What happens when AI comes to the cotton fields

Precision agriculture uses tools and technologies such as GPS and sensors to monitor, measure and respond to changes within a farm field in real time. This includes using artificial intelligence technologies for tasks such as helping farmers apply pesticides only where and when they are needed.

However, precision agriculture has not been widely implemented in many rural areas of the United States.

We study smart communities, environmental health sciences and health policy and community health, and we participated in a research project on AI and pesticide use in a rural Georgia agricultural community.

Our team, led by Georgia Southern University and the City of Millen, with support from University of Georgia Cooperative Extension, local high schools and agriculture technology company FarmSense, is piloting AI-powered sensors to help cotton farmers optimize pesticide use. Georgia is one of the top cotton-producing states in the U.S., with cotton contributing nearly US$1 billion to the state’s economy in 2024. But only 13% of Georgia farmers use precision agriculture practices.

Public-private-academic partnership

Innovation drives economic growth, but access to it often stops at major city limits. Smaller and rural communities are frequently left out, lacking the funding, partnerships and technical resources that fuel progress elsewhere.

At the same time, 75% of generative AI’s projected economic impact is concentrated in customer operations, marketing, software engineering and research and development, according to a 2023 McKinsey report. In contrast, applications of AI that improve infrastructure, food systems, safety and health remain underexplored.

Yet smaller and rural communities are rich in potential — home to anchor institutions like small businesses, civic groups and schools that are deeply invested in their communities. And that potential could be tapped to develop AI applications that fall outside of traditional corporate domains.

The Partnership for Innovation, a coalition of people and organizations from academia, government and industry, helps bridge that gap. Since its launch almost five years ago, the Partnership for Innovation has supported 220 projects across Georgia, South Carolina, Kentucky, Tennessee, Virginia, Texas and Alabama, partnering with more than 300 communities on challenges from energy poverty to river safety.

One Partnership for Innovation program provides seed funding and technical support for community research teams. This support enables local problem-solving that strengthens both research scholarship and community outcomes. The program has recently focused on the role of civic artificial intelligence – AI that supports communities and local governments. Our project on cotton field pesticide use is part of this program.

Cotton pests and pesticides

Our project in Jenkins County, Georgia, is testing that potential. Jenkins County, with a population of around 8,700, is among the top 25 cotton-growing counties in the state. In 2024, approximately 1.1 million acres of land in Georgia were planted with cotton, and based on the 2022 agricultural county profiles census, Jenkins County ranked 173rd out of the 765 counties producing cotton in the United States.

The state benefits from fertile soils, a subtropical-to-temperate climate, and abundant natural resources, all of which support a thriving agricultural industry. But these same conditions also foster pests and diseases.

Farmers in Jenkins County, like many farmers, face numerous insect infestations, including stink bugs, cotton bollworms, corn earworms, tarnished plant bugs and aphids. Farmers make heavy use of pesticides. Without precise data on the bugs, farmers end up using more pesticides than they likely need, risking residents’ health and adding costs.

While there are some existing tools for integrated pest management, such as the Georgia Cotton Insect Advisor app, they are not widely adopted and are limited to certain bugs. Other methods, such as traditional manual scouting and using sticky traps, are labor-intensive and time-consuming, particularly in the hot summer climate.

Our research team set out to combine AI-based early pest detection methods with existing integrated pest management practices and the insect advisor app. The goal was to significantly improve pest detection, decrease pesticide exposure levels and reduce insecticide use on cotton farms in Jenkins County. The work compares different insect monitoring methods and assesses pesticide levels in both the fields and nearby semi-urban areas.

We selected eight large cotton fields operated by local farmers in Millen, four active and four control sites, to collect environmental samples before farmers began planting cotton and applying pesticides.

The team was aided by a new AI-based insect monitoring system called the FlightSensor by FarmSense. The system uses a machine learning algorithm that was trained to recognize the unique wingbeats of each pest insect species. The specialized trap is equipped with infrared optical sensors that project an invisible infrared light beam – called a light curtain – across the entrance of a triangular tunnel. A sensor monitors the light curtain and uses the machine learning algorithm to identify each pest species as insects fly into the trap.

FlightSensor provides information on the prevalence of targeted insects, giving farmers an alternative to traditional manual insect scouting. The information enables the farmers to adjust their pesticide-spraying frequency to match the need.

What we’ve learned

Here are three things we have learned so far:

1. Predictive pest control potential – AI tools can help farmers pinpoint exactly where pest outbreaks are likely – before they happen. That means they can treat only the areas that need it, saving time, labor and pesticide costs. It’s a shift from blanket spraying to precision farming – and it’s a skill farmers can use season after season.

2. Stronger decision-making for farmers – The preliminary results indicate that the proposed sensors can effectively monitor insect populations specific to cotton farms. Even after the sensors are gone, farmers who used them get better at spotting pests. That’s because the AI dashboards and mobile apps help them see how pest populations grow over time and respond to different field conditions. Researchers also have the ability to access this data remotely through satellite-based monitoring platforms on their computers, further enhancing the collaboration and learning.

3. Building local agtech talent – Training students and farmers on AI pest detection is doing more than protecting cotton crops. It’s building digital literacy, opening doors to agtech careers and preparing communities for future innovation. The same tools could help local governments manage mosquitoes and ticks and open up more agtech innovations.

Blueprint for rural innovation

By using AI to detect pests early and reduce pesticide use, the project aims to lower harmful residues in local soil and air while supporting more sustainable farming. This pilot project could be a blueprint for how rural communities use AI generally to boost agriculture, reduce public health risks and build local expertise.

Just as important, this work encourages more civic AI applications – grounded in real community needs – that others can adopt and adapt elsewhere. AI and innovation do not need to be urban or corporate to have a significant effect, nor do you need advanced technology degrees to be innovative. With the right partnerships, small towns, too, can harness innovations for economic and community growth.

This article is republished from The Conversation, a nonprofit, independent news organization bringing you facts and trustworthy analysis to help you make sense of our complex world. It was written by: Debra Lam, Georgia Institute of Technology; Atin Adhikari, Georgia Southern University, and James E. Thomas, Georgia Southern University

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The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.