Duration: 10/01/2026 to 09/30/2031

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

U.S. agriculture is facing intensifying challenges, including rising production costs, labor shortages, pest and disease pressures, and environmental degradation, etc. Artificial intelligence (AI) promises to help address these challenges by transforming large volumes of agricultural data into practical decision-support and automation tools. However, many existing AI technologies are not designed specifically for agricultural systems, and coordination across institutions to develop dedicated AI methods, datasets, and training programs for agroecosystems remains limited. The goal of this multistate project is to develop and promote AI-based technologies through collaborative efforts among institutions to improve crop, animal, and food production, phenotyping and genotyping, and natural resource management, while supporting workforce training and technology adoption. The project, therefore, will focus on three major objectives: (1) developing AI-based approaches for agroecosystems production, processing, & monitoring; (2) data curation, management, accessibility, security, and ethics; and (3) AI adoption (technology transfer) and workforce development. This project will benefit farmers, agricultural industries, researchers, extension professionals, and students. Through collaboration among universities across multiple states, researchers will develop and evaluate AI technologies across diverse agricultural systems and environmental conditions. Outreach activities, including the AI in Agriculture Conference, training workshops, and educational programs, will help industry stakeholders and agricultural professionals better understand and apply AI technologies. Ultimately, this project aims to improve farm productivity and natural resource management, and strengthen the workforce to support the future of smart agricultural systems.

Statement of Issues and Justification

The need as indicated by stakeholders

The competitive nature of modern agriculture demands that agribusiness firms innovate and adapt quickly to capture benefits from advances in new technology such as artificial intelligence (AI) (Ali et al., 2025). Throughout the value chain, there is a critical need to increase efficiency and protect the bottom line. Although currently lagging behind other industries, agriculture is forecasted to experience a “digital revolution” over the next decade.  The market value of AI in agriculture is expected to grow at a compound annual growth rate of 22% per year, reaching $8 billion by 2030, according to a recent report by Insight Partners. Growers, for example, are constantly exploring the best opportunities to increase yield and profit as expressed by Iowa corn and soybean producers at the recent NSF Convergence Accelerator Workshop for Digital and Precision Agriculture. Participants strongly voiced the need for new technology to answer “what is my best opportunity?” to enhance crop productivity and strengthen their bottom line.

Based on the National Science & Technology Council (2019) report, the American Artificial Intelligence Initiative was established in 2019 to maintain American leadership in AI and ensure AI benefits to the American people. The initiative sets up long-term investments in AI research as one of its priorities. Following the initiative, USDA/NIFA invests in many AI-related programs such as the Data Science for Food and Agricultural Systems (DSFAS) and other programs for crop and soil monitoring systems, autonomous robots, computer vision algorithms, and intelligent decision support systems.

AI allows computers and machines to carry out tasks without human cognition. AI includes machine learning (ML) and deep learning (DL). Machine learning is a data analysis method to imitate human learning. Examples of using ML/DL in agriculture include crop production (e.g., yield prediction, pest and disease detection, harvesting, and phenotyping) (Logeshwaran et al., 2024), livestock farming (e.g., behavior and health monitoring) (Oliveira et al., 2021), postharvest processing (e.g., quality inspection, grading, and sorting) (Zhou et al., 2019), and agricultural robotics (e.g., navigation, self-learning, human-machine synergy, and optimization) (Prajapati et al., 2023). DL is a subset of ML and consists of deep neural networks to mimic or sometimes surpass human brain functions. Recently, DL has driven huge improvements in various computer vision problems, such as object detection, motion tracking, action recognition, pose estimation, and semantic segmentation. The Internet of Things (IoT) is a technology that enables data acquisition and exchange using sensors and devices through a network connection. IoT tends to generate big data, which requires AI to make valuable inferences (Xu et al., 2022). 

Crop growth is a complex and risky process, and difficult for producers to analyze in isolation. The emergence of information technology has developed a large amount of data, i.e., big data, that can be analyzed utilizing AI to provide valuable decision support to producers, particularly large-scale operations. AI can provide predictive analytics for growers to better manage risks through improved preparation and response for unexpected events such as severe flooding and drought (Wang et al., 2022). For instance, the Colorado-based company, aWhere, uses machine learning algorithms in connection with satellites and 1.9 million weather stations (virtual) to predict daily weather patterns for clientele, including farmers, crop consultants, and researchers. Improved crop and irrigation planning assists a global network of growers in reducing water usage with a particular focus on the impacts of environmental changes.

Robotics and visual machine learning platforms enable automation of critical labor activities such as field scouting (Mitrofanova et al., 2023), weeding (McAllister et al., 2021), and harvesting (Mail et al., 2023). Pest control companies have begun using aerial drone technology to cut costs in the labor-intensive practice of scouting pests and diseases (Subramanian et al., 2021). According to Brian Lunsford of the Georgia-based Inspect-All company, “In 2016, we performed our first paid drone inspection after months of testing. Most importantly, we wanted to make sure our flight operators could safely fly our drones and provide our customers with substantial value, while at the same time being mindful of privacy concerns.”  As reported by Protein Industries Canada, AI-assisted pest and disease monitoring systems can save pesticide use up to 95% and reduce costs by $52 per acre. Ground robotic weeding systems powered by AI and big datasets have started to be commercially adopted for precision farming, which can recognize weeds and kill them site-specifically, substantially reducing the need for both manual labor and herbicides.

Emerging AI technologies are expected to reach a broader spectrum of the value chain. While prior technologies focused primarily on field crop commodities, AI algorithms will enable specialty crop industry stakeholders to optimize production (Kakaria et al., 2022), improve harvest efficiency, and cut labor costs by using AI-enabled automation or robotics systems. Early adopters such as John White of Marom Orchards support the use of AI in response to the need “to pay wages, organize visas, housing, food, healthcare, and transportation” for a large number of workers and to address critical labor shortages due to “hard, seasonal work and other crops can pay higher wages. Young people all over the world are abandoning agricultural work in favor of higher-paying, full-time urban jobs.” Small, labor-intensive farm operations can thus expand production opportunities by reducing harvest losses by 10% while reallocating freed-up labor to alternative enterprises, alleviating concerns over expected labor shortages that are expected to reach $5 million by 2050, according to Marom Orchards.

Downstream on the value chain, AI is also expected to have a strong demand over the coming decade. A recent article in Food Online lists three new types of AI technology to improve supply chain management (Manning et al., 2022), including: (1) food safety monitoring and testing of product along the supply chain; (2) improved marketing analysis of price and inventory; and (3) a comprehensive “farm to fork” tracking of product. In the food processing industry, AI algorithms are being used at modern packing facilities for food inspection, grading, and sorting. For instance, the Spectrim (an optical sorter) from TOMRA Food has leveraged deep learning for enhanced grading precision of fruits. AI reduces labor time compared to manual sorting, enhances the quality and consistency of products delivered to consumers, and reduces postharvest losses. To fine-tune product development and optimally satisfy consumer preferences, startup companies such as Gastrograph AI use machine learning to assist their clientele in fine-tuning product development. AI is expected to be in high demand to improve hygiene in both manufacturing plants and restaurants. The use of AI in the cleaning of manufacturing equipment is projected by the University of Nottingham researchers to reduce cleaning costs by up to 40% (Porcheron et al., 2020).

 

The importance of the work, and what the consequences are if it is not done

There are pressing challenges within the country’s agroeconomic systems. These challenges directly impact growers, rural communities and consumers, and include:

• Growing enough agrifood to meet growing population demands
• Rising costs
• Invasive pests and weeds
• Plant and animal diseases
• Excessive livestock mortality
• Land and water supply degradation
• Changing environmental conditions that harm production
• Shortage of labor
• Reduce waste and ensure safe food processing
• How to increase production while reducing land use

AI has potential to address and significantly ameliorate many of these agroeconomic challenges. 

• Improve efficiencies and reduce pollution through targeted interventions
• AI tools can analyze large, diverse datasets (remote sensing, IoT, genomics, weather, management) to guide better decision-making, increase yield, and reduce input costs.
• Precision AI technologies can reduce water, fertilizer, and pesticide use, cutting waste and environmental pollution.
• Predictive analytics can detect crop stress, pests, and diseases earlier, improving resilience to shocks such as droughts, floods, or emerging pathogens.
• Robotics and computer vision can support harvesting, scouting, grading, and sorting, which are critical in the face of farm labor shortages.
• AI tools can non-invasively monitor livestock behavior and health indicators, improving welfare while reducing unnecessary treatments and antibiotic use.
• The project will train a new generation of scientists, extension agents, and producers in AI-driven agriculture, ensuring adoption and long-term capacity.
• AI tools can nondestructively detect food fraud, defects in produce, classify food, detect and quantify contaminants such as allergens in food.

 The AI work proposed here will:

• Deliver field-ready solutions that improve profitability, resilience, and sustainability across U.S. agriculture.
• Enable farmers to manage risks and adapt to extreme weather events more effectively.
• Build a strong foundation for U.S. leadership in agricultural AI, supporting competitiveness across global markets.
• Advance environmental stewardship by reducing agriculture’s footprint on soil, water, and the atmosphere.

 If this work is not done:

• There will be a significant opportunity cost.
• Farmers do not have the resources to perform or fund this work.
• Other countries that develop AI solutions for their farmers will gain a competitive advantage.
• Inefficient use of resources will persist, continuing to drive environmental degradation and loss of applied nutrients to water and air.
• Labor shortages and farm-level vulnerabilities will deepen, threatening rural community viability and national food security.
• Shortage of food, less safe foods, more wastes are produced from agrifood production processes, and supply chain challenges.

