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

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Statement of Issues and Justification

The need as indicated by stakeholders

The competitive nature of modern agriculture demands agribusiness firms innovate and adapt quickly to capture benefits from advances in new technology such as artificial intelligence (AI). 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 the 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 in agriculture include crop production (e.g., yield prediction, pest and disease detection, harvesting, and phenotyping), livestock farming (e.g., behavior and health monitoring), postharvest processing (e.g., quality inspection, grading and sorting), and agricultural robotics (e.g., navigation, self-leaning, human-machine synergy, and optimization). Deep learning is a subset of machine learning and consists of deep neural networks to mimic human brain functions. Recently, deep learning has driven huge improvements in various computer vision problems, such as object detection, motion tracking, action recognition, pose estimation, and semantic segmentation (Voulodimos et al., 2018). 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. 

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. 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 climate environmental changes.

Robotics and visual machine learning platforms enable automation of critical labor activities such as field scouting, weeding, and harvesting. Pest control companies have begun using aerial drone technology to cut costs in the labor-intensive practice of scouting pests and diseases. 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, 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, 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 quality and consistence of products adelivered 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%.

 

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.

Machine learning 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 May-July 2021, 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, 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   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

Artificial intelligence (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, disease identification, and pest monitoring, 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, sorghum, soybean, corn, and rice. 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, blueberries, strawberries, peppers, tomatoes, apricots, and eggplant, offering substantial improvements in accuracy and efficiency over traditional methods.

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. Applications include lameness detection in dairy cattle, thermal comfort classification in pigs, aggression monitoring, and estrus identification. 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.

In addition, AI-driven sensing technologies provide nondestructive alternatives to conventional postharvest food quality evaluation methods. 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.

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. Vision-based control systems—particularly closed-loop visual servoing—enable precise fruit manipulation, though controller stability and robustness remain active research areas. 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. Deep learning has also shown strong performance in water quality monitoring and harmful algal bloom detection using remote sensing data, particularly when multimodal and spatiotemporal datasets are integrated through CNN–LSTM architectures.

 

AI for Plant Phenotyping and Genotyping

Deep CNNs have revolutionized image-based plant phenotyping, 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.

Objectives

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

Methods

Obj. 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 algorithms are capable of analyzing large volumes of diverse datasets to uncover complex patterns and relationships that are often challenging to capture through traditional methods, thereby enabling the development of data-driven solutions in agriculture. While some progress has been made in standardizing and centralizing datasets for specific tasks—such as crops/weeds imagery (Deng et al. 2024) and livestock datasets (Bhujel et al., 2025)—significant gaps remain in the availability of comprehensive, well-curated datasets that fully support AI development in agriculture. Effective coordination among land-grant universities will be essential to create standardized datasets in both crop and livestock production systems for the development of AI applications.

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. Such an effort is essential to maximizing the value of these datasets and accelerating AI innovation across institutions. However, the preparation of high-quality datasets is nontrivial, requiring substantial investments in data acquisition, categorization, annotation, standardization, and secure handling. 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 offers a powerful mechanism to overcome these challenges. Publicly available, standardized datasets can significantly reduce the duplication of efforts associated with data collection and curation, while enabling benchmarking, validation, and comparison of AI algorithms developed by different research groups. Within this framework, land-grant universities are well-positioned to serve as trusted gatekeepers of agricultural datasets, ensuring broad access to high-quality, multidimensional data from various sources. This role also allows participating institutions to share knowledge of successes and failures of AI-based methodologies, fostering transparency, reproducibility, and continuous methodological improvements. Developing these datasets also fosters interdisciplinary collaboration among computer engineers, data scientists, and statisticians, while also supporting industry and startups in utilizing the data to create AI solutions and deliver services to end-users.

