., Grunwald, S., & Mizuta, K. (2025). R package: Machine learning models for soil properties (MLSP). The Comprehensive R Archive Network. https://doi.org/10.32614/cran.package.mlsp
64. Bist, R., Chai, L., Subedi, S., Tian, Y., & Wang, D. (2025). Enhancing poultry multi-behavior detection with semi-supervised auto-labeling and prompt-driven zero-shot recognition. Computers and Electronics in Agriculture, 240, 111178.
65. Bist, R., Asnayanti, A., Do, A., Tian, Y., Pallerla, C., Wang, D., & Alrubaye, A. (2025). Automated detection of kinky back in broiler chickens using optimized deep learning techniques. AgriEngineering, 7(12), 415.
66. Davar, A., Xu, Z., Mahmoudi, S., Sohrabipour, P., Pallerla, C., She, Y., et al. (2026). ChicGrasp: Imitation-learning-based customized dual-jaw gripper control for manipulation of delicate, irregular bioproducts. Advanced Robotics Research, e202500149.
67. Feng, Y., Pallerla, C., Lin, X., Suresh, A., Sohrabipour, P., Crandall, P., et al. (2025). Synthetic data augmentation for enhanced chicken carcass instance segmentation. IEEE Transactions on AgriFood Electronics.
68. Crandall, P. G., O’Bryan, C. A., McFadden, B. R., Wang, D., Obe, T., Houlroyd, J., et al. (2025). A review of successful workplace interventions to mitigate work-related musculoskeletal disorders in poultry processing plant workers: Current knowledge and future prospects. Safety and Health at Work.
69. Mahmoudi, S., Davar, A., & Wang, D. (2026). A state-adaptive Koopman control framework for real-time deformable tool manipulation in robotic environmental swabbing. Advanced Robotics Research, e202500142.
70. Mahmoudi, S., Griscom, C., Sohrabipour, P., Tian, Y., Pallerla, C., Crandall, P., & Wang, D. (2025). Evaluation of robotic swabbing and fluorescent sensing to monitor the hygiene of food contact surfaces. Foods, 14(19), 3311.
71. Griscom, C., Wang,D., O’Bryan, C. A., Rõõm, R., & Crandall, P. G. (2025). Rapid detection of chicken residues on poultry plant surfaces using color and fluorescence spectrometry. Foods, 14(24), 4352.
72. Pallerla, C., Bist, R. B., Owens, C. M., Weimer, S., Subbiah, J., & Wang, D. (2025). Thermal imaging-guided detection of transparent plastic contaminants on chicken breast: A combined vision and simulation approach. Food Research International, 117993.
73. Baker, S., Zhao, L., Scott, J. L., Rudd, J., Ibrahim, A. M., Landivar, J., et al. (2026). A double-sigmoid approach for high-throughput phenotyping of winter wheat growth dynamics. Smart Agricultural Technology, 101948.
74. Tiwari, A., Zhao, L., Sarkar, S., Urutyan, V., Vysyaraju, U. S. R., Moon, S., et al. (2026). Transferability of spatial and temporal learning models for winter wheat mapping in data-scarce environments: A case study in Armenia. Remote Sensing Applications: Society and Environment, 101885.
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176. Kaniyamattam, K., Adga, A., McCain, E., Scaborough, M., Muntari, M., Pulikkottil-Rejimon, S., et al. (2025). A real-time data integration and visualization framework for monitoring beef cattle performance and emissions data from precision livestock technologies. MODNUT 2025.
177. Chen, S., Wang, Y., Zhao, Z., Feng, D., Archer, G., Athrey, G., & Xu, Z. (2025). Investigation of poultry manure removal efficiency and volume estimation on grooved-floor panels. Animal Science Proceedings, 16(4), 616–617.
178. Wang, Y., Chen, S., Zhao, Z., Zhou, J., Safranski, T., Wiegert, J., & Xu, Z. (2025). Evaluate changes in respiratory rate of lateral lying sows around onset of parturition using depth camera. Animal Science Proceedings, 16(4), 536–538.