The importance of this project lies in that it will help tackle multiple pressing challenges that we are currently facing in the agroecosystem. The current AI technologies are not explicitly tuned for agroecosystem, which causes problems such as low accuracy in prediction, inefficiency in using computer resources, inefficiency in data management, and not being cost-effective for most agricultural crop productions. Also, the lack of next-generation farmers and workers in this area will be a major bottleneck for adopting and applying AI technologies. Included in this project, multiple AI-centered projects will be conducted at multiple states in the southeast U.S. to develop AI tools suitable for specific applications important to the improvement of production and sustainability of the agroecosystem. The project will also assess the feasibility of different AI technologies and showcase the value of those technologies to stakeholders to improve AI adoption. Additionally, this work will help develop the workforce for the future agricultural production system. Tasks in this project should be completed quickly and efficiently to ensure that production in agriculture meets the global needs in the near future and ensures the sustainability of the agroecosystem. It is also essential that technologies in agriculture must keep up with technologies in other fields to attract more talented people to ensure workforce sustainability. 

   

The technical feasibility of the research

The proposed research is technically feasible, benefiting from recent advances in AI, remote sensing, high-throughput sensing technologies, digital twins, edge computing, and integrated data platforms. The team has access to robust computational resources, including cloud-based platforms and local high-performance computing clusters, that are capable of handling large-scale, heterogeneous datasets typical in agricultural systems. These resources support efficient model training, geospatial analysis, deployment of decision support tools, digital twin simulations, and near-real-time decision-making.

ML algorithms, including ensemble methods, deep neural networks, and explainable AI techniques, are now mature enough to be applied across multiple data streams (e.g., optical remote sensing imagery, IoT sensor data, drone/satellite spatial data, and climate models) to provide real-time information and sampling efficiency. Open-source deep learning libraries such as TensorFlow, PyTorch, XGBoost, and Earth Engine facilitate scalable development and deployment of predictive models. Additionally, software for precision agriculture (Agisoft, ArcGIS Pro QGIS, OpenDroneMap) and remote sensors allow for real-time integration of spatial and temporal data for efficient, local, and large-scale crop monitoring.

The research team is composed of multidisciplinary experts in agronomy, soil science, entomology, plant pathology, statistics, and computer science, and is already working with regional and national stakeholders. Existing data pipelines from previous and ongoing projects can be readily extended, reducing time-to-deployment for new analytical models. Furthermore, the availability of low-cost IoT sensors, drones, and open-access satellite data ensures that experimental designs can be implemented at both plot and landscape scales.

Taken together, the combination of mature technologies, available infrastructure, and interdisciplinary expertise ensures the technical feasibility of the project.

 

The advantages for doing the work as a multistate effort

Each state has different experts in different research areas of AI and applications. Working as a multistate team creates more opportunities for collaborating in various disciplines from other states. From the survey completed in May 2021 at SAASED Institutions (SAASED, 2021), only 46% of the respondents have developed a partnership with other institutions. The survey also found that it is less likely that researchers know what other institutions are doing, and there is not much coordination among them. Therefore, this multistate project will facilitate more productive collaboration through organized coordination to complete the tasks proposed in this project.  

At the initial proposal development stage in, we had meetings every one or two weeks, and there were 15-20 participants for most of the meetings. As a group, we came up with the title, defined objectives, and established the writing team for each specific objective. For each objective, a leader was chosen to lead the writing activities. All members were committed to accomplishing their assigned tasks of writing an introduction, literature review, and detailed activities, along with outputs, outcomes, and milestones, which resulted in this proposal.

Since the creation of the project, we have seen a steady growth of this multistate group with new members participating each year. This group has been meeting twice a year, one at the AI in Agriculture Conference, created and sponsored by this group, and the other meeting specifically for this group. At these meetings, participants from different institutions discuss their research activities, major findings, current issues, funding opportunities, potential collaborations, and future directions. A listserv of emails was created to facilitate efficient communication among the participants. An online cloud folder was created to share and maintain data and information. The group has fostered extensive collaborations across institutions and impactful achievements in advancing AI in Agroecosystems.  

 

What the likely impacts will be from successfully completing the work 

This multistate project could significantly improve farming practices and productivity. With AI tools, growers would be able to identify the best opportunities to increase yield and profitability, using predictive analytics and real-time data. Technologies such as machine learning and deep learning would help detect crop diseases, stress, and pests earlier, allowing for faster and more targeted responses. For large-scale operations, AI would simplify the complexity of managing crops by turning big data into actionable insights. 

Automation would also play a major role in addressing grand labor challenges. Robotics and visual machine learning platforms could take over labor-intensive tasks such as scouting and harvesting, which are especially important given the growing shortage of agricultural workers. Small/medium-sized farms could also benefit by reducing harvest losses and reallocating labor to other areas. They will be able to expand production without increasing workforce demands.

Beyond farming, AI would strengthen the entire agricultural value chain. It could improve postharvest handling, food quality and safety, reduce waste, and support product development based on consumer preferences.

Environmental sustainability would improve as well. AI-driven precision agriculture could reduce the use of water, fertilizers, and pesticides, helping farms lower their environmental impact while maintaining productivity. These technologies would support better planning for extreme weather events such as droughts and floods, making farms more resilient to climate change.

On a national level, the project would support goals set by America’s AI Action Plan, published in July 2025, including (1) Build World-Class Scientific Datasets, (2) Build an AI Evaluations Ecosystem, and (3) Enable AI Adoption. This project would contribute to achieving these goals by developing AI tailored to agriculture, helping the U.S. maintain leadership in this space. By addressing current limitations in cost, accuracy, and data management, the project would help make AI tools more accessible and effective for producers.

Finally, the project would help build a skilled workforce. By integrating AI and robotics into education and extension programs, it would prepare the next generation of agricultural professionals. Multistate collaboration would also foster innovation across institutions, leading to joint publications, shared datasets, and coordinated outreach efforts.

 

Related, Current and Previous Work

AI, including machine learning (ML) and deep learning (DL), has emerged as a powerful approach for handling large, heterogeneous sensor datasets in agriculture. Among DL approaches, convolutional neural networks (CNNs), deep neural networks (DNNs) (e.g., long short-term memory, LSTM), vision transformers, vision foundational models, and recurrent neural networks (RNNs) have received great attention in agriculture.

AI for Crop, Animal, and Food Production

Sustainable crop production faces persistent challenges in managing biotic stressors, such as weeds, insects, and diseases, as well as abiotic stresses arising from variable environmental conditions. Sensing technologies, particularly imaging-based systems, offer promising tools for monitoring crop health, pest pressure, and field variability. AI-based image analysis and modeling have demonstrated strong performance in key vision tasks, including crop stress detection (Cho et al., 2024), disease identification (Wang et al., 2019), weed detection (Dang et al., 2023), and pest monitoring (Rustia et al., 2020; Grijalva et al., 2025), enabling more precise and timely management interventions.

Accurate in-season yield prediction is essential for optimizing input management, harvest planning, crop insurance, and marketing decisions. Crop yield variability results from complex interactions among management practices, climate, water availability, soil properties, genetics, pests, diseases, and weed pressure, making reliable prediction under real-world field conditions inherently challenging. AI-based yield prediction has been successfully demonstrated across major agronomic crops, including wheat (Barbedo 2025), sorghum (Kutyauripo et al., 2024), soybean (Joshi et al., 2024), corn (Kim et al., 2020), and rice (Liu et al., 2021).

Specialty crops present additional complexities due to variable geometry, asynchronous maturity, and multiple harvest cycles. Nevertheless, AI-driven image analysis and neural networks have enabled early yield prediction for fruits and vegetables such as apples (Zheng et al., 2025), blueberries (Niedbala et al., 2022), strawberries (Liu et al., 2025), peppers (Gholipoor et al., 2019), tomatoes (Odah et al., 2025), apricots (Blagojević et al., 2016), and eggplant (Thingujam et al., 2020), offering substantial improvements in accuracy and efficiency over traditional methods.

AI and machine learning also play an important role in plant pathology and entomology by enabling automated detection and management of plant pathogens, weeds, and insect pests. Traditionally, disease and pest identification has relied on visual inspection and manual sampling, which are often subjective, time-consuming, and require specialized training (Grijalva et al., 2023; Høye et al., 2021). AI-based image analysis provides scalable alternatives for rapid, objective, and high-throughput monitoring in agricultural systems.    

Advances in ML have also enabled individual-level animal monitoring for health and welfare assessment. Unsupervised techniques are commonly used for animal segmentation, while supervised learning models support posture, gait, and behavior recognition (Bist et al., 2026). Applications include lameness detection in dairy cattle (Siachos et al., 2024), thermal comfort classification in pigs, aggression monitoring, and estrus identification (Reza et al., 2024). However, most existing studies focus on large animals in confined environments; research on poultry remains limited and is largely conducted under laboratory conditions, although promising results have been reported for broiler health assessment, gait scoring, and behavior monitoring using computer vision and ML.

Beyond production systems, AI-driven sensing technologies also provide nondestructive alternatives to conventional postharvest food quality evaluation methods, including automated grading of fresh produce (Xu et al., 2024). These approaches enable rapid assessment of physicochemical properties, defects, contamination, and pest infestation in agricultural products. When integrated with blockchain technologies and predictive analytics, AI-based systems have the potential to enhance transparency, efficiency, and waste reduction across food supply chains.

AI for Improved Robotic Systems

AI plays a central role in agricultural robotics, particularly in scene interpretation, object detection, navigation, vision-based control, and fleet management. Deep learning has significantly improved fruit detection and localization, which are foundational for robotic harvesting and automated yield estimation. Architectures, such as You Only Look Once (YOLO) series, real-time detection transformers, and the Segment Anything Model (SAM), have been widely adopted, though data annotation requirements remain a bottleneck (Carraro et al., 2023).

Navigation in orchards poses additional challenges compared to open-field crops. Machine vision, light detection and ranging (LiDAR), GPS-based sensor fusion, and multi-sensor approaches have all demonstrated success in row-following and path planning (Ali et al, 2019). Vision-based control systems, particularly closed-loop visual servoing, enable precise fruit manipulation, though controller stability and robustness remain active research areas (Mahmoudi et al., 2024). Beyond individual robots, AI also supports fleet-level optimization, diagnostics, and task planning, offering pathways to improve system reliability and economic viability.

