Brief Summary of Minutes of Annual Meeting

See the meeting minutes attached. 

OVERVIEW OF THE GROUP’S OUTPUTS AND ACCOMPLISHMENTS

S1090 is a USDA multistate group founded in 2021 to foster collaboration across US institutions (land grant and others) and industry in the US, where the focus is on artificial intelligence (AI) and digital agriculture. The group started with several institutions within the US Southeastern conference and has since expanded to other land grant (and non-land grant alike) institutions across the nation, with strong industry collaboration. We are in the third year of our current proposal. The proposal laid out three main objectives that are supposed to foster cross-disciplinary and multistate collaborations among our members. This report documents our activities in the last year: 2024/2025. Primarily on collaborative research projects across stations of our members (including the Ag. Machinery and Production industry). It highlights key ideas and issues tackled, the number of personnel trained, the fundings secured as a result of our collaboration, and the meetings we attended where findings of our research outputs were shared nationally and internationally. The group has also organized three AI conferences since its inception, and the last one was hosted in March this year in Mississippi. The report for the conference has been submitted separately.

MILESTONES AND IMPACT SUMMARY

The metrics of impact in the reporting year include a total of 98 peer-reviewed publications, 125 conference presentations/podium/posters, and one extension publication. A total of 119 undergraduate research assistants, 61 MSc students, 92 Ph.D. students, 28 post-docs, 24 other researchers (visiting scientists, research assistants), and a total of 25 farmers/growers/aggregators were trained. A total of 144 projects have been conducted or are being carried out at various stages of execution and are reported by a total of 14 stations in this reporting year.

PROJECTS BY OBJECTIVE AND STATION

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

Project #1: Development of machine learning tools that use satellite, UAV, and other imagery data to predict yields and necessary field inputs.  This work is intended to enhance on-farm decision-making for field crops and rangeland management (Station: KSU)

Project #2: Automated detection of roseau cane scale using computer vision models. This is part of an ongoing project funded by the USDA-APHIS-PPQ. One MSc student is being trained on the project (Station: LSU)

Project #3: Artificial intelligence for stink bug detection in precision pest management. This is part of an ongoing project funded by the Louisiana soybean and grain research and promotion board. We are planning to submit a USDA-NIFA proposal to expand the research this year. One MSc student is being trained on the project (Station: LSU)

Project #4: Detecting insect-damaged soybeans with artificial intelligence solutions. This is part of an ongoing project funded by the Louisiana soybean and grain research and promotion board. One MSc student and one undergraduate are being trained on the project (Station: LSU)

Project#5: From Leaves to Satellites: AI-Powered Estimation of Photosynthetic Capacity for NASA Missions. (NASA and Louisiana Board of Regents Grant) (Station: LSU)

Project #6: Deep Learning-Enabled Detection of Salt Patches in Coastal Louisiana Farmland Using NASA HLS Data (NASA and Louisiana Board of Regents Grant) (Station: LSU)

Project #7: Supporting soybean (Glycine max) and corn (Zea mays L.) crop and nutrient management in Louisiana with digital agriculture technology (Station: LSU)

Project #8: Supporting climate-smart rice production using digital agriculture technology (Station:  LSU)

Project #9: Harnessing remote sensing and AI technology to support sugarcane production and variety development for Louisiana (Station: LSU)

Project#10: We are building capacity 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. Two Master’s students are being trained on these topics under the MAFES/PSS Startup program (Station: Mississippi State)

Project#11: We are developing the Mississippi Soybean – Climate-smart Irrigation Scheduling Tool (MS-CIST) funded by the Mississippi Soybean Promotion Board (MSPB). This project involves crop modeling, genetic coefficient calibration, evapotranspiration measurement, soil moisture sensor integration, thermal remote sensing, and surface energy balance modeling validated in soybean fields. One Master’s student is being trained through this project (Station: Mississippi State)

Project#12: We are implementing an AI-based geospatial Modeling Approach to develop the Mississippi Carbon Monitoring Dashboard (MCMD) (MAFES/SRI funded). This work leverages hyperspectral and multispectral remote sensing, field sampling, and machine learning (ML), deep learning (DL), and explainable AI (XAI) techniques to predict soil organic carbon (SOC) and evaluate land use/land cover impacts. Two PhD students are being trained on this project. (Station: Mississippi State)

Project#13: Through a USDA-ARS internship program, we are developing an AI-enabled time-series temperature and rainfall prediction map across the state. (Station: Mississippi State)

Project#14: We are developing an Evapotranspiration-based Decision Support System to enable climate-resilient sustainable water management strategies for Mississippi farms (USDA-ARS funded). One PhD student is being trained through this project. Station: Mississippi State

Project#15: Under the USDA-ARS project on Baseline Methane Data and Mitigation Measures, we are conducting field experiments and modeling to quantify methane emissions under alternate wetting and drying (AWD) and flooded rice field management using eddy covariance towers, soil data, and remote sensing. One postdoctoral researcher is being trained through this work. (Station: Mississippi State)

Project#16: AI-based approaches are being developed to assess Rhizoctonia root rot severity in soybean. These methods leverage advanced deep learning models, including YOLO, as well as VGG and Inception architectures. This project is in collaboration with Dr. Xin Sun (NDSU, ND). One Research Specialist is being trained on the project. (Station: NDSU)

Project#17: AI models are being developed to predict Phomopsis stem canker of sunflower in a field setting. The project uses Random Forest, deep learning (neural networks), and linear regression. This project is in collaboration with Dr. Hossein Moradi Rekabdarkolaee (Bowling Green State University, OH). We have received funds from USDA-NIFA for this project to determine fungicide efficacy against Phomopsis stem canker. One PhD student is being trained on the project. (Station: NDSU)

Project#18: AI-based approaches are being developed to determine the use of hyperspectral sensors to diagnose sudden death syndrome (SDS) of soybean. This project is in collaboration with Dr. Xin Sun (NDSU, ND). We have received funds from ND Soybean for this project. One Post-doctoral research associate is being trained on the project. (Station: NDSU)

Project#19: A System Dynamics Approach to Enhancing Sustainability in Dairy Systems:  Engineering the Water-Energy-Food Nexus for US Dairy Farms (Station: TAMU)

Project#20: Advancing Precision Livestock Management Through Computer Vision Systems for Bovine Respiratory Disease Detection (Station: TAMU)

Project#21: Design and Development of Artificial Intelligence Driven Decision Models for Sustainable Livestock Systems in United States (Station: TAMU)

Project#22: Artificial Intelligence-Driven Feedlot Economics Visualization Decision Support Tool for Sustainable Beef Production  (Station: TAMU)

Project#23: An Agent-Based Modeling Approach for Balancing Feed Efficiency and Environmental Footprint of Growing-Finishing Pig Production Systems Production  (Station: TAMU)

Project#24: Machine Learning Approach for Price Prediction and Decision Support in United States Cow-calf Operations  (Station: TAMU)

Project#25: A full-stack web application for swine growth simulation and decision support for the growing finishing production phase (Station: TAMU)

Project#26: Digital Twin Framework for Cotton Crop Monitoring (Station: TAMU)

Project#27: Machine Learning Models for Cotton Yield Prediction using Remote Sensing (Station: TAMU)

Project#28: AI-based approaches were developed for body weight estimation applied for poultry farming. A point cloud processing pipeline has been developed for individual bird segmentation, posture classification, and weight regression. This is part of an ongoing multi-state collaborative research funded by USDA. One PhD and one master student are being trained on the project. (Station: TAMU)

Project#29: A series of AI modules has been developed to construct a video processing algorithm for respiratory rate extraction of lateral lying sows. The pipeline can evaluate respiratory, and behavior features of sows around onset of farrowing. The project is expected to be awarded by NIFA in FY 2025. One master student is currently bring trained on the project. (Station: TAMU)

Project#30: AI models were developed for chicken manures inspection with the goal for mortality detection, feed intake estimation, and health condition assessment in caged-layers. One PhD student is being trained on the project. (Station: TAMU)

Project#31: Develop AI-based model for poultry production management, including bird detection, behavior classification, and disease detection. This is part of a collaborative research project with UGA. One postdoc was being trained on the project. (Station: UArk)

Project#32:  In partnership with Jyga Technologies and JBS, building AI-based model for swine production management, focusing on swine body condition monitoring and feeding optimization. The project is funded by the Arkansas Research Alliance. One postdoc and one Ph.D. are trained on the project.  (Station: UArk)

Project#33: AI-based imaging system for poultry meat myopathies classification and texture regression. The project is funded by USDA-NIFA-AFRI seed grant. One M.S. student trained who continues in the lab as Ph.D. (Station: UArk)

Project#34: Crop Yield and Stress Analysis for Indoor Farming, funded by KeySight Technologies  and AI Institute for Food Systems (Station: UC Davis)

Project #35: Fault Detection and Diagnosis and Automation of Sensors for HVAC and Hydroponic Sensors, funded by the California Department of Food and Agriculture (Station: UC Davis)

Project #36: FAI for Thermal Comfort Analysis for Greenhouse Workers, funded by Western Center Agricultural Health and Safety (Station: UC Davis)

Project#37: AI-based approaches were developed for optical technologies, especially machine vision, applied to automated sweetpotato grading and sorting. This is part of an ongoing multi-state collaborative research project funded by USDA Agricultural Marketing Services, involving five universities, including MSU, MS State University, NCSU, UIUC, and LSU. One Postdoc is being trained on the project. (Station: MSU)

Project#38: AI models were developed for machine vision-based weed detection and control. To enhance weed detection, ControlNet-based stable diffusion models were trained to generate multi-class weed images. An improved version of an AI-based smart sprayer for precision vegetable weeding has been built and field tested. We have received funds from the USDA Specialty Crop Block Grant Program and the Michigan Department of Agriculture and Rural Development. One PhD and one MS student are being trained on the project. (Station: MSU)

Project#39: Harvest decision marking is important for blueberry growers to maximize quality and yield. We are developing computer vision and AI tools for automated blueberry counting and harvest maturity estimation. An AI-powered smartphone application for blueberry detection was developed to support blueberry management. This project is funded by the Michigan State University AgBioResearch, which has supported one Postdoc, one PhD student, and one hourly research assistant. (Station: MSU)