179. Zhao, Z., Wang, Y., Villegas, C., Farnell, M., Archer, G., Athrey, G., et al. (2026). Differentiate moderate woody breast from normal chicken breast using monofilament needles. Applied Food Research, 6(1), 101912.
180. Zhou, J., He, X., Xu, Z., Bromfield, C., Safranski, T., Lim, T., & Tsay, J. H. (2025). Developing a robotic imaging system for detecting estrus of stall-housed sows. Animal Science Proceedings, 16(4), 532–534.
181. Zhou, J., Scaboo, A., Beche, E., Xu, Z., & Zhang, Z. (2025). Transfer learning for improving generalizability in predicting soybean maturity date using UAV imagery. Frontiers in Plant Science, 16, 1720819. https://doi.org/10.3389/fpls.2025.1720819
182. Xu, Z., & Zhou, J. (2025). Development of automated technologies for estrus detection in pigs. In Advances in Precision Pig Farming Technologies. Burleigh Dodds Science Publishing.
183. Deng, B., Lu, Y., Siemens, M., Brainard, D., 2025. Field Test and Evaluation of a Smart Sprayer for Precision Weeding. First Annual Symposium on Digital Agriculture (SyDAg), Purdue University, October, 2025. https://doi.org/10.13140/RG.2.2.23109.10721
184. Islam, K., Deng, B., Lu, Y., Siemens, M., Brainard, D., Srivastava, A., 2025. Speed vs. Precision: Optimizing Tractor- Based Smart Spraying for Vegetables. First Annual Symposium on Digital Agriculture (SyDAg), Purdue University, October, 2025. https://doi.org/10.13140/RG.2.2.33175.43686

ALL STATION CONFERENCE PRESENTATIONS: PODIUM/POSTER

1. Rife T, C Courtney, H Manching, A Hulse-Kemp, J Hershberger. BrAPI and Field Book Updates. Plant and Animal Genome 33 Conference. San Diego, CA. 2026/01/11.
2. Rife T, C Courtney. Field Book in 2026. Plant and Animal Genome 33 Conference. San Diego, CA. 2026/01/09.
3. Zhang, Y., Weerasekara, M., Demain, E., Qi, M., Hartemink, A. E., & Maynard, J. (2025). Machine learning-informed mid-infrared spectral library: Emerging observations of soil carbon and soil health. ILAMB Meeting, New Orleans, LA.
4. Zhang, Y., Weerasekara, M., Hartemink, A. E., & Maynard, J. (2025). An automated, web-based soil property estimation tool using mid-infrared spectroscopy and machine learning. USDA-NRCS Webinar.
5. Qi, M., Weerasekara, M., Zhang, Y., Hartemink, A. E., Maynard, J., & Georgiou, K. (2025). Using mid-infrared spectra to estimate soil organic carbon and its fractions. ASA-CSSA-SSSA International Annual Meetings, Salt Lake City, UT.
6. Kalisz, A., Zhang, Y., Brungard, C., Hodges, R., & Maynard, J. (2025). Rapid identification of andic soil properties using MIR spectroscopy and machine learning. ASA-CSSA-SSSA International Annual Meetings, Salt Lake City, UT.
7. Weerasekara, M., Hartemink, A. E., Maynard, J., Demain, E., & Zhang, Y. (2025). MIR soil prediction platform: Interactive website to predict soil properties using MIR spectroscopy, statistical, and machine learning models. ASA-CSSA-SSSA International Annual Meetings.
8. Mathew, F., Kaur, H., George, M., Mohan, K., Mukaila, T., & Rafi, N. (2025). Plant disease identification and management using remote sensing. In D. K. Shannon, D. E. Clay, & N. R. Kitchen (Eds.), Precision Agriculture Basics (2nd ed.). ASA, CSSA, and SSSA.
9. Pokharel, R., & Setiyono, T. (2025). Rice yield estimation using unmanned aerial vehicle (UAV) remote sensing and machine learning models. ASA-CSSA-SSSA International Annual Meetings, Salt Lake City, UT.