AI for Natural Resources Scouting and Monitoring

Machine learning has accelerated the analysis of soil and environmental data, supporting applications in soil carbon mapping, soil health assessment, and nutrient modeling (Pardarian et al., 2020). Deep learning has also shown strong performance in water quality monitoring and harmful algal bloom (HAB) detection using remote sensing data, particularly when multimodal and spatiotemporal datasets are integrated through CNN–LSTM architectures (Hill et al., 2020).

AI for Plant Phenotyping and Genotyping

Deep CNNs have revolutionized image-based plant phenotyping (Xiong et al., 2021), enabling classification, regression, segmentation, and object detection tasks that were previously difficult using traditional methods. Applications span species identification, disease detection, organ counting, biomass estimation, lodging assessment, and 3D reconstruction using color, spectral, LiDAR, and other sensing modalities. Despite progress, the limited availability of open-source datasets remains a key barrier to rapid algorithm development and benchmarking.

Cross-Cutting Needs: Standardization, Economics, and Education

While many AI technologies demonstrate strong technical performance, fewer studies address their economic, environmental, and social impacts. Economic surplus models, risk analysis, and biophysical simulation tools provide frameworks for evaluating adoption outcomes but are underutilized in AI-focused research. Additionally, the “black-box” nature of AI remains a barrier to adoption, underscoring the need for education, explainability, standardized testbeds, and interdisciplinary training.

AI in Agriculture Conference

Over the past five years, participating stations in the group—including Alabama, Florida, Texas, Mississippi, and North Carolina—have collaborated to organize a national AI in Agriculture Conference that brings together participants from academia, industry, and government agencies. The conference provides a platform for sharing current research, technologies, and best practices in the application of AI to agricultural production, natural resource management, and food processing. Attendance has increased steadily each year, with a minimum participation of approximately 300 attendees. In 2026, the conference is expected to host representatives from more than 60 universities as well as over 40 private and public organizations, including government professionals. The conference serves as a strong example of outreach and knowledge dissemination efforts, which constitute an essential component of Objective 3.

Objectives

1. Develop AI-based approaches for agroecosystems production, processing, & monitoring
   Comments: Sub-objectives: a. AI tools for food, crop, and animal production and processing; b. AI tools for autonomous system perception, localization, manipulation, and planning for agroecosystems; c. Natural resources scouting and monitoring; d. Phenotyping and genotyping;
2. Data curation, management, accessibility, security, and ethics
   Comments: Sub-objectives: a. Create open source agricultural datasets following FAIR (Findable, Accessible, Interoperable, Reusable) principles; b. Data Standardization and testbed development; c. Agricultural data governance, ethics, and privacy
3. AI adoption (technology transfer) and workforce development
   

Methods

Objective 1: Develop AI-based approaches for agroecosystems production, processing, & monitoring

Obj. 1a. AI tools for food, crop, and animal production and processing

A. Introduction

Recent advances in AI, coupled with increased computational power, have enabled the efficient analysis of large and diverse agricultural datasets, creating new opportunities for real-time and data-driven decision-making in crop and livestock production. AI technologies can improve productivity, sustainability, and resource-use efficiency by supporting crop growth monitoring, nutrient and water stress detection, disease and pest identification, weed management, precision spraying, postharvest quality evaluation, and livestock health, welfare, behavior, and production monitoring.

Despite these advances, AI models often struggle to generalize across the highly variable production environments encountered in agriculture. Crop performance is influenced by field-specific factors such as soil properties, topography, weather conditions, and management practices, limiting the transferability of models developed for individual locations. Similarly, livestock production systems differ considerably in housing design, stocking density, environmental conditions, and management practices, reducing the robustness of behavior recognition, welfare assessment, and decision-support models across facilities.

Developing AI models with improved generalization requires integrating diverse, multimodal datasets collected across environments and production systems. For crop applications, these datasets may include historical yield records, satellite and drone imagery, surveys of soil fertility and electrical conductivity and topography data, weather data, and other agronomic measurements. Likewise, livestock AI models can benefit from combining behavioral, physiological, morphological, and environmental information. For example, integrating vulva swollenness features with behavioral indicators has shown promise for improving AI-based estrus detection across different herd sizes and housing conditions.

This objective will develop robust AI tools that leverage multimodal sensing, large-scale datasets, and advanced machine learning techniques to improve crop production, livestock management, and food processing while enhancing model accuracy, robustness, and transferability across diverse agricultural systems.

B. Detailed activities/procedures

The team will conduct research on AI-enabled crop and livestock production, postharvest processing, and agrifood quality evaluation. Research activities will focus on crop and animal status monitoring, yield estimation and prediction, biotic and abiotic stress detection and management, and food quality assessment using multimodal sensing and advanced AI techniques. Various imaging and non-imaging sensors will be used to collect data on crop, food, and animal status.

In crop production, datasets commonly available to producers—including soil electrical conductivity, soil survey information, historical yield records, satellite and drone imagery, and historical and forecasted weather data—will be integrated to develop AI models for high-resolution yield prediction and precision management. RGB imaging will be used to monitor crop growth, detect nutrient deficiencies, estimate yield, and identify diseases, pathogens, and weeds, while thermal imaging will support early detection of crop stress. In particular, AI models will be developed for robust crop/weed recognition, enabling precision, targeted weed control (Deng et al., 2026). Hyperspectral imaging will be employed primarily to identify informative wavelengths associated with specific stressors and guide the development of cost-effective multispectral sensing systems suitable for field deployment. Project efforts will also investigate transforming high-resolution RGB imagery into hyperspectral-like multispectral data for qualitative agrifood assessment and disease phenotyping (Oloyede and Adedeji, 2025). As an example, AI models such as YOLO and VGG will be evaluated for automated assessment of soybean root rot severity caused by Rhizoctonia solani, enabling rapid, objective, and high-throughput disease phenotyping.

Plant diseases often alter chlorophyll content and canopy reflectance in the RGB, near-infrared (NIR), and shortwave infrared (SWIR) spectral regions (Krezhova et al., 2017; García-Vera et al., 2024). Machine learning and deep learning models—including convolutional neural networks (CNNs), support vector machines (SVMs), k-nearest neighbors (KNN), and partial least squares regression (PLSR)—will be investigated for disease detection and classification (García-Vera et al., 2024). Combined with drone-, sensor-, and smartphone-based imaging, these approaches will enable real-time crop monitoring, support disease forecasting, optimize pesticide applications, and facilitate site-specific crop management. Previous studies demonstrated that AI-guided precision spraying can substantially reduce pesticide use compared with conventional broadcast applications (Zanin et al., 2022).

Although the development of novel AI architectures is beyond the scope of this project, state-of-the-art AI models—including CNNs, region-based CNNs, YOLO, SSD, Vision Transformers (ViTs), Segment Anything Model (SAM), generative models, and recurrent neural networks—will be evaluated and adapted to improve crop and livestock monitoring under diverse production environments. Sequence-learning approaches will be investigated for integrating temporally distributed datasets, while strategies such as training under varying illumination conditions will be explored to improve model robustness and transferability.

For postharvest applications, AI-enabled machine vision systems are promising for automated quality evaluation, grading, and sorting of agricultural products (Xu et al., 2024; Xu & Lu, 2026). Human inspection remains labor-intensive and subject to variability and errors, particularly for defect detection. Automated vision systems will therefore be investigated to improve grading consistency, throughput, and traceability throughout postharvest operations (Blasco et al., 2017). Non-destructive sensing technologies, such as near-infrared spectroscopy, multispectral/hyperspectral imaging, 3D vision, and acoustic sensing, will be integrated with machine learning models for applications such as pest detection, adulteration detection, and food quality and safety evaluation (Adedeji et al., 2024; Rady and Adedeji, 2020; Ekramirad et al., 2024).

Research on livestock production will focus on AI-based health, welfare, and production monitoring. Planned activities include body weight estimation, automated behavior recognition, and respiratory rate monitoring for multiple livestock species.

Body weight is an important factor associated with many management practices in livestock production and a good indicator of animal health (Uluta & Saat, 2001). Previous studies used cattle’s body measurements, such as chest girth, body length, and wither height, to predict body weight and achieved good results (Ozkaya & Bozkurt, 2009). The RGB and 3D images will be combined to accurately extract each animal’s chest girth, hip-width, body length, and wither height. The multimodal imaging will be used to automatically extract morphological traits such as chest girth, body length, hip width, wither height, body volume, contour area, and body dimensions. These measurements, together with additional information such as age and breed, will be integrated into AI models—including PointNet and multilayer perceptrons (MLPs)—to estimate body weight in cattle and broilers.

To improve the robustness of behavior recognition across production facilities, automated background removal techniques, including Segment Anything Model and frame subtraction, will be investigated to isolate animals from complex production environments. The resulting models will be evaluated for their ability to maintain reliable performance across different housing systems. Respiratory rate is an important physiological indicator associated with heat stress, disease, and reproductive events. This project will develop AI-assisted 3D video processing techniques to continuously and remotely monitor respiratory rate and respiratory intensity in swine. The proposed framework will combine computer vision with AI-based signal processing to extract respiratory signals under commercial production conditions and will provide a foundation for extending this technology to other livestock species, including cattle and poultry.

 

Obj. 1b. AI tools for autonomous system perception, localization, manipulation, and planning for agroecosystems.

A. Introduction

Automation is becoming increasingly important throughout the agrifood supply chain as labor shortages, production costs, and demands for greater production efficiency and sustainability continue to increase. Although conventional agricultural machinery has significantly improved production efficiency, autonomous robotic systems require advanced AI to perceive complex environments, make intelligent decisions, and perform reliable localization, navigation, manipulation, and task planning under highly variable field conditions.

Recent advances in robotic platforms—including unmanned ground and aerial vehicles, robotic manipulators, quadruped robots, soft robots, and humanoid robots—combined with multimodal sensing and edge computing have greatly expanded opportunities for agricultural automation. AI techniques enhance robotic perception by integrating information from multiple sensors while enabling adaptive control through learning-based approaches such as reinforcement learning and imitation learning. Together, these technologies provide the foundation for robust autonomous systems capable of operating efficiently in dynamic, unstructured agricultural environments.