Project#40: 3D vision with AI was used for detecting harvestable asparagus to support the effort to develop selective asparagus harvesting technology. The effort was funded by the Michigan State University AgBioResearch. One Postdoc is being trained on the project. (Station: MSU)

Project#41: Poultry meat myopathies, such as white striping and woody breast, downgrade quality and cause significant loss to the U.S. poultry industry. AI models were developed for white striping detection of broiler meat using structured-illumination imaging and light scattering imaging. This work was funded by the Michigan Alliance for Animal Agriculture (M-AAA). One MS student has been trained and graduated. We are planning to submit a standard USDA NIFA AFRI proposal to push forward the research. Institutions involved in the proposal include MSU and Mississippi State University. (Station: MSU)

Project#42: A new project was started on developing machine vision technology with AI for recognizing the status of catfish fillets for automated singulation. This project is funded by USDA-NIFA, in collaboration with Mississippi State University. One MS student is working on the project. (Stations: MSU, Mississippi State)

Project#43: Poultry growers face a number of the challenges in moving to cage-free housing. One of them is piling, when birds gather in tight clusters for potentially hours.  In large aviaries piling can cause stress, suffocation and mortality.  We have been developing an AI system to automatically detect and disrupt piling.  The project is funded by M-AAA.  One PhD student and an undergraduate have been working on this. (Station: MSU)

Project#44: Piglet activity monitoring to reduce pre-weaning mortality.  This is a Tri-partite USDA-NIFA-funded project led by MSU and in collaboration with Queens University Belfast, Teagasc in the Republic of Ireland, as well as University of Nebraska, North Carolina State University, and Agriculture and Agri-food Canada. One postdoctoral researcher is working on this at MSU. (Station: MSU)

Project#45: AI-enabled early plant disease detection framework for vegetables. (Station: UF)

Project#46: An AI-enabled automated nursery monitoring system was also developed to support crop growth.  (Station: UF)

Project#47: An AI-enhanced tool was developed to support the sustainable management of Bahiagrass pastures.(Station: UF)

Project#48: Smart Detection: Early Plant Disease Identification in Vegetables Using AI and Remote Sensing.(Station: UF)

Project#49: AI-enabled Automated Nursery Monitoring System to Support Crop Growth. National Watermelon Association. (Station: UF)

Project#50: built a full strawberry production pipeline—from detection to forecasting and hardware deployment.(Station: UF)

Project#51: develop large language model–based systems to support informed decision-making in agriculture. These models act as conversational agents that can query and interpret geospatial data, enabling farmers and advisors to ask natural-language questions.(Station: UF)

Project#52: explore novel computer vision techniques for behavioral studies in animal production facilities–analyze the behavior of Turkeys in a research facility. (Station: UF)

Project#53: Evaluation of Traditional and AI-Driven On-farm Trial Data. (Station: UKY)

Project#54: In-season Diagnosis of Nitrogen and Water Status for Corn Using UAV Multispectral and Thermal Remote Sensing (Station: UKY)

Project#55:Non-destructive method of hyperspectral imaging (HSI) data for gluten source detection and quantification in foods (Station: UKY)

Project#56:HSI data reconstruction from RGB data for gluten source detection (Station: UKY)

Project#57:Barley seed dormancy and viability prediction

Project#58:Using AI to predict bourbon whiskey age for fraud detection and better flavor development (Station: UKY)

Project#59:Non-destructive method for seed dormancy and viability detection for application in barley malting (Station: UKY)

Project#60: Development of farm animal epigenetic clocks  predictive of gestational age, delivery date, and pregnancy complications. (Station: UKY)

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

Project #1: Development of tools for monitoring planter performance in field conditions continued.  This work will provide data for building AI driven autonomous planting systems.  Information generated by this work will be required as foundational information for AI to make decision. (Station: KSU)

Project #2: Advanced the development of a sensor based system to sense soil compact in a field prior to tillage.  This work will better integrate soil condition information into precision agriculture systems. (Station: KSU)

Project #3: Continued work on developing machine vision test stands to study tillage tool field performance.  Images have been collected for analysis and work has commenced to build analysis tools.  This work will provide foundational knowledge necessary for AI driven autonomous machines. (Station: KSU)

Project#4: We are building a multi-functional end-effector for delicate fruit harvesting. The End-effector has 5-DOF and can perform multiple tasks, from in-hand manipulation to picking and transferring the fruit continuously. The project is funded by MAFES-SRI. One MSc student is being trained on this project. (Station: Mississippi State)

Project #5: We are developing a high-speed and low-energy consumption robotic arm that can be used in agricultural settings for tasks like fruit picking. The project is funded by MAFES-SRI. One MSc student is being trained on this project. (Station: Mississippi State)

Project #6: A collaborative UAV-UGV system is being developed to collect plastic contaminants, particularly plastic bags, from cotton fields. The project is funded by Cotton Incorporated and USDA-ARS. Two MSc students are working on this project. (Station: Mississippi State)

Project #7: A robotic system is being developed to collect sweetpotatoes from the field after the digger brings them up and leaves them on the ground. The project is funded by MDAC. One MSc student is working on it. (Station: Mississippi State)

Project #8: An automation system is being developed to assist with the collection of harvest packages (e.g., cotton, hail, etc.). One PostDoc associate, one PhD student, and one undergraduate researcher are working on this project. (Station: Mississippi State)

Project#9: We are automating the synchronization of harvest vehicles to increase efficiency in agricultural fields. One PhD student and three undergraduate researchers are working on this project. (Station: Mississippi State)

Project#10: A safety mechanism is being developed to detect incidents in agricultural machinery and cut off the power in a fraction of a second. One undergraduate researcher is working on this project. The project is funded by the Mississippi State Office of Research and Economic Development (ORED). (Station: Mississippi State)

Project#11: We have trained a machine learning network to estimate the moisture content and bulk density of grains using microwaves. The project is funded by USDA-ARS, and two undergraduate researchers worked on it. (Station: Mississippi State)

Project#12: We have developed a machine learning model to automatically detect and classify disease symptoms on oat leaves. The project is funded by USDA-ARS, and one undergraduate researchers worked on it. (Station: Mississippi State)

Project#13: Multiple projects focused on AI for autonomous agricultural systems (detection and manipulation, navigation and machine synchronization), under the auspices of the Agricultural Autonomy Institute (www.aai.msstate.edu). (Station: Mississippi State)

Project#14: A robotic device to prevent piglets from getting crushed by the sow is prototyped. The device is controlled by AI that monitor sow’s posture in real-time and identify area that needs the device’s intervention. One master student is trained in this project. (Station: TAMU)

Project#15: An AI-based positioning and path finding algorithm is currently under development to enable drone operation in GPS-denied environment for poultry production. The algorithm is being tested in simulated environment. One PhD student is trained in this project. (Station: TAMU)

Project#16: A mechanical sensor is being developed to evaluate stiffness of chicken breast fillet based on needle’s buckling-indentation depth. An AI model is being trained to identify the buckled needle using RGB image. One PhD student is trained in this project. (Station: TAMU)

Project#17: Using computer vision to predict Bovine Respiratory Disease (Station: TAMU)

Project#18: Using computer vision for precision feeding in beef production systems (Station: TAMU)

Project#19: Using machine learning for price prediction for cow-calf and stocker operations (Station: TAMU)

Project#20: Using machine learning for methane prediction (Station: TAMU)

Project#21: Digital Twin Architecture with Agent-Based Modeling for Optimal Energy and Protein Utilization Monitoring in Beef Systems (Station: TAMU)

Project #22: AI-enabled robotics development for chicken rehanging process. The project is funded by USDA-NIFA-AFRI/NSF NRI 3.0 grant, collaborating with Purdue University. One Ph.D. and two M.S. are trained in the project in Arkansas. (Station: UArk)

Project #23: AI-enabled robotics for poultry processing facility environmental sample collection. The project is funded by USDA-NIFA-AFRI, collaborating with GTRI, UNL, FVSU. One postdoc, one Ph.D., one M.S. are trained in the project. (Station: UArk)

Project #24. Integrate an autosteer system onto an electric tractor for small-scale producers.  This is funded by the Michigan Department of Agriculture and Rural Development (MDARD).  Two undergraduates have been working on this. (Station: MSU)

Project #25. A robotic and smart sprayer for precision weed management in plasticulture vegetable production was developed. This robotic system utilizes machine vision, GPS, IMU, and an AI-enabled smart controller for the detection and localization of weeds and plants (tomatoes and peppers). Based on weed location and size, the smart sprayer is capable of targeting only specific weeds without spraying on the plant and bare ground.(Station: UF)

Project #26: developed a multiple object tracking algorithm that detects and tracks plants observed by mobile robotic platforms equipped with video cameras. The model consists of two main components: i) a tracking module that uses bounding box regression and non-maximum suppression to associate predicted tracks with new detections, and ii) a spatial association module that uses a local feature matching transformer to perform visual odometry.​ Building on this work, we developed a model that replaces the typical detection association module by a contrastive learning strategy that allows the model to directly learn highly discriminative features to associate detections among frames. (Station: UF)

OBJECTIVE 1C: Natural resources scouting and monitoring.