10. Setiyono, T., Sutthanonkul, T., Pokharel, R., Poudel, A., Mendoza, H., Kongchum, M., Tubana, B., & Kimbeng, C. (2025). Integration of crop modeling and remote sensing for resilient cropping systems in Louisiana. ASA-CSSA-SSSA International Annual Meetings, Salt Lake City, UT.
11. Zheng, Y., Jjagwe, P., Granja, M., Chandel, A.K., Ortel, C., Zhang, B. (2025). Multimodal UAS-Based High-Throughput Phenotyping and Machine Learning for Early-Season Yield Prediction in Soybean Breeding. Translational Plant Sciences Center Symposium. February 21, 2025. Blacksburg, VA. (oral presentation).
12. Zheng, Y., Jjagwe, P., Granja, M., Chandel, A.K, Ortel, C., Zhang, B., 2025. Multimodal UAS-Based High-Throughput Phenotyping and Machine Learning for Early-Season Yield Prediction in Soybean Breeding. In Center for Advanced Innovation in Agriculture (CAIA) 2025 Big Event. May 6, 2025. Blacksburg, VA. (poster presentation).
13. Zheng, Y., Jjagwe, P., Ogando do Granja, M., Chandel, A.K., Ortel, C., Zhang, B., 2025. Integrating UAS-Based Phenotyping and Machine Learning for Yield Prediction in Soybean. Joint MAS-ASPB (Mid-Atlantic Section of the American Society of Plant Biologists) and UMD (University of Maryland) Plant Symposium. May 28-29, 2025. College Park, MD. (oral presentation).
14. Chandel, A.K., 2025. Free Webtool for Producers to Monitor Field-Level Agroclimate for Planning Precision Agriculture Operations. In ASA-CSSA-SSSA annual meeting, November 10-14, 2025. Salt Lake City, UT. (oral presentation).
15. Vennam, R.R., Raymond, S., Chandel, A.K., Balota, M., Raman, R. 2025. Leveraging Remote Sensing and Machine Learning to Quantify Peanut Leaf Wilting under Heat and Drought Stress. ASA, CSSA, SSSA International Annual Meeting, November 10-14, 2025. Salt Lake City, UT. (oral presentation).
16. Zheng, Y., Jjagwe, P., Ogando do Granja, M., Chandel, A.K., Ortel, C., Zhang, B., 2025. Integrating UAV-Based Phenotyping and Machine Learning for Enhanced Prediction of Soybean Agronomic Traits. ASA, CSSA, SSSA International Annual Meeting, November 10-14, 2025. Salt Lake City, UT. (poster presentation)
17. Nkwocha, C., Chandel, A.K., Balota, M., Bryant, T., Malone, S., 2025. Identification of Southern Corn Root Worm Injury in Peanuts using Deep convolutional neural network based-YOLO. American Peanut Research and Education Society Meeting, July 15-17, 2025, Richmond, VA. (poster presentation).
18. Raymond, S., Chandel, A.K., Balota, M., 2025. Precision Peanut Maturity Mapping for Virginia-Type Cultivars using Aerial Spectral Imagery, Weather Data and Advanced Machine Learning. American Peanut Research and Education Society Meeting, July 15-17, 2025, Richmond, VA. (oral presentation).
19. Jjagwe, P., Chandel, A.K., Balota, M., Raman, R., 2025. Towards weed identification and management in Faba bean crop using aerial multispectral imagery and convolutional neural network-based computer vision models. Defense + Commercial Sensing exhibition, April 13-17, 2025, Orlando, FL. (oral presentation).
20. Raymond, S., Chandel, A.K., Balota, M., Chappell, M., Shridhar, V., 2025. Leveraging Stacked Generalization for Peanut Maturity Mapping Using Aerial Multispectral Imagery and Growing Degree Days. Defense + Commercial Sensing exhibition, April 13-17, 2025, Orlando, FL. (oral presentation).
21. Jjagwe, P., Chandel, A.K., Balota, M., Raman, R., 2025. Faba bean crop plant identification using aerial multispectral imagery and convolutional neural network-based computer vision models. AI in Agriculture and Natural Resources Conference, March 31- April 2, 2025, Starkville, MS. (oral presentation).