This objective will develop AI-enabled perception, localization, manipulation, and planning algorithms that improve the autonomy, adaptability, and operational efficiency of robotic systems for agricultural production and processing.

B. Detailed activities/procedures

Our research activities will focus on developing AI tools that improve robotic perception, localization, manipulation, navigation, and planning for autonomous agricultural systems. Closed-loop robotic systems will be developed by integrating multimodal sensor data with control strategies that continuously compare system outputs with desired operating conditions to optimize robotic performance.

Large datasets will be collected from both field operations and simulation or digital twin environments to support model development and validation. Multimodal sensing will include two- and three-dimensional LiDAR, RGB and depth imaging, distance sensors, GPS, inertial measurement units (IMUs), rotary encoders, tactile sensors, and other sensing technologies appropriate for specific applications. AI models, including CNNs, transformer-based architectures, and other DL approaches, will be developed to extract informative features from multimodal sensor data and improve perception, localization, object recognition, and environmental understanding. These features will support predictive models for autonomous decision-making while reducing the dimensionality and complexity of heterogeneous sensor data.

Robotic platforms will incorporate commercially available electronics, robotic manipulators, autonomous vehicles, and customized agricultural machinery. Control strategies will combine conventional feedback control with data-driven approaches, including reinforcement learning and imitation learning, enabling robotic systems to directly map multimodal sensor observations to control actions. Compared with rule-based controllers, learning-based methods have greater potential to accommodate nonlinear system behavior and adapt to diverse production environments.

To facilitate deployment under field conditions with limited internet connectivity, predictive models and control algorithms will be implemented using edge computing platforms, including smart cameras, NVIDIA Jetson embedded processors, and GPU-enabled edge servers. Field experiments will be conducted across multiple production environments, geographical regions, and growing seasons to evaluate the robustness, scalability, and transferability of the developed systems. Research on the use of different protocols will be conducted through best practices from ongoing AI projects from multistate members.

 

Obj. 1c. Natural resources scouting and monitoring.

A. Introduction

Healthy soil and water resources are fundamental to sustainable agricultural production and ecosystem resilience. Soil supports essential ecosystem services, including nutrient cycling, water regulation, biodiversity, and carbon sequestration. However, intensive agricultural practices have accelerated soil degradation through organic matter depletion, erosion, compaction, and salinization, resulting in reduced productivity, declining soil health, water pollution, and increased greenhouse gas emissions.

Soil health is closely linked to soil structure and soil organic carbon (SOC), both of which influence water retention, aeration, nutrient availability, and crop productivity. Traditional measurements of SOC, soil fertility, and other soil health indicators rely on extensive field sampling and laboratory analyses that are labor-intensive, time-consuming, and costly. Recent advances in sensing technologies and AI provide opportunities to integrate soil, spectral, environmental, and management data for rapid, accurate, and cost-effective assessment of soil health, supporting precision agricultural management.

Water quality monitoring presents similar challenges. Agricultural nutrient runoff and climate change have increased the frequency and severity of harmful algal blooms (HABs), while many important water quality indicators cannot be directly measured through conventional remote sensing. Integrating multimodal datasets—including remote sensing, weather, hydrologic, and geographic information—with AI models offers new opportunities to predict non-optically active water quality parameters and forecast HAB development, improving decision-making for water resource management.

This objective will develop AI-based approaches that integrate multimodal sensing, environmental observations, and computational modeling to improve monitoring, assessment, and management of soil and water resources across agricultural landscapes.

B. Detailed activities/procedures

Our research will begin with the development of harmonized multistate datasets that integrate soil, water, and environmental information under standardized quality assurance and metadata protocols. Existing and newly collected soil cores, laboratory measurements of physical, chemical, and biological soil properties, visible-near infrared (VNIR) and mid-infrared (MIR) spectra, proximal and remote sensing imagery, weather observations, terrain information, management histories, and water quality measurements will be standardized to enable reproducible integration across locations and production systems.

AI models will be developed using both data-driven and physics-informed approaches to estimate key indicators of soil health, including SOC, bulk density, texture, pH, cation exchange capacity, nitrate, ammonium, plant-available phosphorus, potassium, and other soil fertility parameters. Water quality models will predict nutrient export risk, harmful algal bloom susceptibility, and other watershed-scale indicators by integrating soil properties, hydrologic connectivity, weather, topography, land cover, and management information through multimodal data fusion.

Monitoring networks established across diverse soil types, landscape positions, and management systems will support long-term evaluation of soil carbon dynamics, bulk density, and other soil health indicators. Model performance will be evaluated with emphasis on uncertainty quantification, transferability across regions and seasons, and generalization through approaches including domain adaptation, hierarchical modeling, ensemble learning, multimodal foundation models, and federated learning.

3D computer vision techniques will be applied to soil cores, monoliths, and soil pits to generate digital meshes and voxel models describing aggregate structure, pore networks, root channels, and soil horizons. Quantitative structural metrics—including aggregate size distribution, macropore connectivity, and root channel density—will be related to infiltration characteristics, bulk density, SOC fractions, and nutrient retention. The resulting digital models, together with interpretive educational materials, will support classroom instruction, extension programs, field demonstrations, and immersive virtual and augmented reality applications that improve understanding of soil structure-function relationships.

To improve accessibility of advanced soil characterization, a publicly available web portal will be developed to allow users to upload VNIR/MIR spectra and receive AI-based predictions of soil properties together with uncertainty estimates, documentation, and guidance for appropriate interpretation. This platform will extend the original project vision by enabling robust soil property prediction across instruments while broadening access to AI-assisted soil analysis for researchers, educators, and agricultural practitioners.

 

Obj. 1d. Phenotyping and genotyping.

A. Introduction

High-throughput phenotyping is fundamental to modern plant breeding because it enables rapid, accurate, and non-destructive characterization of plant traits required to accelerate cultivar development. Recent advances in genomic selection, remote sensing, and AI have created new opportunities to integrate genomic, phenotypic, and environmental information, improving the prediction of complex agronomic traits and increasing breeding efficiency (Cuenca et al., 2013; Rambla et al., 2014; Sahin-Cevik et al., 2012; Vardi et al., 2008; Zheng et al., 2014; Albrecht et al., 2016).

Traditional field phenotyping relies heavily on manual measurements that are labor-intensive, time-consuming, and difficult to scale (Mahlein, 2016; Shakoor et al., 2017). Likewise, field surveys for disease and weed assessment remain costly and inefficient (Luvisi et al., 2016; Cruz et al., 2017; Cruz et al., 2019). Unmanned aerial vehicles (UAVs) equipped with imaging sensors provide a flexible, cost-effective platform for high-throughput phenotyping by enabling continuous monitoring of crop growth, stress, water status, and disease development (Pajares et al., 2015; Singh et al., 2016; Abdullahi et al., 2015; Abdulridha et al., 2018; Abdulridha et al., 2019). Coupled with machine learning, UAV-based remote sensing has become an important tool for genotype evaluation and breeding programs (Ampatzidis and Partel, 2019; Ampatzidis et al., 2019; Costa et al., 2021).

Recent advances in AI—including self-supervised learning, ViTs, SAM, Graph Neural Networks (GNNs), foundation models, and multimodal learning—provide more robust feature extraction while reducing dependence on large labeled datasets. These technologies enable seamless integration of genomic, phenotypic, and environmental information to improve genotype evaluation and selection (LeCun et al., 2015; Krizhevsky et al., 2012; Simonyan and Zisserman, 2015; Yoosefzadeh Najafabadi, 2023).

During the previous project period, the team established common sensing protocols, standardized data processing workflows, developed shared databases, deployed UAV-based field trials across multiple locations, and created AI algorithms for phenotypic trait extraction and genotype evaluation. Building on this foundation, the next phase will focus on integrating emerging AI technologies to improve scalability, robustness, and deployment of high-throughput phenotyping systems.

B. Detailed activities/procedures

Building upon the infrastructure and methodologies developed during the previous project period, research activities will focus on refining AI-enabled phenotyping systems while incorporating recent advances in machine learning, remote sensing, and autonomous field sensing.

Self-supervised learning, ViTs, SAM, and GNNs will be investigated to improve automated extraction of phenotypic traits while reducing labeling requirements and enhancing model robustness. Transformer-based and multimodal AI frameworks will integrate genomic, phenotypic, and environmental datasets to strengthen genotype-phenotype associations and improve predictive analytics for breeding applications.

To improve model generalization under diverse production conditions, synthetic training datasets generated using generative adversarial networks (GANs) and diffusion models will be explored for disease detection, stress assessment, and trait mapping. These approaches will augment existing datasets and improve AI performance across varying environments.

Research will also investigate Edge AI for real-time field phenotyping through onboard processing of UAV imagery, reducing post-processing requirements and enabling rapid in-field decision-making. In parallel, advanced AI algorithms will be developed to improve autonomous ground robotic platforms for high-throughput phenotyping, expanding their efficiency and integration into breeding programs.

Existing algorithms for plant parameter extraction will be further refined to improve estimation of biomass, plant architecture, disease severity, and abiotic stress. Additional multi-location field trials representing diverse agroecological environments will provide comprehensive validation of the developed AI models and sensing systems.

 

Objective 2: Data curation, management, accessibility, security, and ethics

Obj. 2a. Create open source agricultural datasets following FAIR (Findable, Accessible, Interoperable, Reusable) principles

A. Introduction

AI depends on large, diverse, and well-curated datasets to discover complex patterns and support data-driven agricultural decision-making. Although important progress has been made in developing standardized datasets for specific applications, including crop and weed imagery (Deng et al., 2024) and livestock systems (Bhujel et al., 2025), agricultural datasets remain fragmented across institutions, production systems, and sensing platforms. The lack of standardized, interoperable datasets limits reproducibility, benchmarking, and the development of robust AI models.