Project #1: Developing new sensor to detect volumetric water content, nitrate-nitrogen, and ammonium-nitrogen through soil permittivity.  Laboratory experiments are being conducted at controlled moisture and fertilizer levels.  These experiments are being compared to field data collected during the corn growing season.  This work will provide a new sensor input for AI. (Station: KSU)

Project #2: Developing a robot, the “Scumbot”, to monitor blue green algae in a public reservoir.  The Scumbot, a robotic kayak can operate autonomously.  It is capable of collecting geolocated water samples without human guidance.  This allows for the monitor of nutrient loading in reservoir to better understand the dynamics of a blue green algae bloom. (Station: KSU)

Project #3: AI based calibration transfer techniques were tested to transfer fine-ground soil spectral models to predict non-fine-ground soil spectra in the mid infrared region. This study which is a part of an ongoing USDA-NRCS funded project, is already published in a peer-reviewed journal. One graduate student is trained on this project. (Station: Mississippi State)

Project #4: Different VisNIR and MIR spectroscopic techniques and the potential for calibration transfer between different instruments were tested using soil samples obtained from Mississippi. This study is published in a peer-reviewed journal and one graduate student is trained. (Station: Mississippi State)

Project #5:  Developing an automated, web-based soil property estimation tool using mid-infrared (MIR) spectroscopy and machine learning. Robust calibration transfer and transfer learning algorithms are also developed in this project to transfer MIR spectra collected by secondary instruments to those measured by the primary instrument to reduce the inconsistency and errors in the modeling and estimation. This project is funded by USDA NRCS. It is a multistate collaborative project including OSU and UW-Madison. One PhD student and one undergraduate are being trained on this project. (Stations: Oregon State, UW-Madison)

Project #6: SpectrAnd: Spectroscopy for rapid identification of Andic soil properties. We will build robust ML/AI models with extremely sparse samples by incorporating expert knowledge (knowledge-guided). This project is funded by USDA NRCS. It is a multistate collaborative project including OSU and NMSU. One PhD student is being trained on this project. (Station: Oregon State)

Project #7: Advanced sensing technologies for soil health assessment in sustainable hop cultivation. We will use MIR spectra and data fusion to build efficient ML models for rapid soil health assessment and detect soil health changes caused by sustainable hop management (such as no-till, cover crop). This project is funded by Oregon Agricultural Research Foundation. One PhD student and two undergraduate students are being trained on this project.  (Station: Oregon State)

Project #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. This is a collaborative project with USDA ARS. One PhD student is being trained on this project. (Station: Oregon State)

Project #9: Interactive mapping of soil and surrounding environment in Cascade Siskiyou National Monument. We are creating an interactive mapping tool with multi-source remote sensing and ground truth data to visualize soil and environmental variables, thereby enhancing public engagement. This project is funded by Friends of the Cascade Siskiyou National Monument Fund. One undergraduate student is being trained on this project. (Station: Oregon State)

Project #10: A Real-time Data Integration and Visualization Framework for Monitoring Beef Cattle Performance and Emissions Data from Precision Livestock Technologies (Station: TAMU)

Project #11: An Agent-Based Framework of Cattle Value Discovery System for Precision Nutrient Requirements and Utilization Prediction of Beef Cattle (Station: TAMU)

Project #12: developed a set of deep learning models to estimate key soil properties at global scale using multimodal Earth observation and in-situ data. The models leverage convolutional neural networks and ensemble learning strategies, achieving higher accuracy than existing baselines across diverse soil attributes. In addition, the modular design allows the models to be retrained and extended for new regions and datasets.(Station: UF)

Project #13:  Sensitivity Evaluation of Visible Near-Infrared Spectroscopy Data to Variable-Rate Soil Moisture for AI-Driven Prediction of Soil Properties (Station: UKY)
Project #14: Improvement of Soil Spectral Prediction for Plant-Available Nutrients Using Machine and Deep Learning algorithms (Station: UKY)
Project #15:  Evaluating the Permeation of 3-Dimensional Technologies into Soil Science Education: Literature Review and Case Study (Station: UKY)
Project #16: Evaluating Fire Chronology Impacts on Soil Properties Using VNIR Spectroscopy in Southeastern Pine Flatwoods(Station: UKY)
Project #17: R package development: Integration of 20 pre-processing methods and four parametric and non-parametric calibration models for soil predictions with visible-near-infrared spectroscopy data (Station: UKY)

OBJECTIVE 1D: Socioeconomic sustainability

Project #1: Developed a system dynamics model for climate smart beef production systems (Station: TAMU)

Project #2: Developed a system dynamics model for economics of BRD in beef production systems (Station: TAMU)

Project #3: Developed a system dynamics model for economics of cow-calf operations in beef production systems (Station: TAMU)

Project #4:Evaluation of Agronomic, Economic, and Environmental Benefits of Remote Sensing and AI-Based Calibration Strip Technology for In-season Nitrogen Application for Corn (Station: UKY, UMN)
Project #5:Assessment of Attainable Soil Carbon Sequestration Capability by Depths and Crops Using Econometric Techniques (Station: UKY)
Project #6:Assessment of Adoption Rates for Precision Agriculture Technologies and Soil Health Practices in Kentucky (Station: UKY)

OBJECTIVE 1E: Phenotyping and genotyping

Project #1: Developing AI tools to estimate biomass, nutrient content, existing cover crop species, and soil moisture content in production fields.  These tools are foundational to the development of automated machines. (Station: KSU)

Project #2: AI-Based Methodologies for Major Crops in Louisiana. Developed and validated a data science pipeline for crop yield prediction using Extreme Gradient Boosting for the three major Breeding programs in Louisiana (Rice, Soybeans, Sugarcane). Conducted a comprehensive audit of over 10,000 agronomic trial records, identifying and documenting gaps in metadata, soil/weather coverage, and trait standardization—offering concrete recommendations for improving future statewide research infrastructure. (Station: LSU)

Project #3:Support UAS-based phenotyping for wheat breeding programs  (Station: TAMU)

Project #4:Develop tool to evaluate genotypes with respect to growth and development using UAS based canopy features (Station: TAMU)

Project #5: AI-enabled blackberry phenotyping from handheld devices and UAV platforms. The project is funded by the Arkansas Research Alliance. One Ph.D. is trained in the project. (Station: UArk)

Project#6: A project to remotely phenotype swine is ongoing.  This uses a camera to monitor pigs in pens or walking down hallways for health-related issues, including poor body condition and lameness. This project is funded by USDA-NIFA in collaboration with the University of Nebraska. Two PhD students at MSU have worked on it. (Station: MSU)

Project#7: develop an AI-enabled plant phenotyping tool for precision nutrient management in tomatoes, utilizing ground and aerial remote sensing.(Station: UF)

Project#8: AI-enabled plant phenotyping for novel nutrient management in tomatoes. Florida Specialty Crop Block Grant Program.(Station: UF)
Project#9: Detecting plants and measuring their organs using bounding boxes to mitigate frequent occlusions.(Station: UF)

Project#10: Efficient Pasture Monitoring with Deep Learning-Based Estimation for Tall Fescue Abundance.(Station: UKY)

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

Project #1:  Developing new machine use data that can be used to estimate fuel and operational costs of agricultural equipment.  Most of the existing data is nearly 40 years old.  Modern agricultural equipment has advanced and increased in efficiency and cost.  The old data is foundational to the USDA agricultural production cost models, which means these cost models do not truly represent actual costs. (Station: KSU)

Project #2:  Systems that autonomously identify and geolocate weed and insect infestations are under development.  These new systems include the development of hardware, software, and databases that will automate the task of field scouting.  These systems will be more accurate and less labor-intensive methods to create field maps that can be used with other precision agricultural tools to increase productivity and reduce production costs. (Station: KSU)

Project #3: Blockchain for Beef Traceability and Better Markets (Station: TAMU)

Project#4: The lack of public, large-scale weed datasets is considered a bottleneck to developing robust machine vision-based weeding systems. To alleviate this, we have been developing public weed datasets. In the Years 2024-2025, we released a three-season weed detection dataset in the Zenodo repository and associated software programs for AI models for weed detection. We also experimented with generative modeling through stable diffusion to augment multi-class weed image data for enhanced weed detection. (Station: MSU)

Project#5: engaged with several other members of the S1090 project to create comprehensive datasets to evaluate our detection, segmentation, and tracking methods.(Station: UF)

Project#6: advance efforts on microplastics self-supervised learning (SSL) and diesel-contaminated soil detection in agricultural systems. Publications and accompanying data archives are planned for release following peer review.(Station: UF)
Project#7: Participation in P4005 working group of IEEE to develop standards and protocols for spectroscopy.(Station: UF)

Project#8: ​​Development of Vis-NIR and MIR spectral library for Soil in the Kentucky State (Station: UKY)

Project#9: ​​Develop open-source, public agricultural datasets for benchmarking AI algorithms with a focus on explainability (Station: UKY)
Project#10: ​​Standardization and testbed development – data standardization and software development (Station: UKY)

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

Project #1: A test stand was completed to measure rolling resistance on agricultural tires.  Initial testing has been conducted and the results were inconclusive.  More development work is needed on the test stand. (Station: KSU)

Project #2:  Grazing Simulation Bot (Cattle–Elk Conflict) An educational AI agent built to simulate natural resource negotiations between ranchers and the U.S. Forest Service. Includes land degradation simulation, role-play essay feedback, and strategic adaptation across multiple rounds. (Station: OKState)

Project #3: Essay Feedback Bots for Ethics & Environmental Policy: Automatically critiques and refines student writing on topics like pollution control, Kantian ethics, and environmental justice. (Stations: OKState)

Project #4: Farm Financial Coaching Agents: Includes depreciation schedule bots, enterprise budget builders, and loan application agents to guide students through hands-on financial planning. (Station: OKState)

Project #5: AI Grading Assistants: Piloted classroom AI agents that provide targeted essay evaluations using custom system prompts aligned with rubrics and ethical reasoning frameworks. (Stations: OKState)

Project #6: Computer vision pipeline for cattle activity classification (Station: TAMU)

Project #7: Farm data management pipeline (Station: TAMU)

Project#8: ​​Development of KY Statewide Protocol for Adaption of Emerging Technologies for Soil Testing(Station: UKY)

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

Project #1: Automation and Mechanization Technology and Labor for Specialty crops. (Station: LSU)

Project #2: Considerations on AI Adoption for Academic Programs: Ethics, Teaching and Outreach (Station: LSU)

Project #3: Data management training for disintegrated beef production sectors for farmers, and industry (Station: TAMU)

Project #4:Development of Online Platform for Automated Fertilizer Prescription Calculation Driven by (AI and) Remote Sensing-Based Calibration Strip Technology: only first prototype was developed. The AI algorithm needs to be added in the future for advanced calculation of fertilizer recommendation rates. (Station: UKY)
Project #5:Development of Cheap and Rapid Soil Testing Service Using Spectroscopy(Station: UKY)
Project #6:Development of Campus-wide AI Agrifood Institute (Station: UKY)
Project #7:Precision Management for Agriculture and Natural Resources Conservation will be taught in 2025 fall (Station: UKY)