22. Raymond, S., Chandel, A.K., Balota, M., 2025. Advancing Non-Invasive Peanut Maturity Prediction using Aerial Multispectral Imagery and Weather data with stacked ensemble Multi-View Learning. AI in Agriculture and Natural Resources Conference, March 31- April 2, 2025, Starkville, MS. (oral presentation).
23. Chandel, A.K. AgroVAP: A farmer friendly tool for field level precision agricultural management. February 13, 2026. Chesapeake, VA. (Contact time: ~25 min, Attendees: ~70).
24. Chandel, A.K. Agroclimate Viewer and Planner App (AgroVAP) for Soybean Growers. Soybean field day. September 11, 2025. Warsaw, VA. (Contact time: ~25 min, Attendees: ~70).
25. Chandel, A.K. Introduction to Agroclimate viewer and planner app (AgroVAP) for precision agriculture. APRES field tour. July 11, 2025. Goodrich farms, VA. (Contact time: ~20 min, Attendees: ~70).
26. Chandel, A.K. Agroclimate viewer and planner app: A free platform for crop management decision making. Berry field day. June 4, 2025. Virginia Beach, VA. (Contact time: ~15 min, Attendees: ~40).
27. Chandel, A.K. Know how your crop is doing with Agroclimate Viewer and Planner App (Features and Utilization). Technology, Economy, and Wellness workshop for farmers and Extension agents. April 9, 2025. Suffolk, VA. (Contact time: ~1 h, Attendees: ~65).
28. Huang, Z., Lee, W. S. (Author & Presenter), & Le, M. (2025, July 14). AI-Driven Plant Tracking and Segmentation for Precise Canopy Estimation in Strawberry Fields. 2025 CSABE/ASABE Annual International Meeting, Toronto, Canada.
29. Lee, W. S. (2025, August 22). 2025 W-4009 Florida. W4009 Annual Meeting Agenda (Year 2025), Gainesville, FL.
30. Lee, W. S. (Author & Presenter). (2025, August 01). AI applications in strawberry production in Florida. 2025 S1090 Multistate Project Annual Meeting, East Lansing, MI.
31. Lee, W. S. (2025, June 10). Florida State Report, NCERA-180. 2025 NCERA-180 and S-106 Joint Meeting, Brookings, SD.
32. Huang, Z. (Author & Presenter), & Lee, W. S. (2025, March 31). A State Space Model with Tree Topology for Strawberry Detection. 2025 AI in Agriculture & Natural Resources Conference, Starkville, MS.
33. Liu, S. (Author & Presenter), Ampatzidis, Y., Lee, W. S., & Zhou, C. (2025, March 31). Optimizing strawberry harvest planning through machine vision and AI-enabled predictive analytics. AI in Ag Conference, Starkville, MS.
34. Kondaparthi, A., & Lee, W. S. (Author & Presenter). (2025, May 13). Strawberry plant wetness detection using color imaging and artificial intelligence for the Strawberry Advisory System (SAS). 43rd Annual Agritech, Plant City, FL.
35. Huang, Z. (Author & Presenter), & Lee, W. S. (2025, October 24). Advancing Strawberry Agriculture with AI: From Fruit Detection and Canopy Volume Estimation to Yield Forecasting. UF AI Days: Harvesting Insights with UF/IFAS, GAINESVILLE, FL.
36. Son, W. (Author & Presenter), & Lee, W. S. (2025, October 24). Monocular 6D pose estimation for Strawberries Using Sim-to-Real Transfer and Vision Foundation Model. UF AI Days: Harvesting Insights with UF/IFAS, GAINESVILLE, FL.
37. Huang, Z. (Author & Presenter), Lee, W. S., & Zhang, P. (2025, March 26). Segment Any Strawberry Plant, An End-to-End Strawberry Canopy Volume Estimation. UF ABE Poster Symposium, GAINESVILLE, FL.
38. Medeiros, H. “High-throughput phenotyping using robotic platforms​.” VII International Symposium on Genetic Improvement and Preservation of Plants, Goiania, Brazil, December 2025 (invited talk).