Over the last several years, the team has initiated and contributed to numerous AI-based efforts, resulting in the generation of various information-rich datasets. This Multistate project aims to integrate these datasets into a common, shareable, and standardized environment following the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. Such an effort is essential to maximizing the value of these datasets and accelerating AI innovation across institutions. However, the preparation of high-quality, large datasets is nontrivial, requiring substantial investments in data acquisition, categorization, annotation, standardization, and secure handling (Lu et al., 2020). Additional challenges include data encryption, de-identification, compliance with data-sharing and privacy requirements, all of which can create significant barriers to effective reuse. 

Coordinated data sharing among land-grant universities provides an efficient mechanism for addressing these challenges while enabling benchmarking, validation, and comparison of AI algorithms across institutions. Such datasets will also promote interdisciplinary collaboration among agricultural scientists, computer scientists, statisticians, and industry partners, accelerating the development and adoption of AI technologies for agriculture.

B. Detailed activities/procedures

Our priority will be to launch an inquiry to various land-grant universities to identify publicly available databases that they maintain. We will extend that to private companies affiliated with those universities and more. This way, we will compile a list of participating units and their capabilities of sharing or exchanging information. A standardized survey/questionnaire will be sent to all unit heads of extension programs, and the relevant research will be identified. 

The group will also identify publicly available datasets and create an easy-to-search catalog. Our researchers will test the accessibility of USDA-established databases and identify databases that include crop phenotyping data, imagery data sets, crop and livestock production and management datasets, satellite imagery, soil information, weather data, and economic data. Multiple modalities will be included in those datasets, and they will be as de-identified as possible. Standardization will be the key element, and for that effort, a thorough literature review will be conducted to identify established practices in the field. We will be building upon standardizations with an emphasis on AI tools development. The questionnaire will also be sent to the various companies like Oracle, Microsoft (Azure), as well as Digital/precision Agriculture companies like Mothive, Ag-Analytics, Agri-Data, and more working in the field of data management that have expressed an interest in participating. 

After combining the feedback from the questionnaires above, the group will develop new publicly accessible, large-scale image datasets designated for agricultural vision tasks (e.g., weed detection and control) with image- and pixel-level annotations and will benchmark the state-of-the-art deep learning algorithms for the datasets. Many small-scale datasets exist at this stage in various universities, so bringing them together will be a challenge but also a straightforward process after a few affiliated universities create the first venues of collaboration. The main source of these datasets will be the initial core researchers in this Multistate proposal, as explained in Objective 1.

The group will create and share de-identified datasets from participating farmers, extension agents, and other partners. This will include obtaining new samples and laboratory data to create datasets. These automated processes are already in place, and some of the co-PIs have already been extending them (LSU’s connection with Ag Analytics is such a platform that is now being explored further). The group will share benchmark attempts and the corresponding standardized datasets. All de-identified information, including transfer protocols, de-identification protocols, and final results, will be available to participating universities. Finally, we propose the creation of various educational programs for students and extension agents, but also for interested researchers in the field, whose goal would be to provide a robust understanding of the theoretical underpinnings of the basic AI models, in an Agricultural setting. 

Yearly meetings and workshops on applications of AI with an emphasis on hands-on learning, especially for students in agriculture, as well as forums and yearly talks about the ethical issues in AI and the effect they may have on various stakeholders, will be established. The program will be connecting our experts to specialists in Computer Science, who will then participate in various workshops and presentations about the ethical issues with the use of AI and the better distribution of results.

 

Obj. 2b. Data Standardization and testbed development

A. Introduction

One of the major challenges hindering the widespread adoption of AI in agriculture is the absence of standardized datasets and integrated software solutions tailored to handle agricultural big data for end-users. Agricultural data is inherently complex and heterogeneous, encompassing diverse sources. For example, in crop production, data is generated by different sensors at varying capacities and scales (e.g., soil moisture, satellite imagery, drone data, weather information, field notes, genetic profiles, and management records). Data generated from livestock production systems is also vast, diverse, and increasingly digital, encompassing a wide range of biological, environmental, and management variables. This includes real-time data from precision livestock farming technologies such as wearable sensors, RFID tags, automated feeders, climate control systems, and video monitoring, which track metrics like animal movement, weight, feed intake, temperature, reproductive status, and health indicators. In addition, forage and pasture management and quality data, genomic and phenotypic data, veterinary records, and milk or meat production outputs further contribute to the data system. In both crop and animal production systems, datasets often vary in format, structure, resolution, and semantics, making it difficult to aggregate, share, or apply AI models across different contexts. Thus, there is an increasing need to develop data standardization protocols based on FAIR data principles (Thomasson et al., 2025),  allowing for the development and utilization of advanced ML/AI models. 

Besides the integration and standardization of the resulting datasets, the creation of dedicated testbeds presents an opportunity for a continuous data-generating process that will serve as a fixed point in the data creation and accumulation. A testbed is a platform for conducting rigorous, transparent, and replicable testing of scientific theories, computational tools, and new technologies.  We suggest adopting a digital portal or data hub with a user-friendly interface to integrate tools, implement algorithms, and facilitate data sharing and collaboration. For example, Texas A&M AgriLife developed a data portal named UASHub to facilitate UAS-based data communications. The UASHub integrates electronic field notes, raw and post-processed UAS imagery data for sharing, visualization, analysis, and interpretation of large volumes of UAS-derived data. The UASHub includes tools to integrate online data management and access tools, allowing research scientists to download both raw and processed geospatial data products to their workstations for further analysis. Establishing a collaborative, cloud-based, open-access platform that connects multiple experimental and data collection sites—and transforming this into an AI-ready testbed—will facilitate innovation, experimentation, testing, education, and community involvement in AI. By incorporating datasets from a range of production environments and management systems, the testbed will promote interdisciplinary research, teaching, and outreach across fields such as agriculture, animal science, geospatial science, computer science, data science, and statistics.

B. Detailed activities/procedures

Developing standardized datasets and AI-ready testbeds in agriculture requires a coordinated series of activities that span technical, organizational, and community dimensions. Based on the results obtained from assessing the publicly available databases (Objective 2A), we plan to evaluate the data quality of those databases and verify the quality of both the data and metadata. Additionally, rigorous planning and experiments will be conducted to develop data standardization protocols along with the pipeline to store and share metadata. Simultaneously, efforts will be made to design and deploy data integration pipelines that can clean, align, and aggregate diverse datasets such as sensor readings, satellite imagery, genetic information, and field observations. AI-ready testbeds will then be established by identifying and equipping representative production sites with real-time sensing technologies and cloud-connected infrastructure, enabling continuous data collection, transfer, and access. Annotation tools and quality control procedures will be implemented to label datasets for machine learning, facilitating the development and benchmarking of AI models. To ensure accessibility, a cloud-based digital platform or data portal will be created, offering user-friendly tools for visualization, analysis, and download of raw and processed datasets—similar to the UASHub developed by Texas A&M AgriLife. Education and outreach will be integrated through training programs, curriculum development, and hackathons to build capacity and foster interdisciplinary collaboration.

 

Obj. 2c. Agricultural data governance, ethics, and privacy

A. Introduction

Agricultural AI depends on large, shared datasets collected from ground-based sensors, drones, satellite imagery, farm equipment, and Internet of Things (IoT) devices. These datasets often contain sensitive information about farming operations, land use, management practices, environmental conditions, and, in some cases, personal information associated with producers and workers. Without appropriate governance, data sharing can create concerns regarding ownership, consent, transparency, privacy, equitable benefit sharing, and responsible AI development.

Current governance frameworks do not adequately address the complexity of agricultural data sharing. Traditional open-data licenses, including CC BY-SA, ODbL, and GODAN, facilitate data reuse but generally focus on raw datasets while providing limited guidance on ownership and use of derived products, such as predictive models, analytical reports, and proprietary databases. This gap can discourage producer participation when data contributors perceive that commercial value generated from shared data is not appropriately recognized or protected.

Differences in stakeholder perspectives further complicate agricultural data governance. Federal agencies generally promote public access to research data, whereas producers often regard farm data as private property. Plant breeders, technology providers, research institutions, and government agencies may also have competing interests regarding ownership, intellectual property, commercialization, and public dissemination. Although voluntary governance initiatives—including the Privacy and Security Principles for Farm Data (American Farm Bureau Federation, 2016; Ferris, 2017), the Australian Farm Data Code (NFF, 2020), the French Charter on Agricultural Data (Data Agri, 2018), the Swiss Charter on Digital Agriculture (Agridigital, 2018), and the EU Code of Conduct (Copa-Cogeca et al., 2018)—have improved transparency, these agreements remain largely voluntary and provide limited guidance for emerging AI applications and collaborations involving universities, producers, and agricultural technology providers.

Research institutions face similar challenges. Existing agreements, such as those developed through the USDA National Agricultural Producers Data Cooperative (NAPDC), recognize producer ownership while supporting federally funded research through negotiated data-use rights. However, these agreements generally address only relationships between producers and research institutions, leaving unresolved issues involving third-party technology providers, derivative AI products, and standardized methods for agricultural data de-identification. Developing practical governance frameworks that balance producer ownership, public-access requirements, privacy protection, and responsible AI innovation therefore remains a critical research need.

B. Detailed activities/procedures

We will begin with an analysis of existing governance documents and stakeholder interests to identify where claims conflict. Further, we will develop template agreements covering the data-sharing scenarios these projects actually encounter, across different data types, technologies, and stakeholder positions, and addressing ownership, permitted use, and privacy protection. The result will be a set of workable, ethically balanced agreements, drafted with legal expertise, that state the rights, obligations, and benefits of each party clearly enough to keep the system both accessible and equitable.

Stage 1: Governance document corpus and clause analysis

We will assemble a corpus of data governance documents in agriculture spanning five layers. The federal layer covers data management and public-access requirements from federal agencies (e.g., USDA and NSF), including repository designation and deposit rules. The institutional layer covers executed data use and transfer agreements requested in de-identified form from sponsored programs offices at ten to fifteen land-grant institutions and related codes in their states, together with institutional data classification policies. The industry layer covers public terms of service, license agreements, and privacy policies from agriculture technology providers across equipment, agronomic platforms, imagery, and livestock systems, extending the corpus construction methods our group has already applied to approximately 160 such documents. The sector governance layer covers Ag Data Transparent principles and certified agreements, the EU Code of Conduct on agricultural data sharing, and producer organization data policies. A reference layer covering health-sector de-identification standards and federal statistical disclosure practice provides comparison points for the privacy work in Stage 3, not models to be copied.