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 Artificial Intelligence 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, 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

As agriculture becomes increasingly data-driven, the need for robust data governance frameworks that address ethics and privacy is more critical than ever. Agricultural data is often collected from farms using a range of technologies such as sensors, drones, satellite imagery, and IoT devices, and may include sensitive information about land use, production practices, environmental conditions, and even personal data about farmers and workers. Without clear policies and protections, this information can be misused, leading to concerns around data ownership, consent, transparency, and equity. Our idea to map the data supply chain from Objective 1 will help us understand the data flow from its source through various intermediaries to its final end-users, which will provide a foundation for the economic, legal, and ethical implications of agricultural data.

Traditional open data licenses are often inadequate for the agricultural data value chain due to their narrow scope and failure to address critical stakeholder concerns. Licenses like CC BY-SA (Creative Commons Attribution-ShareAlike), ODbL (Open Database License), and GODAN (Global Open Data for Agriculture & Nutrition) typically require derivative datasets to be shared under the same terms but exclude other data derivatives—such as analytical reports, proprietary databases, or predictive models—which are often the most commercially valuable components. This omission creates ethical risks, exemplified by technology companies profiting from software built with farmer-shared data without providing recognition or compensation, potentially disincentivizing participation. 

This complexity is compounded by the diverse and often conflicting perspectives of stakeholders across the expanded data life cycle. Federal agencies may suggest open access for publicly funded research data, while farmers, unaware of these policies, may view the same data as private property. Similarly, early publication of research data related to a new crop variety could undermine a breeder's patent claims and commercial return. Agricultural Technology Providers (ATPs) also hold disparate perspectives in rights of data with some startups recognizing farmers’ ownership while well-established tech giants taking more aggressive control by legal leverages. Such conflicts can be evidenced by the "right to repair" lawsuit against John Deere regarding its smart tractors. The company placed restrictions on farmers’ access to the underlying data and ability to repair their equipment, asserting ownership over the software and data. This forced farmers to rely on authorized dealers for repairs, raising costs and limiting autonomy. Several lawsuits have been filed to force John Deere to change this policy, but the issue remains unresolved. These disparities highlight that a one-size-fits-all license is insufficient.

Therefore, a more nuanced approach is essential. This begins with a comprehensive analysis of existing governance documents and stakeholder interests to identify potential conflicts. The system must then develop staged, flexible frameworks that range from fully open data (including commercial derivatives) to partially open tiers that may reserve commercial rights or require profit-sharing. Ultimately, the goal is to create actionable, ethically balanced agreements, crafted with legal expertise, that clearly specify rights, obligations, and benefits for each stakeholder, ensuring the system remains both accessible and equitable.

B. Detailed activities/procedures

The path of establishing the proposed open data system begins with a comprehensive discovery phase: 1. aggregating disparate data governance policies from all relevant federal agencies, 2. soliciting and analyzing industrial contracts from ATPs who involved in research projects by land grant universities for a preliminary understanding of industrial practices of data governance regarding various devices, services, and farming operations, and 3) conducting in-depth social research (e.g., surveys, interviews) to capture the nuanced perspectives, concerns, and incentives of farmers, researchers, and ATPs. This intelligence is then used to construct a map of the entire agricultural data value chain, explicitly delineating each stakeholder's role, responsibilities, data inputs/outputs, and economic interests. This mapping is not merely descriptive but analytical, serving to identify and diagnose points of potential conflict in data ownership, usage rights, and value distribution. The system then facilitates a structured stakeholder engagement process to negotiate compromises and co-create mutually acceptable data-sharing agreements. Consensus-driven principles will be created during this process to direct the development of robust, standardized template agreements and flexible, staged data protocols, ranging from fully open data for commercial use to restricted models allowing only non-commercial derivatives or requiring profit-sharing. These processes will be summarized into educational materials to be integrated into courses in educational institutes (e.g., land grant universities). Ultimately, the system provides dynamic guidance and tools, empowering future participants to navigate this complex landscape, anticipate challenges, and reliably build upon a sustainable and equitable open data ecosystem.

 

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 characteristics, 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).

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

Outcomes or Projected Impacts

Milestones

Projected Participation

View Appendix E: Participation

Outreach Plan

Organization/Governance

Literature Cited

Attachments

Land Grant Participating States/Institutions

Non Land Grant Participating States/Institutions