39. Medeiros, H. “AI – Based Precision Poultry Management using Computer Vision and Environmental Sensors​.” VIII International Workshop on Precision Environments for Animal Husbandry, Campinas, Brazil, November 2025 (invited talk).
40. Wang, D. Opportunities of Precision Agriculture Development in the state of Arkansas. In Arkansas Research & Computing Stakeholder Forum. Little Rock, AR [Invited oral]
41. Pallerla C., Bist R., Owens C.M., Weimer S., Subbiah J., Wang D. Thermal Imaging-Guided Detection of Transparent Plastic Contaminants on Chicken Breast: A Combined Vision and Simulation Approach. In the 2025 Arkansas Association for Food Protection Meeting, Fayetteville, AR [Poster]
42. Mahmoudi S.,  Wang D. Data-Driven Contact-Aware Control Method for Real-Time Deformable Tool Manipulation: A Case Study in the Environmental Swabbing. In the 2025 Arkansas Association for Food Protection Meeting, Fayetteville, AR [Poster]
43. Tian Y., Pallerla C., Howell T., Subbiah J., Wang D. AI-Enabled Portable Electrochemical Impedance Immuno-biosensor for Ultra-Sensitive Detection of Escherichia coli O157 in Poultry Samples. In the 2025 Arkansas Association for Food Protection Meeting, Fayetteville, AR [Poster]
44. Mizuta, K. (2025). Sensor‑Based Precision Agriculture for Monitoring Soil Health. Waseda University, Tokyo, Japan. (o*)
45. Mizuta, K.(2025). Advancing Soil Security: Future Research Opportunities with In‑Situ MIR Spectroscopy Sensor Technologies. National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan. (o*)
46. Mizuta, K. (2025). Leveraging Sensing Technology and AI for Soil Health. National Agriculture and Food Research Organization, Ibaraki, Japan. (o*)
47. Mizuta, K. (2025). Soil Health Initiatives in the U.S. Science Council of Japan, Tokyo, Japan. (o*)
48. Bowling, M.B.§, Hodelka, B., McKinney, K., Ekramirad, N., Beck, E.G., Lee, B.D., Mizuta, K. (2025). Soil Bulk Density Estimation Using Multi‑Sensor Core Logger and Machine Learning. Kentucky Academy of Science Annual Meeting, Louisville, KY. (p)
49. Benedicto Pérez, J., Ruffner, M., Moore, J., Rodriguez Lopez, C.M. Early-stage tornado and salvage logging effects on tree community regeneration in a mesic hardwood forest in Kentucky. 87th Annual Meeting of the ​Association of Southeastern Biologists
50. Anand, L., Magnani, R., Walker, M., Loux, S., MacLeod, J.N., Rodriguez Lopez C.M. (2026). DNA Methylation Changes During Pregnancy Progression in Mares Enable Predictive Models of Gestational Age and Parturition. International Havemeyer Foundation Horse Genome Workshop - August 16-19, 2026
51. Lakshay Anand, Rodriguez Lopez C.M.(2025). AI-Driven Epigenetic Clocks for Predicting Gestational Age and Parturition Date in Mares. A poster presentation AI in Ag. Conference held at Raleigh, North Carolina. March 31 – April 2, 2026.
52. Benedicto Pérez, J., Ruffner, M., Moore, J., Rodriguez Lopez, C.M. Forest Management Effects after Tornado Disturbance on Abiotic Conditions and Tree and Soil Microbial Communities in a Mixed Hardwood Forest in Kentucky. 86th Annual Meeting of the ​Association of Southeastern Biologists
53. Oloyede, A. and Adedeji, A.A. (2026). Deep Learning-based HSI reconstruction from RGB Data for gluten detection and quantification in foods products. AI in Ag. Conference held at Raleigh, North Carolina. March 31 – April 2, 2026.
54. Oloyede, A. and Adedeji, A.A. (2025). Deep learning-based hyperspectral model reconstruction from RGB data for gluten detection in food products. A poster presentation at the 6^th International Electronic (Virtual) Conference on Foods organized by MDPI from October 28 – 30, 2025.