Each document will be coded against a fixed clause taxonomy: ownership of raw data, ownership of derivatives, permitted use, redistribution and sublicensing, retention and deletion, de-identification obligation, publication and pre-review rights, benefit return, liability, and survival of terms after termination. Coding records both the position a document takes on each clause and the conditions under which that position changes. The analysis yields two products. The first is an empirical baseline showing what is standard practice, what varies, and what no agreement in the corpus has ever conceded. Our earlier finding that derivative ownership is vendor-retained in roughly 94 percent of documents examined is an example. The second product is a clause library of verbatim language supporting negotiation and institutional review, allowing clause selection and drafting per sharing scenarios.

Stage 2: Stakeholder perspectives

Governance documents record the rules but not the beliefs stakeholders agree to. Three respondent groups will be studied under a human subjects protocol. Producers will be surveyed, with a target of 250 to 300 responses recruited through Extension networks, commodity organizations, and cooperatives, measuring their understanding at enrollment, such as what they believe they own once data leaves their equipment, and which secondary uses they find acceptable. The survey will present concrete scenarios rather than abstract questions about ownership. Principal investigators, sponsored programs officers, and technology licensing staff in research projects will be interviewed, with a target of twenty to twenty-five interviews covering how vendors are currently selected, whether data terms enter that decision at all, and which provisions a project will not execute. Technology providers will be interviewed individually, ten to fifteen firms under nondisclosure where required, on what a flow-down provision would need to contain for legal approval and who holds that authority. All three instruments are aligned to the clause taxonomy in governance document analysis so that stated perspectives can be compared directly against coded terms.

Stage 3: Conflict identification and parameterization of agreement terms

Two classes of conflict emerge from the combined evidence. Term-versus-term conflicts arise where documents governing the same data disagree, such as vendor terms retaining derivative rights, for instance, against an agency requirement to release derived products. Term-versus-perspective conflicts arise where a party's belief does not match the instrument it signed, as when producers assume they own model outputs trained on their fields, or principal investigators assume producer consent settles the release question. Every conflict identified will be entered in a register and ranked on two axes: how often it appears across the corpus, and how difficult it is to resolve.

The ranked conflicts feed a parameterized rule set that maps the characteristics of a proposed partnership onto appropriate clause settings. Six parameters describe a partnership. Two concern the data itself: its type (e.g., agronomic and yield, imagery and geospatial, financial, etc.); and the technology that collected it (e.g., a producer-owned instrument, a vendor cloud platform, a university-deployed sensor, etc.). A third captures privacy sensitivity, assessed as the re-identifiability of commercial sensitivity to competitors, personal data, farming operations, etc. The remaining three concern what will be done with the data (e.g., the intended use, release and model training, hosting, etc.)

The mapping will be derived empirically wherever the corpus permits. For parameter combinations that appear in existing agreements, the observed convergence becomes the default setting; where existing agreements diverge, the combination is flagged as a negotiation point rather than assigned a default. Combinations absent from the corpus, which are concentrated in cases involving public release of derivative products, will be resolved by reasoning from the federal requirements layer and the privacy analysis and marked as untested. Privacy terms specifically are set by data type, sensitivity, and repository role together, with de-identification requirements, aggregation thresholds, and spatial and temporal generalization determined from empirical relinkage testing rather than borrowed from health-sector practice.

Instruments, case application, and partnership protocol

The conflict register and the parameter mapping are used together as an instrument to draft a contract for a specific partnership. Three cases will test the instruments on live projects. The first covers a producer sharing data with a university under an open release obligation. The second covers a university and a technology provider, with the university hosting the repository. This is the harder case, because the university acts as redistributor rather than recipient. The third runs the first two in sequence as a three-party chain, which is where gaps appear between what the producer conveyed and what the university must be able to promise. Each case will be drafted under the parameter mapping, executed on real data, and logged for routing time, edits, refused provisions, and producer comprehension.

Case findings drive a final revision, released for general use. The sequence is then codified as a partnership formation protocol with three elements. A short questionnaire asks about a proposed partnership and points to the clauses that apply. The document collection and the parameter mapping are reviewed regularly, since vendor terms and agency requirements change. If a partnership involves a data type or a technology the mapping does not cover, only the mapping is revisited rather than the whole analysis.

 

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

A. Introduction

The human and social dimensions of AI knowledge transfer are essential for applying systems approaches to stakeholder engagement, science communication, and experiential learning to strengthen agricultural workforce development (Daigh et al., 2024). Research has shown that knowledge alone is often insufficient to drive adoption, especially when political, economic, or social factors play a role (Knowles, 1984; Kolb et al., 2014). The adoption of AI in agriculture is influenced by individual personality, information dissemination methodology, environmental conditions, structural factors, technology attributes, demographic variables, farmer education, household size, land tenure, access to credit, and the availability of extension services (Ruzzante et al., 2021). Facilitators of adoption and the establishment of trust between farmers and technology providers are especially critical for its successful implementation (Sood et al., 2022). Active collaboration between scientists and stakeholders can improve technology transfer and adoption. For example, field days that include active engagement of extension personnel and other stakeholders (e.g., growers) have proven to be effective tools in promoting experiential learning (Knowles, 1984; Kolb et al., 2014), which increase interest and willingness in adopting new technology (Rogers, 2003).

The major focus areas under this objective are to (i) support AI algorithms development team during the development of user-friendly digital tools/platforms by engaging stakeholders through User-Centered Design (UCD) (Parker, 1999) process (ii) train consultants, extension specialists, county agents, producers and allied industry on the use of digital and AI-based tools/platforms for precision farm management (technology transfer), through field days, workshop and outreach programs that provides hands on practical training, (iii) grow the number of next-generation experts on AI and digital agriculture tools development and use. The detailed procedure is described below to illustrate the method that will be followed to achieve this objective. We believe that this model will serve as a foundation for AI technology transfer, adoption, and workforce development. The experience and methods will be transferable and scalable to other states according to their needs and resource availability.

B. Detailed activities/procedures

Advancing Equitable AI in Agriculture Through User-Centered Design, Stakeholder Engagement, and Policy Advocacy

To successfully develop and deploy AI tools in agriculture, it is essential to go beyond merely sharing information and instead actively involve stakeholders at every stage of the process. This approach includes conducting structured needs assessments and involving end users from the very beginning of technology development, following a User-Centered Design (UCD) framework (Parker, 1999). User-Centered Design is an iterative design process that involves end-users in all phases of product development and addresses their needs (Barnum et al., 2020). Producer heterogeneity differences in farm size, technological readiness, educational attainment, and resource access play a substantial role in shaping adoption dynamics, which makes user-centred approaches imperative (Schimmelpfennig, 2016; Paudel et al., 2021). Policy briefs and evidence-based advocacy materials are developed to influence state and federal investments in digital infrastructure and to promote supportive mechanisms for integrating AI into agriculture. The integration of AI with IoT can optimize resource management, improve productivity, and reduce greenhouse gas emissions in agriculture, although policy interventions will be necessary to encourage broad adoption (Mohamed et al., 2021; Morkunas et al., 2024). This comprehensive approach integrates user-centered design, experiential learning, inclusive communication, modular education, and long-term impact assessment to ensure that agricultural advancements are technologically sound, socially grounded, and equitably accessible.

Empowering Agricultural Communities Through Extension Education and Technology Transfer

Historically, extension and outreach programs have often followed a deficit model, which operates on the assumption that people will adopt emerging technologies or scientific innovations if they are simply provided with more information (Nisbet & Scheufele, 2009). This project proposes a comprehensive extension, outreach, and education model that will function as both a national resource and a scalable framework for AI technology transfer in agriculture. The model will leverage a network of extension specialists and county agents trained in the application of AI tools for precision agriculture. For example, Texas A&M AgriLife Extension operates a broad network of Agriculture and Natural Resources Extension Agents and Integrated Pest Management (IPM) specialists across nearly every county. Virtual reality simulations will be used to offer immersive, experiential training on AI applications such as pest detection, irrigation management, and yield forecasting. Building upon this existing infrastructure, extension crop specialists in strategic locations will collaborate with county agents and IPM staff to facilitate the dissemination, evaluation, and refinement of AI-based tools across diverse cropping systems and ecological zones. To reach underserved and remote regions, mobile training units will provide in-person demonstrations in areas with limited digital connectivity. Social media platforms will also be used to share concise and engaging educational content that connects with younger and more technologically adept farmers. Community-Based Participatory Research (CBPR) initiatives can enable producers, researchers, and industry partners to co-design and refine technologies based on practical needs.

Additional strategies to deepen engagement include establishing regional Farmer-Led Innovation Councils to guide AI deployment, organizing participatory scenario planning workshops to explore potential future agricultural scenarios, and creating farmer innovation labs to serve as hubs for demonstrations, pilot testing, and user feedback. More collaborative approaches, such as active engagement between scientists and stakeholders, have demonstrated greater success in promoting technology transfer. For example, field days that include hands-on participation from extension agents, producers, and other stakeholders can effectively promote experiential learning and encourage the adoption of innovations (Rogers, 2003). Capacity building will be achieved through initiatives such as AI Literacy Bootcamps, which will provide short and intensive training for farmers and extension agents to demystify AI concepts and applications. Train-the-Trainer programs will equip local educators and extension agents with the skills and pedagogical tools needed to effectively teach AI concepts in rural communities.