Refereed Journals/Book Chapters

KSU:

1. Ravinder Singha*, Sehijpreet Kaurb, Deepak R. Joshi, et al. (2025). Estimating Cotton Biomass and Nitrogen Content using Satellite and UAV Data Fusion with Machine Learning. Smart Agriculture Technology.
2. Tulsi P. Kharel*, Heather L. Tyler, Partson Mubvumba, et al. (2025). Machine learning on multi-spectral imagery to estimate nutrient yield of mixed species cover crops. Agricultural & Environmental Letters.
3. Deepak R. Joshi, David E. Clay*, Ron Alverson, et al. (2025). Tillage intensity reductions when combined with yield increases may slow soil carbon saturation in the central United States. Scientific Reports.
4. Dipankar Mandal, Raj Khosla*, Louis Longchanps and Deepak R. Joshi. (2025).Soil Moisture Sensor Location-allocation using Spatial Association of Surface Moisture Data. Smart Agricultural Technology.
5. Janet Moriles-Miller, Deepak R. Joshi, Graig Reicks, et al. (2025). Delaying Cover Crop Termination Reduced Corn Yields in a Dry Spring. Agrosystems, Geosciences & Environment.
6. Rai, S., R. Slichter, A. Dalal, A. Sharda. (2025) Enhancing seeding efficiency using a computer vision system to monitor furrow quality in real-time. Precision agriculture ‘25.  Pages 1038-1044.
7. Skye Brugler, David E. Clay, Deepak R. Joshi, Thandi Nleya, Garry Hatfield and Sharon A. Clay. (2025). Does splitting the nitrogen rate reduce carbon equivalents? Agronomy Journal.
8. Shailesh Pandit, Graig Reicks, Janet Moriles-Miller, et al. (2025). Rye as a Cover Crop Reduces Methane Emission Until its Termination. Agronomy Journal.
9. Mansur, H., Gadhwal, M., Abon, J. E., & Flippo, D. (2025). Mapping for Autonomous Navigation of Agricultural Robots Through Crop Rows Using UAV. Agriculture, 15(8), 882.
10. Rahman, R., Indris, C., Bramesfeld, G., et al. (2024). A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields. Journal of Imaging, 10(5), 114.
11. Pokaharel, P., A. Sharda, D. Flippo, K. Ladino (2024). Design and systematic evaluation of an under-canopy robotic spray system for row crops. Smart Agriculture Technology, Vol. 8 100510 ISSN 2772-3755.
12. Hasib Mansur, Stephen Welch, Daniel Flippo. (2025). A novel way of using flex sensors as tactile sensors in agricultural robotics: A proof-of-concept study. TechRxiv. May 01, 2025.
13. Singh, R., J. Fabula, K. Shende, A. Sharda. (2025). Spray Coverage and Droplet Size Uniformity of Pulse Width Modulation (PWM) Systems at Different Duty Cycles and Frequencies. Applied Engineering in Agriculture. 41(2): 119-124. 

LSU:

1. Moseley, D., Reis, A., Parvez, A., et al. 2025. Using variety testing data to select soybean varieties: Guidelines for practitioners. Crop, Forace, & Turfgrass Management. Accepted (Aug 6, 2025). Current status: In production (Sep 5, 2025). 
2. Poudel, A., Burns, D., Adhikari, R., et al. 2025. Cover crop biomass predictions with Unmanned Aerial Vehicle remote sensing and TensorFlow machine learning. Drones. 9(2), 131.
3. Acharya, B., Dodla, S., Tubana, B., et al. 2025. Characterizing optimum N rate in waterlogged maize (Zea mays L.) with Unmanned Aerial Vehicle (UAV) remote sensing. Agronomy. 15(2), 434.
4. de Souza, F.L.P., Dias, M.A., Setiyono, T.D., Campos, S., Shiratsuchi, L.S., Tao, H. 2025 Identification of soybean planting gaps using machine learning. Smart Agricultural Technology. 10, 100779.
5. de Souza, F.L.P., Shiratsuchi, L.S., Dias, M. A., et al. 2025. A neural network approach employed to classify soybean plants using multi-sensor images. Precision Agriculture. 26, 32.
6. Bampasidou, M. and J. Fields. 2025. “Is Labor Shortage Pushing Towards Automation and Mechanization of the US Nursery Industry?” Choices 40,1 
7. Bampasidou, M. et al. 2025. Navigating Emerging Technologies in Specialty Crops: Production, Labor and Ethical Considerations. (Theme proposal Choices). Choices 40,1
8. Hasan, M. R.*, K. P. Paudel, M. Regmi, et al. 2025. “Toward Rice Production Self-Sufficiency in Bangladesh: The Role of Plot Attributes, Farmer Characteristics, and Technology”, Journal of Agricultural and Resource Economics

Mississippi State

1. Gamagedara, Y., Wijewardane, N. K., Feng, G., et al. (2024). Can we use a mid-infrared fine-ground soil spectral library to predict non-fine-ground spectra?. Geoderma, 443, 116799.
2. Silva, F. H. C. A., Wijewardane, N. K., Cox, et al. (2025). Assessment of different VisNIR and MIR spectroscopic techniques and the potential of calibration transfer between MIR laboratory and portable instruments to estimate soil properties. Soil and Tillage Research, 251, 106555.
3. H Gharakhani, JA Thomasson, Y Lu, KR Reddy.  (2024). Field test and evaluation of an innovative vision-guided robotic cotton harvester.  Computers and Electronics in Agriculture 225, 109314.  2024.
4. PK Yadav, JA Thomasson, R Hardin, SW Searcy, U Braga-Neto (2024). AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application. Remote Sensing 16 (15), 2754.

NDSU: 

1. Mathew, F., Kaur, H., George, M., Mohan, K., Mukaila, T., Rafi, N., & Clay, S. Plant disease identification and management using remote sensing. In D. K. Shannon, D. E. Clay, & N. R. Kitchen (Eds.), Precision agriculture basics (2nd ed.). American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc. 

OKState:

1. Vitale, J., & Robinson, J. (2025). In-Season Price Forecasting in Cotton Futures Markets Using ARIMA, Neural Network, and LSTM Machine Learning Models. Journal of Risk and Financial Management, 18(2), 93.
2. Council for Agricultural Science and Technology. (2024). AI in Agriculture: Opportunities, Challenges, and Recommendations. Issue Paper 75. 

Oregon State: 

1. Weerasekara, M., Hartemink, A.E., Zhang, Y., Stevenson, A., 2024. Spectral signatures of soil horizons and soil orders from Wisconsin. Soil Science Society of America Journal, 1–18.
2. Ghimire, S., Zhang, Y., Huang, J., et al., 2025. Using mid-infrared spectroscopy to estimate soil microbial properties at the continental scale. Applied Soil Ecology 211, 106110.

SDSU:

1. Wongpiyabovorn, O., Wang, T., Menendez, H., & Yago, A. L. (2025). Precision Livestock Farming Technologies in Beef Cattle Production: Current and Future. Choices, 40(2), 1-8.
2. Cho, W. & Wang, T. 2025. “From Farm Kids to Ag Tech Leaders: Who’s Driving Precision Agriculture?”. Forthcoming at Choices Magazine.

TAMU:

1. Rahimifar, K. Kaniyamattam, J. Wiegert and L. O. Tedeschi. 2025. A stochastic dynamic model for nutrient requirement and utilization prediction of U.S. based grow-finish swine production systems. J. Ani. Sci. (In Press). (agent-based modeling)
2. H. A. Samad, R. V. Suhana, K. Vineeth, et al. 2025. An assessment of climate-smart strategies for cleaner, sustainable, and carbon-neutral livestock systems in India. Sustainability, 17(5), 2105.
3. Tedeschi, L. O., Johnson, D. C., Atzori, A. S., et al. 2024. Applying Systems Thinking to Sustainable Beef Production Management: Modeling-Based Evidence for Enhancing Ecosystem Services. Systems, 12(11), 446.
4. Pal P., J. Landivar, J. L. Scott, et al. 2025. Unmanned Aerial System and Machine Learning Driven Digital-Twin Framework for Cotton Crop Forecasting. Computer and Electronics in Agriculture, 228, 109589.
5. Reddy J., Niu H., J. L. Scott, et al. 2024. Cotton yield prediction via UAV-based cotton bolls image segmentation using YOLO and SAM Models. Remote Sensing, 16, 4346.
6. Khuimphukhieo I., M. Bhandari, J. Enciso, J. A. da Silva. 2025. Estimating sugarcane yield and its components using unmanned aerial systems (UAS)- based High throughput phenotyping (HTP). Computers and Electronics in Agriculture. (in press)
7. Rose, D. C., Crouch, K., Germundsson, L. B., Gaspard, M., Giller, O., Ortolani, L., & Strong, R. (in-press). Supporting the adoption of digital technology on-farm: ten tips for Extension. Journal of Extension. 
8. Lee, C-L., & Strong, R. (2025). What factors prevent sustainable agriculture science from being applied?: Understanding U.S. Extension professionals’ intentions to promote precision agriculture technologies. Discover Sustainability, 6(445).           
9. Mulkerrins, M., Strong, R., Kilboyle, J., et al. (2025). The Influence of Digital Knowledge Exchange on Advancing Irish Students Knowledge and Adoption of Sustainable Grassland Management Innovations. Journal of International Agricultural and Extension Education, 32(1).            
10. Paudel, D., Kallenberg, M., Ofori-Ampofo, S., et al. (2025). CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecasting. Earth Systems Science Data.     