55. Joshi, D.R., Bishwakarma, S., & Westbrook, S. (2025). Developing a validation dataset for assessing land use change sustainability. Poster presentation presented at CANVAS 2025. November 9-12, 2025, Salt Lake City, Utah.
56. Joshi, D.R., Spencer, K., Lollato, R.P., & Sullivan, T. (2025). Integration of earth observation data into machine learning models for predicting wheat yield. Oral presentation presented at CANVAS 2025.November 9-12, 2025, Salt Lake City, Utah.
57. Spencer, K., Joshi, D.R., Lollato, R.P., & Patrignani, A. (2025). Precision nutrient management in wheat using multispectral UAV imagery and AI modeling. Oral presentation presented at CANVAS 2025.November 9-12, 2025, Salt Lake City, Utah.
58. Lucero, M., Adee, E., Ruiz Diaz, D., Sullivan, T., & Joshi, D. R. (2025). Integrating weather and soil data into machine learning models to predict corn–soybean yield. Poster presentation at the K-State AI Symposium 2025, October 14–16, 2025, Manhattan, KS.
59. Spencer, K., Joshi, D. R., Lollato, R. P., & Patrignani, A. (2025). Leveraging UAV multispectral imagery and machine learning for high-throughput phenotyping in winter wheat. Poster presentation at the Governor’s Conference on the Future of Water in Kansas, November 12–13, 2025, Manhattan, KS.
60. Bishwakarma, S., Obour, A., Ruiz Diaz, D., & Joshi, D. R. (2025). Assessing dryland agriculture management practices using UAV multispectral signatures. Poster presentation at the Governor’s Conference on the Future of Water in Kansas, November 12–13, 2025, Manhattan, KS.
61. Santos, G., Rollins, M.B., Zhou, C., Flasco, M. T., Dalla Lana, F. and Gama, A. B. 2025. Identifying spectral differences between sugarcane yellow leaf virus-positive and negative samples using hyperspectral imaging. Plant Health 2025. Honolulu, Hawaii, USA.
62. Setiyono, T., Gentimis, T., Rontani, F., Duron, D., Bortolon, G., Adhikari, R., Acharya, B., Han, K-J., Pitman, W.D. 2025 Application of Tensor Flow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images. 2025 AI in Agriculture and Natural Resources Conference. March 31 – April 2. Mississippi State University. Sackville, MS.
63. Subhash, T., Chetan, B., Alison, G., Deanna, S., Guru, N., Satish, S., (2026). Enhancing YOLOv11 Generalization for Stored-Product Pest Detection using Lightweight Convolutions and Dynamic Upsampling. AI in Agriculture 2026 Conference, Raleigh, NC. Poster Presentation
64. Rajesh, G., Subhash, T., Chetan, B., Alison, G., Deanna, S., Guru, N., Satish, S., (2026). Scaling Pest Surveillance: Deep Learning-Driven Insect Trap Detection and Localization for Mobile Robots in a Food Large Storage Structure. AI in Agriculture 2026 Conference, Raleigh, NC. Poster Presentation
65. Sutthanonkul, T., Kimbeng, C., Orgeron, A., Blanchard, B., Duron, D., Setiyono, T. (2026). Monitoring sugarcane biomass and sucrose yield with UAV remote sensing, geospatial computation tools, and AI-based framework. AI in Agriculture Conference. March 30 – April 2. NC State University. Raleigh, NC.
66. Fu, Peng., 2025. How Well Can Satellite Sensors Estimate Photosynthetic Capacities? A Spectral Library-Based Evaluation. American Geophysical Union Annual Meeeting 2025. (Project#1, Peng Fu)
67. Fu P. Remote Sensing Meets AI for Digital Agriculture. LSU AgCenter Precision Ag Summit. Dec.10, 2025, Alexandria, LA, hosted by LSU AgCenter.
68. Fu P. Estimating Photosynthetic Capacity using Reflectance Spectra and Machine Learning. Nov.7-8, 2025, LaSpace Annual Meeting, Baton Rouge, LA.