Educational Innovation and Inclusivity in Agricultural AI: A Participatory Framework for Experiential Learning and Industry Collaboration

This project will develop modular, project-based curricula for undergraduate and graduate students, focusing on three core areas: high-quality data collection, data processing and analysis, and AI applications in agriculture. Partnerships with land-grant universities, minority-serving institutions, and agricultural technology companies will ensure diversity, inclusion, and geographic reach. AI Mentorship Networks will pair students and early-career professionals with experienced researchers and industry leaders to provide ongoing guidance and career development opportunities. Educational expansion will include dual enrolment AI-in-agriculture courses for high school students, certificate programs at community colleges for non-traditional learners, and gamified learning platforms that deliver interactive modules to increase engagement and retention. Micro-credentials or stackable certificates will be offered in areas such as data science, machine learning, and AI-driven farm management. To enhance inclusivity and accessibility, culturally responsive curricula will be designed to incorporate local agricultural practices and cultural contexts, making them more relevant to diverse audiences. Bilingual community ambassadors will act as liaisons and trainers in underserved regions. Low-bandwidth and offline-compatible AI tools will be developed to address connectivity limitations in rural areas. Digital literacy training will be delivered alongside AI modules to promote equitable access. Students will have opportunities for internships and apprenticeships with agricultural technology startups and research farms. Activities such as hackathons, innovation challenges, and interdisciplinary design sprints will encourage creativity and problem-solving skills. A new course on the human dimensions of AI will address the ethical, cultural, and socio-economic aspects of technology adoption, ensuring that graduates are not only technically skilled but also socially and ethically informed.

Monitoring, Evaluation, and Impact Assessment for Scalable Agricultural AI

The monitoring and evaluation framework will measure behavioral change metrics that capture shifts in farmers’ decision-making, risk tolerance, and technology adoption after interventions. Interactive dashboards will be developed to allow stakeholders and project leads to track tool adoption, training participation, and farm-level outcomes in real time. Longitudinal impact studies will assess environmental, economic, and behavioral changes resulting from AI adoption, which will inform the scaling and refinement of extension approaches.

The project will establish an open-source repository of datasets, use cases, and AI models to be shared nationally and globally. Collaborations with international organizations such as CGIAR and FAO, as well as global universities, will enable the adaptation of solutions for use in developing agricultural systems.

Measurement of Progress and Results

Outputs

• Obj. 1a: Major findings will be published, demonstrating the application of AI for crops, livestock, and postharvest systems. Trained deep learning algorithms will be available for crop yield estimation, crop stress assessment and management, monitoring of animal health, welfare, and behavior, and postharvest quality evaluation. Algorithms will be developed to automate the workflow of multi-scale data analysis, information extraction, information scaling, and synthesis for generating plots, field crop characteristics maps, or animal health and productivity indicators, supporting data-driven decision-making
• Obj. 1b: New AI methods will be developed for: (1) advanced perception, localization, and manipulation for robotic production (harvesting, management, pruning, etc.) and processing (sorting, handling, packaging, etc.) tasks, (2) object detection and dynamic mapping in the field and processing plants, (3) end-to-end path planning and obstacle avoidance approaches in agricultural settings and (4) edge computing approaches for perception, localization, and task planning.
• Obj. 1c: Soil cores, plant tissues, and water samples will be collected from multiple U.S. states and analyzed using VNIR and/or MIR spectroscopy alongside other sensing technologies and laboratory measurements of key physical, chemical, and biological soil indicators. These datasets will form the foundation for the development of AI‑based predictive models capable of estimating soil health properties directly from spectral inputs. Methods for calibration transfer will be created and tested for model robustness and accuracy. A comprehensive, openly accessible soil-water-plant database will be compiled to support modeling and methodological innovation. Using these resources, a web‑based prediction interface will be designed that allows users to upload lab and sensing data related to soil health and water quality. This online system will serve farmers, researchers, conservation agencies, and land managers by providing scalable, rapid assessments derived from advanced AI/ML approaches. The combined outputs—soil-plant-water datasets, physics (or soil science)-informed calibration models, transfer algorithms, spectral libraries, and decision‑support tools—will comprise a nationally relevant resource for soil health monitoring and natural resource stewardship.
• Obj. 1d: A standardized, multi-scale, multi-modal dataset including imagery and measurements of plant architecture, yield, yield-related traits, and disease across environments and time points. AI-based image analysis tools will be developed for predicting yield, drought response, and disease. Prototype AI-enabled prototyping platforms with integrated data visualization and decision-support tools will be developed to support variety selection and high-throughput phenotyping. A harmonized high-throughput multi-omic pipeline and centralized database will enable cross-location data interoperability and ML-driven crop improvement.
• Obj. 2a: A shareable database schema with specific access capabilities will be created as well as a methodology to connect to it safely with both adding and downloading capabilities. Recurring workshops discussing the theoretical underpinnings of AI models in agriculture as well as the ethical use of AI and various security issues.
• Obj. 2b: The primary end product from this objective will be a database management system: (1) a UAS-based platform to collect detailed and high quality HTP data for research plots or commercial fields, (2) automated procedures for data processing, analysis, and growth parameter extraction, to analyze, visualize and interpret collected data, and (3) web-based algorithms for data management and communication for all project scientists
• Obj. 2c: 1) A map of agricultural data value chain and specific logic of data governance (e.g., ownership of data and data derivatives) will be created by analyzing existing contractual documents in technology adoption cases in agriculture and the policy/regulations from government agencies. 2) A database of de-identified interview transcripts will be created from in-depth social studies targeting agriculture stakeholders, including farmers, researchers, and ATPs. 3) A data governance agreement template will be created based on previous results in facilitating different governance scenarios.
• Obj. 3: Several annual workshops will be developed on a need basis. In the last five years, several of these specialized workshops were provided during the annual AI in Agriculture Conference that includes hands-on experience on software use, data analytics, etc. Also, technology expo, seminars, webinars, podcast series, field days, and in-service training will be established that target primarily extension agents, farmers, and allied industries, focusing on the uses of AI in agriculture. Extension publications, infographics, and fact sheets will be developed to present advancements in AI-based technologies. Classes with a focus on AI applications in agriculture and natural resources will be added and evaluated on a yearly basis. Certifications and Specialization programs will be added to the already established Minors in relevant fields.

Outcomes or Projected Impacts

• Obj. 1a: The projected outcomes include improved preparation for harvest and storage through enhanced yield prediction, timely implementation of management strategies to minimize the risk of pest invasion, and reduced losses associated with low-quality produce entering the supply chain. These advancements are expected to lead to more efficient resource use and higher overall profitability. Farmers and stakeholders will benefit from the deployment of advanced algorithms for accurate yield forecasting and variable rate input applications, optimizing operational decisions. In the area of animal health and welfare, expected impacts include the development of automated assessment tools that support more effective farm management, improved animal welfare, increased productivity, and enhanced profitability across livestock operations.
• Obj. 1b: Automated bioproduct detection and processing systems using AI to improve performance and efficiency will enhance site-specific crop/animal management and processing practices to increase yield, reduce cost, enhance biosecurity, and improve grower profit. Implementation of AI-based edge processing will improve execution speed and perception effectiveness in a lower cost embedded vision controllers.
• Obj. 1c: The AI‑calibrated models and the web‑based soil prediction portal developed under Objective 1c will expand the accessibility and accuracy of soil health assessments for diverse end users, including farmers, county agents, researchers, and educators. By reducing dependence on traditional laboratory methods, these tools enable more frequent, lower‑cost soil monitoring that directly supports improved nutrient management, soil conservation planning, and precision agriculture. The integration of computer vision and 3D soil modeling will also enhance educational and outreach impacts. High‑resolution, spatial–temporal reconstructions of soil profiles, landscapes, and natural‑resource conditions will help students better grasp the complexity of agroecosystems, including spatial variability, soil formation processes, and linkages between soil, water, and plant interactions. The development of virtual 3D soil monoliths, soil pits, and field environments, accessible through virtual reality (VR), will allow learners to explore realistic field scenarios regardless of weather conditions, resource availability, mobility limitations, or geographic constraints. These immersive, computer‑vision–based tools will sustainably improve experiential learning for students who may not have access to field sites, instructors, or higher‑education resources. Through virtual soil pits, cross‑sections, and field tours, learners can engage directly with soil morphology, texture, structure, horizons, and other diagnostic features traditionally observed only during in‑person fieldwork. This democratizes access to hands‑on environmental education and strengthens training in environmental and agricultural sciences. Collectively, these outcomes will improve the scientific literacy of future agricultural professionals, promote broader participation in soil and environmental sciences, and support the development of a digitally skilled workforce capable of applying AI, sensing, and computer vision tools. Long‑term impacts extend to enhanced soil stewardship, reduced off‑farm nutrient losses, increased agroecosystem resilience, and improved environmental sustainability across working lands.
• Obj. 1d: New knowledge will be gained on efficiently applying state-of-the-art deep learning models, pretrained on large-scale color image datasets, to crop phenotyping tasks with limited labeled data and heterogeneous sensor modalities. The work will foster transdisciplinary, multi-institutional collaboration in high-throughput phenotyping (HTP), supporting breeders and agricultural scientists in elite genotype selection and interpretation of experimental treatments. Adoption of standardized multi-omic integration pipelines will improve data consistency and interoperability across different breeding programs, enabling more accurate identification of elite genotypes and trait-associated markers. This will reduce the data generation costs by facilitating data sharing and reducing duplication efforts while accelerating genotype selection decisions.
• Obj. 2a: AI-based algorithms depend heavily on the existence of large, clean, and information-rich databases. By combining multiple datasets, the users will see a dramatic increase in their algorithms’ predictive percentages and pattern recognition ability.
• Obj. 2b: This objective will enable transdisciplinary scientists to communicate and exchange information and accelerate the development of agricultural applications for crop management. The data portal has the potential to deliver tools and methodologies for UAS-based HTP, enabling the development of AI-based cognitive tools. Data gathered using the proposed framework would provide users with a high level of both spatial and temporal details of crops at a scale.
• Obj. 2c: The key logics of data governance in the value chain will be clear to all stakeholders, mitigating underlying misunderstandings and ambiguities. Such clarity will bring ethical and stable relationships among different stakeholders in adopting data-intensive technologies with complex business structures. Potential conflicts and related costs can be significantly reduced by the developed agreement, which may provide a principled framework to address these conflicts. Most importantly, all stakeholders will obtain an opportunity in learning the related knowledge and best practices in adopting new technologies.
• • Obj. 3: An outcome of this Objective is to increase the acceptability, awareness, and trust of AI by stakeholders in the agriculture industry while building workforce capacity to meet emerging demands for AI-enabled food production and processing systems. This project will serve as a nexus point for education, training, and outreach in AI for agriculture and increase knowledge of available careers in AI for underrepresented groups, including minorities, women, rural residents, and other disadvantaged populations. Every year, we plan to publish over 200 extension articles, release over 100 podcasts, publish over 120 peer-reviewed articles, organize at least 10 workshops during the calendar year, and host AI in Ag. Conference, and educate more than 5000 students, post-doctoral researchers, faculty, farmers, data engineers, agricultural and biosystems engineers, and other stakeholders on AI developments for precision agriculture applications, nondestructive testing methods, large language applications in information dissemination, etc. via our education, extension, and outreach activities