UArk: 

1. Pallerla, C., Feng, Y., Owens, C. M., et al. (2024). Neural network architecture search enabled wide-deep learning (NAS-WD) for spatially heterogenous property awared chicken woody breast classification and hardness regression. Artificial Intelligence in Agriculture.
2. Bist, R. B., Bist, K., Poudel, S., et al. (2024). Sustainable poultry farming practices: a critical review of current strategies and future prospects. Poultry Science, 104295. 
3. Li, Z., Wang, D., Zhu, T., Tao, Y., & Ni, C. (2024). Review of deep learning-based methods for non-destructive evaluation of agricultural products. Biosystems Engineering, 245, 56-83. 
4. Wang, D., Sethu, S., Nathan, S., et al. (2024). Is human perception reliable? Toward illumination robust food freshness prediction from food appearance—Taking lettuce freshness evaluation as an example. Journal of Food Engineering, 112179. 
5. Xu, Z., Uppuluri, R., Zhang, X., et al. (2025). UniT: Data Efficient Tactile Representation with Generalization to Unseen Objects. IEEE Robotics and Automation Letters. 
6. Sohrabipour, P., Pallerla, C. K. R., Davar, A., et al. (2025). Cost-Effective Active Laser Scanning System for Depth-Aware Deep-Learning-Based Instance Segmentation in Poultry Processing. AgriEngineering, 7(3), 77. 
7. Mahmoudi, S., Davar, A., Sohrabipour, et al. (2024). Leveraging Imitation Learning in Agricultural Robotics: A Comprehensive Survey and Comparative Analysis. Frontiers in Robotics and AI, 11, 1441312 (1B)
8. Crandall, P. G., O’Bryan, C. A., Wang, D., et al. (2024). Environmental monitoring in food manufacturing: Current perspectives and emerging frontiers. Food Control, 110269. 
9. Tagoe, A., Silva, A., Koparan, C., et al. (2024). Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing. AgriEngineering, 6(4), 4549-4569. 

UCDavis:

1. Karimzadeh, S., Li, Z., & Ahamed, M. S. (2025). Machine learning-based fault detection and diagnosis of electrical conductivity and pH sensors in hydroponic systems. Computers and Electronics in Agriculture, 237, 110544. 
2. Li, Z., Karimzadeh, S., Chavanapanit, A., et al. (2025). Detection of Calcium Deficiency in Indoor-Grown Lettuce under LED Lighting using Computer Vision. Smart Agricultural Technology, 101144. 

MSU:

1. Xu, J., Lu, Y., Deng, B., 2024. Design, prototyping, and evaluation of a new machine vision-based automated sweetpotato grading and sorting system. Journal of the ASABE 67 (5), 1369-1380.
2. Deng, B., Lu, Y., 2025. Weed image augmentation by ControlNet-added stable diffusion for multi-class weed detection. Computers and Electronics in Agriculture 232, 110123.
3. Deng, B., Lu, Y., Vander Weide, J., 2025. Development and preliminary evaluation of a YOLO-based fruit counting and maturity evaluation mobile application for blueberries. Applied Engineering in Agriculture 41(3), 391-399.
4. Deng, B., Lu, Y., Li, Z., 2024. Detection, counting, and maturity assessment of blueberries in canopy images using YOLOv8 and YOLOv9. Smart Agricultural Technology 9, 100620.
5. Xu, J., Lu, Y., 2025. 3D vision-based perception and length estimation of green asparagus for selective harvesting. Journal of the ASABE 68 (2), 239-256.
6. Cai, J., Lu, Y., 2025. Detection of woody breast condition in broiler breast fillets using light scattering imaging. Journal of the ASABE 68 (1), 13-24.
7. Cai, J., Lu, Y., 2025. Assessment of woody breast in broiler breast fillets using structured-illumination reflectance imaging coupled with surface profilometry. Journal of Food Engineering 391, 112459.
8. Yao, T., Jing, Y., Lu, Y., Liu, W., Lyv, J., Zhang, X., Chang, S., 2024. Recognition of catfish fillets using computer vision toward automated singulation. Journal of Food Process Engineering 47, e14726.
9. Bhujel, A., Wang, Y., Lu, Y., Morris, D., Dangol, M., 2025. A systematic survey of public computer vision datasets for precision livestock farming. Computers and Electronics in Agriculture 229, 109718.

UF:

1. Vijayakumar V., Ampatzidis Y., Lacerda C., et al., 2025. AI-driven real-time weed detection and robotic smart spraying for optimized performance and operational speed in vegetable production. Biosystems Engineering, 259, 104288.
2. Zhou C., Ampatzidis Y., Guan H., et al., 2025. Agrosense: Accelerating precision orchard management through an AI-enabled monitoring system. Precision Agriculture, 26(4), 73.
3. 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.
4. 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.
5. Li X., Huang F., Sun H., et al., 2025. A bio-inspired framework for apple leaf disease detection: integrating lesion localization, ant colony optimization, and machine learning. Smart Agricultural Technology, 101141.
6. 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.
7. Kunwar S., Babar A., Jarquin D., Ampatzidis Y., et al., 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.
8. McBreen J., Babar A., Jarquin D., et al., 2025. Leveraging multi-omics data with machine learning to predict grain yield in small vs. big plot wheat trials. Agronomy, 15(6), 1315.
9. 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.
10. Tulu B., Teshome F.T., Ampatzidis Y., et al., 2025. AgriSenAI: Automating UAV thermal and multispectral image processing for precision agriculture. SoftwareX, 30, 102083.
11. Javidan S.M., Ampatzidis Y., Banakar A., et al., 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.
12. McBreen J., Babar A., Jarquin D., et al., 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).
13. Chen, Y., A. Shu, Z. Liu, Y. Chen, et al. 2025. SP-RTSD: A lightweight real-time strawberry detection on edge devices for onboard robotic harvesting. Journal of Field Robotics, 2025; 1-19.
14. Tapia, R., W.S. Lee, V.M. Whitaker, and S. Lee. 2025. Multiple methods for predicting strawberry powdery mildew severity from field canopy reflectance data. PhytoFrontiers.
15. Kim, J.-H., Y.-H. Cho, K.-M. Kim, et al. (2025). Sweet potato farming in the USA and South Korea: A comparative study of cultivation pattern and mechanization status. Journal of Biosystems Engineering 50:210–224.
16. Huang, Z., W. S. Lee, P. Yang, et al. 2025. Advanced canopy size estimation in strawberry production: a machine learning approach using YOLOv11 and SAM. Computers and Electronics in Agriculture 236 (2025), 110501.
17. Huang, Z., W. S. Lee, P. Zhang, et al. 2025. SASP: segment any strawberry plant, an end-to-end strawberry canopy volume estimation. Smart Agricultural Technology 11 (2025) 101017.
18. Pardo-Beainy, C., C. Parra, L. Solaque, and W. S. Lee. 2025. Deep learning and georeferenced RGB-D imaging for hydroponic strawberry yield mapping. Smart Agricultural Technology 12 (2025) 101293.
19. 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.
20. Hernandez, B., & Medeiros, H. (2024). Multi-object tracking in agricultural applications using a vision transformer for spatial association. Computers and Electronics in Agriculture, 226, 109379.
21. Medeiros, H., Tabb, A., Stewart, S., & Leskey, T. (2025). Detecting invasive insects using Uncrewed Aerial Vehicles and Variational AutoEncoders. Computers and Electronics in Agriculture, 236, 110362.
22. Gallios, I.,Tziolas, N., & Tsakiridis, N. (2025). Federated learning applications in soil spectroscopy. Geoderma, 456, 117259.
23. Kalopesa, E. Tziolas, N., Tsakiridis, N.L., Safanelli, J.L., Hengl, T., Sanderman, J. (2025). Large-Scale Soil Organic Carbon Estimation via a Multisource Data Fusion Approach. Remote Sensing. 17, 771.  
24. Zhao, X., Xiong, Z., Karlshöfer, P., et al., (2025). Soil organic carbon estimation using spaceborne hyperspectral composites on a large scale. International Journal of Applied Earth Observation and Geoinformation.
25. Demattê, J. A. M., Rizzo, R., Rosin, N. A., et al. (2025). A global soil spectral grid based on space sensing. Science of the Total Environment, 968, 178791.
26. Novais, J. J. M., Melo, B. M. D., Junior, et al. (2025). Online analysis of Amazon's soils through reflectance spectroscopy and cloud computing can support policies and the sustainable development. Journal of Environmental Management, 375, 124155.
27. Ads, A., Tziolas, N. Al Shehhi, M. R. (2025) Quantitative Analysis of Water, Heat, and Salinity Dynamics During Bare Soil Evaporation. Hydrology.
28. Demattê, J. A. M., Poppiel, R. R., Novais, et al. (2025). Frontiers in earth observation for global soil properties assessment linked to environmental and socio-economic factors. The Innovation, 6, 100985.

UKY:

1. Thomasson, J. A., Ampatzidis, Y., Bhandari, M., et al. (2025). AI in Agriculture: Opportunities, Challenges, and Recommendations. Council for Agricultural Science and Technology
2. Souza, E.F., Fernández, F.G., Fabrizzi, K.P., et al. (2025). Precipitation influences pre‐sidedress soil nitrate thresholds for corn production. Soil Science Society of America Journal, 89(3), p.e70085.
3. Ragland, J., Egli, D., Mizuta, K., Greb, S., Levy, J.E. (2005). The role of phosphorus in Kentucky Agricultural Development: A story of the haves and the have-nots. University of Kentucky Cooperative Extension Service. ID-278
4. Ekramirad, N., Doyle, L.E., Loeb, J.R., Santra, D., Adedeji, A.A. (2024). Hyperspectral imaging and machine learning as a nondestructive method for proso millet seed detection and classification. Foods 13(9), 1330.
5. Tizhe Liberty, J., Sun, S., Kucha, C., Adedeji, A. A., Agidi, G., & Ngadi, M. O. (2024). Augmented reality for food quality assessment: bridging the physical and digital worlds. Journal of Food Engineering 367, 111893.
6. Anand, L., Combs, D. (2025). “chromoMap: A tool for interactive visualization if multi-omics data and annotation of chromosomes”. (Innovation Report Application: OI2025-00810). Disclosure date May 25, 2025
7. Rodríguez López C.M. (2025). “A Non-Invasive Method To Predict Gestational Conditions In Pregnant Mares”. (Provisional Patent Application: WO2011157995). Filed April 3, 2025

ALL STATION CONFERENCE PRESENTATIONS: PODIUM/POSTER

KSU:

1. Deepak R. Joshi, David Clay, Sharon Clay and Prakriti Sharma. Multisource Data Fusion and Machine Learning for Accurate on-Farm Corn Yield Prediction.  ASA, CSSA and SSSA International Annual Meeting.  November 9-12, 2024, San Antonio, TX.   
2. Jitendar Rathore, Deepak R. Joshi, Halimeh Abuayyash, et al. Co-designing a Zone-Specific On-farm Digital Support System for Crop Yield Prediction. AGU International Conference, December 9-13, 2024, Washington, D.C. 
3. Jitendar Rathore, Deepak R. Joshi, et al. Employing random forest, support vector machine learning models, and Planet Scope satellite data to predict crop yield on the farm. AGU International Conference, December 9-13, 2024, Washington, D.C. 
4. Tulsi P. Kharel*, Heather L. Tyler, Partson Mubvumba, et al., Mixed Species Cover Crop and Nutrient Yield Estimation Using Multi-Spectral Drone Imagery. ASA, CSSA and SSSA International Annual Meeting.   November 9-12, 2024, San Antonio, TX. 
5. Dua, S., B. Aryal, J. Peiretti, A. Sharda. (2025) Optimization of GWL margin to enhance planter performance. 2025 ASABE Annual International Meeting 2501079. 
6. Kaloya, T., A. Sharda. (2025) Quantifying Horizontal and Vertical Movement Accuracy in Agricultural Sprayer Booms Using a Distance Quantifier System Integrated with GPS and CAN Networks. 2025 ASABE Annual International Meeting. 2501029
7. Janbazialamdari, S., E. Brokesh.  (2025) Enhancing Soil Compaction Prediction in Precision Agriculture Using Advanced Machine Learning Models. 2025 ASABE Annual International Meeting. 2501409
8. Singh, R.,  A. Sharda (2025) A Comprehensive Assessment of Fertilizer Response Across Diverse Nutrient Application Strategies for Enhanced Crop Vigor and Yield. 2025 ASABE Annual International Meeting. 2500460 
9. Sharda, A., J. Peiretti, (2025).  Assessing Toolbar Location's Impact on Autonomous Regulation in Row Crop Planters. 2025 ASABE Annual International Meeting. 2501642 
10. Vail, B., A. Sharda, B. McCornack. (2025) Development and Testing of a Seed Placement Data Collection System for Wheat Drill Row Units Using Computer Vision. 2025 ASABE Annual International Meeting.  2501558 
11. Dua, A., A. Sharda, W. Schapaugh, R. Hessel. (2025) Automated tool for rapid data analytics of remotely sensed data for phenotypic and precision agriculture applications. 2025 ASABE Annual International Meeting. 2500879 
12. Dua, A., A. Sharda, W. Schapaugh, R. Hessel. (2025) Ry Sense: An Automated Tool for Rapid Yield Predictions of UAV based Remotely Sensed Data for Field Based Breeding Programs. 2025 ASABE Annual International Meeting. 2500879
13. Deepak Joshi. How AI and Precision Agriculture Enhance Farm Decision-Making and Efficiency. Sustainable Agronomy Conference 2025, July 9-30, 2025 (Virtual), Organized by American Society of Agronomy.  
14. Deepak Joshi. Utilization of Different Data Layers for Precision Decision Making in On-farm Research. Data Tech Conference 2025, May 16, 2025, Minneapolis, MN, Organized by MinneAnalytics.  
15. Deepak Joshi. Spray Drone in Pasture and Ranch Management. AI in Kansas Agriculture Conference, July 22, 2025, Lyndon, KS 
16. Deepak Joshi. Management Zones & Variable Rate Technology in Precision Agriculture. Kansas Agricultural Technology Conference, March 7, 2025, Clay Center, KS, organized by Kansas Agricultural Research and Technology Association (KARTA). 
17. Deepak Joshi. Utilization of Different Data Layers for Precision Decision Making in On-farm Research. K-State Plant Pathology Department Seminar, January 30, 2025, in Manhattan, KS.
18. Sharda, Ajay. Future of AI and Autonomy. Lincoln Agritech at Lincoln University, Lincoln, New Zealand. Feb 17^th, 2025.
19. Sharda, Ajay.  AI and Autonomy in Ag. AI in Ag Conference. Mississippi State University. April 1^st, 2025  
20. Sharda, Ajay. Computer Vision, Machine Learning and AI system integration for air-seeder furrow quality quantification. ECPA 2025 Barcelona, Spain. July 1, 2025.

LSU:

1. Setiyono, T., Gentimis, T., Rontani, F., et al. 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, USA 
2. Pokharel, R., Poudel, A., Sutthanonkul, T., et al. 2025. AI-Driven rice yield prediction using UAV imagery and plant parameters across growth stages. 2025 AI in Agriculture and Natural Resources Conference. March 31 – April 2. Mississippi State University. Sackville, MS, USA. 
3. Sutthanonkul, T., Gentimis, T., Kimbeng, C., Setiyono, T. 2025. The predictive modeling of sugarcane yield utilizing Artificial Intelligence (AI) techniques while fusing phenotypic, genotypic, and Unmanned Aerial Vehicle (UAV) remote sensing data. 2025 AI in Agriculture and Natural Resources Conference. March 31 – April 2. Mississippi State University. Sackville, MS, USA. 
4. Poudel, A., Burns, D., Adhikari, R., et al. 2025. Cover crops biomass predictions with UAV remote sensing and TensorFlow machine learning. 2025 AI in Agriculture and Natural Resources Conference. March 31 – April 2. Mississippi State University. Sackville, MS, USA. 

NDSU:

1. Diwakar, P., Karg, Z., Paxton, H., et al. 2025. Multi-modal sensing for precision agriculture: LIBS, spectral imaging, and machine learning for soil and plant health monitoring. SciX Conference 2025, Covington, KY. October 5-10, 2025. (Talk) 

OKState: 

1. Coalition for Advancing Digital Research & Education June 25, 2025 — Oklahoma State University, Stillwater, OK
   Symposium Theme: Ethical Application and Regulation of Artificial Intelligence in Research and Education Presented: “Regenerative AI Bots in Teaching: Immersive Modules for Ag Econ Classrooms”

Oregon State:

1. Qi, M., Weerasekara, M., Zhang, et al., 2025. Predicting soil carbon fractions and sequestration potential using mid-infrared spectroscopy. Oregon Society of Soi Scientists Annual meeting. Poster 
2. Kalisz, A., Zhang, Y., Brungard, C., Maynard, J., Hodges, R., 2025. SpectrAnd: Spectroscopy for rapid identification of Andic soil properties. Oregon Society of Soi Scientists Annual meeting.
3. Weerasekara, M., Zhang, Y., Hartemink, A.E., Maynard, J., 2025. Soil health indicators estimated from mid-infrared (MIR) spectroscopy and machine learning. Oregon Society of Soi Scientists Annual meeting. Poster 
4. Zhang, Y., Weerasekara, M., Hartemink, A.E., Maynard, J., 2024. Soil health indicators estimated from mid-infrared (MIR) spectroscopy and machine learning. American Geophysical Union Fall Meeting. Poster 
5. Weerasekara, M., Zhang, Y., Hartemink, A.E., Maynard, J., 2024. Soil health indicators estimated from mid-infrared (MIR) spectroscopy and machine learning. ASA-CSSA-SSSA Annual Meeting. Oral 
6. Zhang, Y., 2024. Applications of big data and machine learning in soil science: what do we need from domain science in the era of artificial intelligence? Fall 2024 UK Artificial Intelligence and Machine Learning Research Symposium 

SDSU:

1. Wongpiyabovorn, O., Wang, T., Menendez, H., & Yago, A. L. (2025). Precision Livestock Farming Technologies in Beef Cattle Production: Current and Future. Choices, 40(2), 1-8.
2. Cho, W. & Wang, T. 2025. “From Farm Kids to Ag Tech Leaders: Who’s Driving Precision Agriculture?”. Forthcoming at Choices Magazine.
3. Wang, T., H. Jin., & A. Oyebanji. “Factors Affecting Farmer Adoption of Unmanned Aerial Vehicles: Current and Future.” Revised and Resubmitted to Precision Agriculture.
4. Han G., Z. Wei, T. Wang, “Adoption of Precision Agriculture Technology Bundles: Role of Values and Perceptions.” Submitted to Technology in Society. 

TAMU:

1. Wang, Y., Xu, Z., 2025. Evaluate changes in respiratory rate of lateral lying sows around onset of parturition using depth camera. Oral presentation at the USPLF Conference (Lincoln, NE) 
2. Wang, Y., Xu, Z., 2025. Investigation of poultry fecal removal efficiency and volume estimation on grooved-floor panels. Oral presentation at the Poultry Science Association (Raleigh, NC) 
3. Zhao, Z., Xu, Z., 2025. Evaluation of mild woody breast meat’s contact stiffness via beam buckling mechanism. Oral presentation at the Poultry Science Association (Raleigh, NC) 
4. Strong, R., Herbert, B., Bhandari, M.,  et al. (2025, July). ExtensionBot’s AI Impacts on Extension and Advisory Services [Refereed Oral Presentation]. 27th European Seminar on Extension & Education conference. University of Trás-os-Montes and Alto Douro (UTAD); Vila Real, Portugal. 
5. Strong, R., Athanasiadis, I., & AgML Community. (2025, July). The Emergence of AgML’s CY-Bench: An AI Platform to Enhance Engagement, Empowerment, and Partnerships [Refereed Oral Presentation]. 27th European Seminar on Extension & Education conference. University of Trás-os-Montes and Alto Douro (UTAD); Vila Real, Portugal. 
6. Strong, R., & Landaverde, R. (2025, June). Stargate and academic opportunities to elevate AI food system knowledge transfer [Refereed Oral Presentation]. Development Studies Association conference. University of Bath; Bath, United Kingdom. 
7. Marburger, M., Strong, R., Fares, A., et al. (2025, April). SDG Project Impacts from Water Instructional Interventions: National Competitive Funds Develop the Next Generation of Climate Smart Agriculture Leaders [Refereed Oral Presentation]. Association for International Agricultural Extension and Education conference. Kingsmills Hotel and Conference Center; Inverness, Scotland. 
8. Dooley, K., Strong, R., Fares, A., Moore, J., & Marburger, M. (2025, April). Harnessing AKIS to Understand Multi-disciplinary Multi-institutional Faculty’s Climate Smart Knowledge Transfer Impact [Refereed Poster Presentation]. Association for International Agricultural Extension and Education conference. Kingsmills; Inverness, Scotland. 
9. Marburger, M., Strong, R., Murray, S., Fares, A., & Porter, A. (2025, April). Are We Enhancing AI Knowledge Transfer and Fostering Collaboration?: 2024 AI in Agriculture and Natural Resources Conference Inferential Data Impacts [Refereed Oral Presentation]. 2025 AI in Agriculture and Natural Resources conference. Mississippi State University; Starkville, Mississippi. 
10. Ahn, J., Benge, M., Greenhaw, L., & Strong, R. (2025, April). AI and Social Networks in Florida’s Agricultural Extension Systems [Refereed Oral Presentation]. 2025 AI in Agriculture and Natural Resources conference. Mississippi State University; Starkville, Mississippi. 
11. Marburger, M., Kaniyamattam, K., Tedeschi, L., Strong, R., & Reedy, D. (2025, April). Student Responses and Large Language Models: Artificial Intelligence’s Attributes in Understanding Animal Nutrition and Human Health Research [Refereed Poster Presentation]. 2025 AI in Agriculture and Natural Resources conference. Mississippi State University; Starkville, Mississippi. 
12. Artificial Intelligence-Driven Stocker Economics Visualization Decision Support Tool for Sustainable Beef Production (Poster Presentation, Texas and Southwestern Cattle Raisers Association, Forth Worth)
13. Machine Learning for Economic Decision-Making in Texas Stocker Cattle Operations (Poster Presentation, Texas and Southwestern Cattle Raisers Association, Forth Worth)
14. Causal Relationships of Ecosystem Services: A Systems Approach for Diversifying Income in Cow-Calf Operations (Poster Presentation, Texas and Southwestern Cattle Raisers Association, Forth Worth)
15. Strong, R. (July, 2025). AI tools and impacts for farmers [Invited Seminar]. Northeast SARE (Sustainable Agriculture Research and Education) Virtual PDP Workshop. Cornell University; Cornell, New York. 
16. Strong, R. (July, 2025). Student knowledge and career goal impacts from participation in NIFA funded smart agriculture project [Invited Seminar]. Prairie View A&M University; Prairie View, Texas.
17. Strong, R. (July, 2025). Digitalization and artificial intelligence in education and Extension: Lessons learned and ethical considerations [Panelist]. 27th European Seminar on Extension & Education conference. University of Trás-os-Montes and Alto Douro (UTAD); Vila Real, Portugal. 
18. Strong, R., Palcynski, L. & Landaverde, R. (2025, June). Artificial intelligence opportunities for developing transformative positive change in future food systems. [Invited Leaders for Seminar Panel]. Development Studies Association conference. University of Bath; Bath United Kingdom.
19. Jurney, C., Paez, X., Haynes, M., Wall, E., & Strong, R. (2025, June). Use of AI to enhance efficiency of administration [Invited Presentation]. LEAD AgriLife. Plaza Hotel; San Antonio, Texas. 
20. Strong, R. (2025, May). AI tools and impacts for agricultural service providers [Invited Seminar]. Northeast SARE (Sustainable Agriculture Research and Education) Virtual PDP Workshop. Cornell University; Cornell, New York.
21. Strong, R., & Marburger, M. (2024, December). Social implications effecting smart agriculture knowledge transfer to stakeholders [Invited Presentation]. Smart Agriculture Workshop. Texas A&M University; College Station, Texas. 

UArk:

1. Pallerla C., Subbiah J., Bist R., et al. (2025) Enhancing foreign material detection in poultry processing plants using thermal imaging and vision-based systems. In 2025 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Toronto, Canada [Oral]. 
2. Pallerla C., …, Wang D.#, (2024) Hyperspectral imaging and Machine learning algorithms for foreign material detection on the chicken surface. In 2024 Poultry Science Annual International Meeting. Lexington, KY [Oral] 
3. Atungulu G., Tachine C., Pallerla C., Wang D. (2025) Assessment of a New Nondestructive Method to Measure Rice Chalk Content Based on Rough Rice Properties. In 2025 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Toronto, Canada [Oral] 
4. Vinson S., Wang D. (2025) Cross-Facility Reliable Deep Learning Based Beef Marbling Assessment Via Unsupervised Domain Adaptation Regression. In 2025 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Toronto, Canada [Poster, proceeding] 
5. Feng Y., Pallerla C., Lin X., et al. (2025) Leveraging Blender-Synthesized Data and Depth information for High-Precision Instance Segmentation of Chicken Carcasses. In 2025 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting. Toronto, Canada [Poster] 
6. Davar A., Xu Z., Mahmoudi S., et al. (2025) ChicGrasp: Imitation-Learning Control for Dual-Finger Manipulation of Delicate, Irregular, and Bio-products. In International Conference on Robotics and Automation (ICRA) 2025[Poster] 
7. (1B)Mahmoudi S., Wang D.#, (2025) Data-Driven Contact-Aware Control Method for Real-Time Deformable Tool Manipulation: A Case Study in the Environmental Swabbing   In International Conference on Robotics and Automation (ICRA) 2025 [Poster] 
8. Wang, D., Mahmoudi S., Griscorn C., Crandall P (2024). Automated Environmental Swabbing: A Robotic Solution for Enhancing Food Safety in Poultry Processing — Human Swabbing Evaluation and Preliminary Robotic Swabbing Setup. In 2024 Institute of Biological Engineering (IBE) Annual Meeting, Atlanta, GA [Oral]
9. Azmir, M. N., Tagoe, A., Koparan, C., et al. (2025). Automated Weed Pressure Measurement System Evaluation for Unmanned Aerial Vehicles. In 2025 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers. [Poster, proceeding] 
10. Koparan C., Johnson D., Wang D., Worthington M., Poncet A., (2025) Preliminary Analysis of Computer Vision for Blackberry Flower Quantification. In 2025 American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting.Toronto, Canada [Poster] 

UCDavis:

1. Li, Z., & Ahamed, M. S. Advancing Lettuce Growth Modeling in Controlled Environments with Physics-Informed Neural Networks. ASABE Annual Meeting 2025, Toronto, Canada. 
2. Karimzadeh, S. & Ahamed, M. S. GrowDose: A Novel Software for Precision Ion-Based Nutrient Management in Closed-Loop Hydroponics. ASABE Annual Meeting 2025, Toronto, Canada.
3. Karimzadeh & Ahamed, M. S. Advanced Model Predictive Control for Optimized Nutrient Management in Closed-Loop Hydroponics. ASABE Annual Meeting 2025, Toronto, Canada.

MSU:

1. Xu, J., Lu, Y., 2025. Development and evaluation of a multispectral vision-based automated sweet potato sorting system. Sensing for Agriculture and Food Quality and Safety XVII.
2. Deng, B., Lu, Y., 2025. Weed image augmentation by IP-adapter-based stable diffusion for multiclass weed detection. Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications III; 1345909.
3. Deng, B., Lu, Y., Brainard, Improvements and Evaluation of a Smart Sprayer Prototype for Weed Control in Vegetable Crops. 2025 ASABE Annual International Meeting 2500323
4. Deng, B., Lu, Y., Brainard, D., 2025. Semi-Supervised Weed Detection in Vegetable Fields: In-domain and Cross-domain Experiments. https://doi.org/10.48550/arXiv.2502.17673
5. Singh, N., Lu, Y., 2025. Development and Laboratory Assessment of Cutting and Snapping Mechanisms for Green Asparagus Harvesting. 2025 ASABE Annual International Meeting 2500323.
6. X. Yang, A. Bhujel, M. Bashar, M. Benjamin, D. Morris, 2025. Enhanced Piglets Monitoring with a Multiview Camera System” in 2025 ASABE Annual International Meeting.
7. B. Smith, Y. Long, D. Morris, 2025. An Automated LED Intervention System for Poultry Piling, 2025 ASABE Annual International Meeting.
8. A. Bhujel, D. Morris, J. Siegford, M. Benjamin, M. Bashar ‘A Computer Vision Dataset for Monitoring and Tracking Gilt’s Daily Activities” in 3^rd US Precision Livestock Farming Conference, 2025.

UF:

1. Zhou L., Amini M., Reisi Gahrooei M., Ampatzidis Y., 2025. Integrating federated learning and hyperspectral imaging for early detection of tomato disease. MIT URTC (Undergraduate Research and Technology Conference), Cambridge, Massachusetts, October 10-12, 2025.
2. Ampatzidis Y., 2025. Harvesting Innovation Together: Cross-Sector Synergies in Agricultural Robotics. Industry-Academia Panel. 8th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL 2025), Davis, CA, USA, August 27-29, 2025.
3. 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. 8th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL 2025), Davis, CA, USA, August 27-29, 2025.
4. Frederick Q., Burks T., Watson J., et al., 2025. Investigating feature types for automated citrus peel disease detection. 8th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL 2025), Davis, CA, USA, August 27-29, 2025.
5. Liu W., Ampatzidis Y., Wilkinson B., 2025. The accuracy of remote sensing technologies for tree height estimation: a comparative evaluation in citrus orchards. ASABE Annual International Meeting, Toronto, Ontario, Canada, July 13-16, 2025, 2500561, doi:10.13031/aim.202500561.
6. Liu W. and Ampatzidis Y., 2025. Optimizing citrus tree detection: A novel improved distance-based individual tree segmentation method for aerial LiDAR data. ASABE Annual International Meeting, Toronto, Ontario, Canada, July 13-16, 2025.
7. Cho Y., Yu Z., and Ampatzidis Y., 2025. Enhancing trust in agriculture: Addressing data sharing with blockchain technology. ASABE Annual International Meeting, Technical Session 110 – Connectivity, Cloud Computing, and Internet of Things in Agriculture and Natural Resources, Toronto, Ontario, Canada, July 13-16, 2025.
8. Vijayakumar V., Neto A.D.C., Ampatzidis Y., 2025. AI-driven autonomous spraying for precision weed management in specialty crop production. 15th European Conference on Precision Agriculture, Barcelona, Spain, June 29 – July 3, 2025.
9. Liu W. and Ampatzidis Y., 2025. Enhancing canopy height measurement accuracy and efficiency in citrus orchards with LiDAR. Florida State Horticultural Society (FSHS) Annual Conference, Bonita Springs, Florida, June 8-10, 2025.
10. Pullock D.A., Zhou C., Ampatzidis Y., et al., 2025. Potential for automation of citrus psyllid pest identification using computer vision-based artificial intelligence recognition. 2nd International Electronic Conference on Entomology, Online, May 19-21, 2025.
11. Ampatzidis Y., Liu S., Guan H., Neto A.D.C., 2025. Enhanced AI-driven sensing and analytics platform for precision orchard management. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X Conference, SPIE Defense + Commercial Sensing, Orlando, FL, April 14-16, 2025.
12. Guan H., Neto A.D.C., Liu S., Ampatzidis Y., 2025. AI-driven precision spraying with canopy-specific parameter optimization for enhanced orchard efficiency. AI in Agriculture Conference: The Role of AI in Autonomous Agricultural Systems and Socioeconomic Effects. Starkville, MS, March 31 to April 2, 2025.
13. Huang, Z., and W. S. Lee. 2025. A state space model with tree topology for strawberry detection. AI in Agriculture Conference: The Role of AI in Autonomous Agricultural Systems and Socioeconomic Effects. Starkville, MS, March 31 to April 2, 2025.
14. Liu S., Ampatzidis Y., Lee W.S., Zhou C., 2025. Optimizing strawberry harvest planning through machine vision and AI-enabled predictive analytics. AI in Agriculture Conference: The Role of AI in Autonomous Agricultural Systems and Socioeconomic Effects. Starkville, MS, March 31 to April 2, 2025.
15. Huang, Z., W.S. Lee, and M. Le. 2025. AI-driven plant tracking and segmentation for precise canopy estimation in strawberry field. ASABE Paper No. 202500347. St. Joseph, MI.: ASABE.
16. Daryani, A. E., Bhutta, M., Hernandez, B., & Medeiros, H. (2025). CaMuViD: Calibration-Free Multi-View Detection. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 1220-1229).
17. Khademi, Z. and Medeiros, H. (2025). End-to-end multi-object tracking and segmentation for precision agriculture. In American Society of Agricultural and Biological Engineers Annual International Meeting.
18. Daryani, A. E., and Medeiros, H. (2025). AgriTrack: A Robust Framework for Temporal Tracking of Lettuce Plants. In American Society of Agricultural and Biological Engineers Annual International Meeting.
19. Wang, R., Hofstetter, D., Medeiros, H., and Boney, J., 2025. YOLO + Focal Loss to Improve Detection of Turkey Behaviors. Agricultural and Biological Engineering (ABE) Poster Symposium, University of Florida, Gainesville, FL, March 26, 2025.
20. Khademi, Z. and Medeiros, H. (2025). Multi-Object Tracking and Segmentation for Precision Agriculture. American Society of Agricultural and Biological Engineers Florida Section Meeting.
21. H. Medeiros (2025), Robotic Perception for Agricultural Applications​: How advanced models are enabling novel robotic platforms for agricultural production, EPSRC Centre for Doctoral Training in Agri-Food Robotics Online Conference (invited talk).
22. H. Medeiros (2025). Artificial Intelligence and Robotics in Agriculture​: How AI & robotics are impacting agriculture and food production​. Strategic Dialogues for the Future: Advancing Agricultural Science to Deliver Societal Value in LAC and the U.S. FONTAGRO Workshop (invited talk).

UKY:

1. Rodríguez López C.M. Development of an Epigenetic Clock to Predict Gestational Age in pregnant mares. Commonwealth Computational Summit 2024, Lexington, KY, 10/16/2024
2. Rodríguez López C.M. Panelist: AI in Agriculture: Opportunities, Challenges, and Recommendations. 4^thannual AI in Agriculture conference Starkville, MS. April 1-3, 2025.
3. Chen, S., Rodríguez López C.M. MacLeod, J. White Blood Cell Population Flux during Gestation in Pregnant Mares Annual Kentucky American Water Science Fair (1^st place animal science category) – Central Kentucky Regional Science and Engineering Fair (3^rd place animal science category).
4. Khalsa, S.J.; Mizuta, K.; Nagel, P. (2025) Developing a Standard for Validation of Innovative Methods in Agricultural Soil Testing. European Geosciences Union. Vienna, Austria
5. Mizuta K.  (2024) From Soil to Success: Leveraging Proximal and Remote Sensing Technologies with AI for Precision Tobacco Agriculture, KY Tobacco Research and Development Center. Lexington, KY.
6. Mizuta K.  (2024) Collaboration Opportunities: Precision Agriculture, Pedometrics, AI, and Soil Health, The UK Research and Education Center in Princeton. Princeton, KY.
7. Mizuta K., Miao Y, Lu J, and Negrini R.  (2024) Evaluating Different Strategies to Analyze On-farm Precision Nitrogen Trial Data. 16th International Conference on Precision Agriculture, Manhattan, KS.
8. Mizuta K. (2024) Challenges and Opportunities in Sensor-Based Site-Specific Soil Survey Research and Management. ASA, CSSA, SSSA International Annual Meeting, San Antonio, TX.
9. Mizuta K. (2024) Toward Efficient, Profitable, and Sustainable Food Production Systems and Better Environmental Outcomes Through Data-Driven Computational Approaches. University of Kentucky Department of Biosystems and Agricultural Engineering.
10. Mizuta K. (2024) Perspectives on Soil Health Research in the U.S.: Definitions, Key Players, Grants, Quantitative Measurements, and Other Institutional Activities. The Science Council of Japan.
11. Mizuta K., Nagel, P., Zamudio, W., Paliotti, M., and Clingensmith, C. (2024) Comparing the Prediction Accuracies of Machine Learning and Deep Learning-Based Models for Extractable Phosphorus in Soil for Precision Agriculture Purposes. ASA, CSSA, SSSA International Annual Meeting, San Antonio, TX.
12. Mizuta K., Zamudio, W., and Nagel, P. (2024) In-Situ Soil Spectroscopy Application for Extractable Phosphorus Prediction for Precision Agriculture with Machine and Deep Learning Models. Pedometrics International Conference. Las Cruces, NM.
13. Mizuta K., Miao Y, Lu J, and Negrini R.  (2024) Evaluating Different Strategies to Analyze On-farm Precision Nitrogen Trial Data. 16th International Conference on Precision Agriculture, Manhattan, KS.
14. Mizuta K., Miao Y, Lu J, and Negrini R.  (2024) Evaluating Different Strategies to Analyze On-farm Trial Data: A Case Study for Nitrogen Trials. International Conference for On-Farm Precision Experimentation. South Padre Island, TX.
15. Miao Y, Kechchour A, Sharma V, Flores A, Lacerda L, Mizuta K, Lu J, and Huang Y.  (2024) In-season Diagnosis of Corn Nitrogen and Water Status Using UAV Multispectral and Thermal Remote Sensing. 16th International Conference on Precision Agriculture, Manhattan, KS.
16. Kechchour A, Miao Y, Folle S, and Mizuta K. (2024) On-farm Evaluation of The Potential Benefits of Variable Rate Seeding for Corn in Minnesota. 16th International Conference on Precision Agriculture, Manhattan, KS.
17. Morales-Ona A, Quinn D, Mizuta K, Miao Y. (2024) Effects of Crop Rotation on Estimation of In-Season SidedressNitrogen Rates for Corn Based on Satellite Imagery. 16th International Conference on Precision Agriculture, Manhattan, KS.
18. Morales-Ona A, Quinn D, Mizuta K, Miao Y. (2024)  Precision Nitrogen Management. Lessons learned from our On-farm PNM project (2021-2023). 75th Annual Corn Improvement Conference, West Lafayette, IN.
19. Lu J, Miao Y, Mizuta K, et al. (2024) On-farm Evaluation of a Remote Sensing-based Precision Nitrogen Management Strategy Across Diverse Corn Trials. 16th International Conference on Precision Agriculture, Manhattan, KS.
20. Negrini R, Miao Y, Mizuta K, et al. (2024) Within-Field Spatial Variability in Optimal Sulfur Rates For Corn (Zea mays L.) in Minnesota: Implications for Precision Sulfur Management. 16th International Conference on Precision Agriculture, Manhattan, KS.
21. Negrini R, Miao Y, Mizuta K (2024) Optimizing Sulfur Management in Corn through On-Farm Experimentation and Machine Learning in Minnesota: A Study on Within-Field Variability and Limiting Factors. ASABE Regional Meeting, Brookings, SD.
22. Oloyede, A. and Adedeji, A.A. (2025). Deep learning-based hyperspectral model reconstruction from RGB data for gluten detection and quantification in foods. A paper presented (oral) during the Annual International Meeting of American Society of Agricultural and Biological (ASABE) held at the Sheraton Centre Hotel, Toronto, Canada from July 13 – 16, 2025. Paper #: 2500068.
23. Oloyede, A. and Adedeji, A.A. (2025). Development of a multispectral real-time system for gluten detection and quantification in gluten-free products. A paper presented (poster) during the Annual International Meeting of American Society of Agricultural and Biological (ASABE) held at the Sheraton Centre Hotel, Toronto, Canada from July 13 – 16, 2025.  Paper #: 2500067
24. Khalsa, S.J.; Mizuta, K.; Nagel, P. (2025) Developing a Standard for Validation of Innovative Methods in Agricultural Soil Testing. European Geosciences Union. Vienna, Austria
25. Nagel, P, Mizuta, K., and Khalsa, S.J. (2024) Towards a Standardized Protocol and Policy for Acceptance of Innovative Soil Testing. 2024 IEEE India Geoscience and Remote Sensing Symposium. Goa, India, Dec 2-5, 2024.

EXTENSION TRAINING AND CONFERENCES FACILITATED

1. KSU: Sharda, Ajay.  Co-Organized the AI in Kansas Ag Conference in Lyndon, KS July 22, 2025.  This conference was organized through the ID3A group and Kansas State Research and Extension.  The conference was part of a series of AI in Ag conferences presented this year.