69. Fu P. Advanced in high-throughput phenotyping of photosynthesis. The 2nd Machine Learning for Agricultural Research (AgML) Workshop. November 3-5, 2025. Hosted by Helmholtz Centre for Environmental Research – UFZ.
70. Zhou, C. (2025, January). Transforming traditional farming with AI and robotics. Paper presented at the 2025 Louisiana American Society of Agricultural and Biological Engineering Annual Meeting.
71. Grijalva, I. Applying computer vision for site-specific management in Louisiana agroecosystems. Louisiana Land-Grant Agriculture Summit. Baton Rouge, LA, U.S. – Invited presentation.
72. Grijalva, I. Applying computer vision for site-specific management in Louisiana agroecosystems. Louisiana Mosquito Control Association. Baton Rouge, LA, U.S. – Invited presentation. (Project 1 and 2)
73. Grijalva, I. Machine learning vision for insect monitoring and site-specific management. Cornell University. Remotely – Invited presentation. (Project 1 and 2)
74. Grijalva, I. Applying computer vision for site-specific management in Louisiana agroecosystems. Precision Agricultural Summit. Alexandria, LA, U.S. (Project 1 and 2)
75. Grijalva, I. Applying computer vision for site-specific management in Louisiana agroecosystems. American Society of Agricultural and Biological Engineers (ASABE). Alexandria, LA, U.S. – Invited presentation.
76. Grijalva, I. Machine learning for crop insect monitoring. Louisiana Agricultural Consultants Association (LACA). Marksville, LA, U.S. – Invited presentation.
77. Grijalva, I., Upadhyaya S., Davis, J. A., & McCornack, B. Machine Learning Vision for crop insect monitoring. Entomological Society of America. Portland, OR, U.S. – Invited presentation Larry Larson Symposium.
78. Upadhyaya, S., Davis, J. A., Hoffseth, K., & Grijalva, I. Computer vision-based detection of red banded stink bugs (Piezodorus guildinii) and associated damage in soybean seeds. The 15th Annual Entomology Graduate Student Symposium, Louisiana State University. Baton Rouge, LA, U.S. – Research poster.
79. Grijalva, I., Gentimis, T., 2025. Introduction to cloud-based tools for labeling and training object detection models. 2025 AI in Agriculture and Natural Resources Conference. Starkville, MS. Workshop
80. Grijalva, I., 2025. An initial framework for roseau cane scale detection using machine learning. 7th Annual Rosea Cane Research Summit. Baton Rouge, LA. Presentation
81. Broussard, J., Grijalva, I., 2025. Automatic detection of roseau cane scale using machine learning approaches. Entomological Society of America, Southeastern Branch Meeting. Baton Rouge, LA. Presentation
82. Broussard, J., Grijalva, I., 2025. Machine learning models for detecting roseau cane scale. 7th Annual Rosea Cane Research Summit. Baton Rouge, LA. Poster presentation
83. Singh, N., Lu, Y., Tian, Y., Islam, K., 2026. An improved 3D vision pipeline for high-throughput online sweetpotato volume estimation. North American Plant Phenotyping Network (NAPPN) Annual Conference, East Lansing, MI, February 2026.
84. Islam, K., Lu, Y., Brainard, D., Srivastava, A., 2026. Plant height perception by dual-perspective 3D vision for precision mechanical weeding. North American Plant Phenotyping Network (NAPPN) Annual Conference, East Lansing, MI, February 2026.
85. Yeafi, A., Lu, Y., 2026. Single-Shot Demodulation for Structured-Illumination Reflectance Imaging in Poultry Quality Assessment. 2026 AI in Agriculture Conference, Raleigh, NC, April 2026.
86. Hasan, M., Lu, Y., 2026. Machine Vision with Enhanced YOLO for Automated Catfish Fillet Inspection towards Individual Quick Freezing. 2026 AI in Agriculture Conference, Raleigh, NC, April 2026.
87. Mu, X., Lu, Y., 2026. On-ground chestnut detection using self-supervised learning toward autonomous harvesting. Great Lakes EXPO, Grand Rapids, MI, December 2025.