Milestones

(2027):Obj 1: In year 1, multiple multistate research teams will be built within existing members and newly joined members to cover different tasks (a) AI for food, crop, and animal production and processing, (b) AI for robotics, (c) AI for natural resources, and (d) AI for phenomics and genomics. Obj. 2b: During year 1, we will select and develop testbed fields at various locations, and also a cloud-based “Data Portal” and software to upload, analyze, and visualize remote sensing data. Various educational workshops will be organized to train users of the portal and get feedback. Obj. 2c: We will develop an automated process to solicit government policies/regulations regarding data governance and industrial contractual documents from the internet. A natural language processing (NLP) packet will be created to extract the key logic and knowledge from these documents. A social study questionnaire will be developed based on the extracted logic and knowledge so that the following interviews or surveys will be grounded in specific cases. Obj. 3 A major milestone expected every year in the next five years is the annual workshops organized along with AI in Ag. Conference. Currently, there are more than five stations interested in hosting the annual AI conference beyond the time of this project, along with workshops that focus on hands-on training in data analytics for all stakeholders from academia and the agrifood industries. Also, stations will work together to consider proposal ideas that target grants such as the USDA Strengthening Agricultural Systems (SAS) for Artificial Intelligence (AI) for K12 Food and Agricultural Sciences. A working group formed for the extension and outreach programming among stations involved in extension activities will work to regularly share information on best practices to enhance various outreach programs such as webinars, seminars, field days, in-service training, and extension publications.

(2028):Obj 1: Standardized data acquisition and AI adoption protocols will be discussed for different applications, including (1a) yield monitoring, pest infestations, postharvest quality evaluation, animal behaviors, (c) soil properties, (d) crop phenomics and genomics. Obj. 2a: Within the first two years of the project, a common repository will be formed, and the necessary transfer protocols, safety protocols, and access protocols will be established. We will continue collecting data, expand testbed locations, add more users, and improve the analytical and visualization algorithms. Obj. 2c: We will continue and expand the educational workshops on uses of the system and receive feedback from participants. The map of data governance logic and practices will be created from solicited documents. We will start social studies to learn about stakeholders’ perspectives regarding data governance. Obj. 3 Sponsor and organize AI in Ag. conference and related workshops. Submission of SAS proposal for K12 stakeholder training in AI. The working group on extension and outreach programming continues to meet to share ideas and develop an online multi-institutional program that targets consultants and service providers on how to include AI tools in their portfolio.

(2029):Obj 1: There will be at least one collaborative research project and one submission of multi-university research proposals for each individual research team. The AI model's robustness, reproducibility, and transferability will be discussed and studied. Obj. 2b: In years 3-5, we will expand and refine the activities listed in year 2 and add many surveys to determine the use of the resulting portal and extend its capabilities. Obj. 2c: The social studies will be completed, and the results will be analyzed to reflect stakeholders’ perspectives about data governance. Obj. 3 Sponsor and organize AI in Ag. conference and related workshops. If funded, involved stations will execute the SAS project that provides training to teachers and students in K12 on AI applications in agrifood systems. In year 3, stations will begin to work on developing LLM tools (chatbots, Agentic AI, and Autonomous AI tools) that can enhance information dissemination in agrifood production in a way that is timeless and asynchronous extension with limited interference of experts based on tools such as Agentic AI know-how of AI applications. The working group on extension and outreach programming continues to share ideas and organize joint training sessions for stakeholders.

(2030):Obj 1: Items listed in Year 2 will be refined and improved. The database from different research groups will be organized and structured, and the AI model developed will be organized and disseminated. Obj. 2a. Connection with existing databases will be established on the 4th year mark. Obj. 2b: In years 3-5, we will expand and refine the activities listed in year 2, and add many surveys to determine the use of the resulting portal and extend its capabilities. Obj. 2c: An agreement of data governance will be drafted to facilitate all data value chains in this project, with consideration of ethical and fair collaboration and data management. All collected information and derived results will be integrated into educational materials. Obj. 3 Sponsor and organize AI in Ag. conference and related workshops. If funded, the SAS outreach-training project execution will continue. Also, the LLM tools will be tested in-house across different stations. The working group on extension and outreach programming continues to share ideas and organize joint training sessions for stakeholders.

(2031):Obj. 1a: At the end of the five-year project, key milestones will include the development of AI models and tools with high accuracy for predicting yield, detecting pest infestations, and assessing the quality of both in-field and postharvest agricultural products. A structured and comprehensive database, along with standardized data acquisition protocols, will be established as a foundational step to enable effective application of AI approaches for crop health and pest management. For animal production, milestones include (1) development of a generalized computer vision system to estimate cattle & broiler bodyweight, evaluate broiler behaviors, and remotely access the respiratory rate of sows, (2) commercialization and deployment of the smart monitoring systems in livestock systems and non-destructive models that include reconstructed HSI data-RGB-based for food contaminant source detection and quantification. Obj. 1b: At the end of the five-year project, key milestones will include the development of AI models and tools for improving robotic-enabled crop and animal production and processing practices. Structured simulation platform, standardized data processing protocols, and a robotic control pipeline will be established to improve 1) specialty crop harvesting, 2) automated greenhouse management, 3) automated postharvest management, and 4) automated animal production house management. 5) automated food processing. Obj. 1c: In years 4-5, a harmonized spectral library containing soil health measurements and corresponding spectra will be finalized and documented. Using the validated models and transfer algorithms, a user‑friendly, web‑based predictive portal will be developed, tested with stakeholders, and released publicly. Documentation, tutorials, and training materials will also be created to support adoption by farmers, agencies, and researchers. Obj. 1d: Enable the training and deployment of multimodal machine learning models for genomic selection, trait prediction, and genotype–environment interaction analysis in subsequent spin-off projects. Completion of an integrated genotyping and multiomics platform for large-scale AI model refinement, real-time decision-support development, and cross-location predictive deployment. Obj. 2a: The dissemination of information will begin in earnest in year five. Obj. 2b: In years 3-5, we will expand and refine the activities listed in year 2, and add many surveys to determine the use of the resulting portal and extend its capabilities. Obj. 2c: We will use the agreements to guide the activities for all involved parties to enhance the trust of the partnership with all stakeholders. The developed educational materials will be used in extension events and other related activities to leverage stakeholders’ awareness and knowledge about data governance. Obj. 3. Sponsor and organize AI in Ag. conference and related workshops. USDA SAS project execution will continue. At the end of year 3 of the SAS project (year 5 of the multistate proposal), we expect to have trained 2000 stakeholders across several stations that will participate in the project. The LLM chatbot that will be developed will be tested with farmers, and feedback will be evaluated to improve users' experience. The working group on extension and outreach programming continues to share ideas and organize joint training sessions for stakeholders.

Projected Participation

View Participation Form/Appendix E: Participation

Outreach Plan

This project will train county Extension agents, crop consultants, producers, and allied industries in the practical use of AI-based tools for farm decision-making. Because Extension personnel are trusted sources of science-based information, investing in their professional development creates a multiplier effect that accelerates adoption across agricultural communities.

Results will be disseminated through refereed publications, Extension bulletins, peer-reviewed fact sheets, digital infographics, and multimedia resources. Outreach activities include: (i) digital education materials and online modules; (ii) a podcast series highlighting applied AI innovations; (iii) topical webinars addressing seasonal and emerging applications; (iv) in-service training for Extension agents (in person and via eXtension); (v) producer field days and on-farm demonstrations; (vi) technology showcases and expos; and (vii) presentations and hands-on workshops at the annual AI in Agriculture Conference, a major multistate engagement venue. Materials will be delivered in hybrid formats (in-person and virtual), archived for asynchronous access, and distributed through county offices and institutional platforms. Efforts will prioritize outreach to underserved and underrepresented producers by partnering with minority-serving institutions, commodity groups, and community-based organizations. Field demonstrations will be hosted in diverse production regions to enhance geographic accessibility.

Through multistate collaboration, Extension networks, and partnerships with industry and producer organizations, the project will strengthen technology transfer and workforce capacity. Short-term outcomes include increased AI literacy and confidence in interpreting decision-support tools. Intermediate outcomes include integration of AI tools into Extension programming and increased producer adoption. Long-term impacts include improved productivity, resilience, and sustainability through data-driven management, strengthened public trust in AI applications, and sustained multistate leadership in digital agriculture innovation.

Organization/Governance

This multistate research project will be administered by three elected officers: a Chair, Vice Chair, and Secretary, who together will constitute the Project Executive Committee. The Executive Committee will oversee project activities, coordinate efforts among participating institutions, and facilitate annual project meetings.

Officers will be elected for two-year terms to ensure leadership continuity. At project initiation, all officers will be elected, with terms concluding at the end of the second annual meeting. Upon completion of each term, the Vice Chair will assume the role of Chair, the Secretary will advance to Vice Chair, and a new Secretary will be elected by the project members.

Annual meetings will be held to share project progress, exchange findings, and foster collaboration among participants. Administrative guidance will be provided by an assigned Administrative Advisor and a NIFA Representative.

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