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

Objectives

Methods

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