88. Yang, A. Bhujel, M. Bashar, M. Benjamin, D. Morris “Enhanced Piglets Monitoring with a Multiview Camera System” in 2025 ASABE Annual International Meeting, 2025; https://doi.org/10.13031/aim.202501683.
89. Smith, Y. Long, D. Morris “An Automated LED Intervention System for Poultry Piling” in 2025 ASABE Annual International Meeting, 2025; https://doi.org/10.13031/aim.202501648.
90. Chesang, A. K., & Uyeh, Daniel Dooyum (2025). Sensor fusion and augmented reality towards enhanced SfM priors for fruit tree reconstruction. In Three-Dimensional Imaging, Visualization, and Display 2025 (Vol. 13465, p. 134650Z). SPIE.
91. Nwaneri, I., & Uyeh, Daniel Dooyum (2025). AgriMoistNet: A low-cost CNN-based system for moisture content prediction in livestock feed. In Real-Time Image Processing and Deep Learning 2025 (Vol. 13458, p. 1345804). SPIE.
92. Akintan, O. A., & Uyeh, Daniel Dooyum (2025). Can we determine water activity in heterogeneous materials: a computer vision approach. In Pattern Recognition and Prediction XXXVI (Vol. 13464, p. 134640M). SPIE.
93. Oreofeoluwa, A., & Uyeh, Daniel Dooyum (2025). Parameter sensitivity and model fitting for corn leaf moisture using a modified GAB Isotherm.
94. Patience Chizoba Mba^*, Uyeh, Daniel Dooyum. (2025) Empirical characterization of soil water retention and availability for rainfed crop. ASABE-PASAE conference, Morocco
95. Andrew Kibor Chesang, Jacquelyn Perkins, and Uyeh, Daniel Dooyum, (2025) Robotic scouting platform for structural assessment in high-density apple orchards, Great Lakes Fruit Workers Meeting
96. Andrew Kibor Chesang, Jacquelyn Perkins, and Uyeh, Daniel Dooyum, (2025) Autonomous scouting for high-density orchard assessment, Great Lakes Expo
97. Nwaneri, I., & Uyeh, Daniel Dooyum (2025). Automating livestock mixed formulation and feed quality control using semantic segmentation. ASABE 2025 International Meeting, Toronto
98. Chesang, A. K., & Uyeh, Daniel Dooyum (2025). Streaming incrementally reconstructed orchard representations for enhanced situational awareness in teleoperation through virtual reality. ASABE 2025 International Meeting, Toronto
99. Akintan, O. A., & Uyeh, Daniel Dooyum (2025). Estimation of water activity in homogenous materials using multispectral imaging. ASABE 2025 International Meeting, Toronto
100. Dong, Y., (2025). Improving Irrigation Management using an AIoT (Artificial Intelligence of Things) system. 7th Conference of the Pan African Society for Agricultural Engineering. Morocco
101. Rana, S., Dong, Y., (2025). Gap Filling in Leaf Wetness Data: Comparative Analysis of Machine Learning and Hybrid Model Approaches. American Society of Agricultural and Biological Engineers (ASABE) annual meeting. Toronto, Canada.
102. Sun, X., and Mathew, F. 2026. Identification of Sudden Death Syndrome using Hyperspectral Imaging and Deep Learning. 2026 NDSU Soybean Symposium, Fargo, ND. March 5, 2026. (Talk)
103. Farajpoor, P., Pourreza, A., Narimani, M., El‐Kereamy, A., & Fidelibus, M. W. (2025, May). Leaf spectral reflectance prediction using multihead attention neural networks. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (Vol. 13475, pp. 244-251). SPIE.
104. Parsons, Ira., Jameson Brennan, Hector Menendez III. Real-time use of precision livestock technology, and equipment monitoring and management. South Dakota State University Animal Science Research Report. DOI: https://openprairie.sdstate.edu/ans_report_2025. 2025
105. Awasthi, B., Meng, X., Gentimis, T., & KC, M. (2026, February). Forecasting Land Area Dynamics Across Louisiana’s Coastal Wetlands from Landsat Time Series. In 2026 Ocean Sciences Meeting. AGU.