by Precision agriculture uses tools and technologies such as GPS and sensors to monitor, measure and react to them in real time within an agricultural area. This includes the use of artificial intelligence technologies for tasks such as
However, precision agriculture was not widespread in many rural areas of the United States.
We study intelligent communities, environmental health, health policy and health of the community and took part in a research project on AI and Pesticide consumption in a rural agricultural community in Georgia.
Our team, led by Georgia Southern University and the city of Millen, with the support of the University of Georgia, the local high school and the farm technology company Farmensense, style of AI-driven to optimize the use of pesticides from cotton farmers. Georgia is one of the best cotton states in the United States, with cotton in 2024 contributing almost $ 1 billion to the state’s economy.
Public-private academic partnership
Innovation promotes economic growth, but access to this often ends in large city limits. Smaller and rural communities are often left out without financing, partnerships and technical resources that promote progress elsewhere.
At the same time, 75% of the projected economic effects of generative AI focus on customer operation, marketing, software engineering as well as research and development, as can be seen from a McKinsey report from 2023. In contrast, applications of AI that improve the infrastructure, food systems, security and health.
But smaller and rural communities are rich in potential – the home of anchoring institutions such as small companies, citizens’ groups and schools that are deeply invested in their communities. And this potential could be equipped to develop AI applications that are outside of traditional business areas.
The partnership for innovation, a coalition of people and organizations from science, the government and industry, helps to close this gap. Since its introduction almost five years ago, the partnership for innovation has supported 220 projects in Georgia, South Carolina, Kentucky, Tennessee, Virginia, Texas and Alabama, which have worked with more than 300 communities to challenges from energy poverty to the safety of river.
A partnership for innovation program offers seed financing and technical support for community research teams. This support enables local problem solving, which strengthens both research grants and community results. The program recently focused on the role of artificial intelligence of bourgeois intelligence – AI that supports communities and local governments. Our project for the use of pesticides in Kaumwollfeld is part of this program.
Cotton pests and pesticides
Our project in Jenkins County, Georgia, tests this potential. With around 8,700 population, Jenkins County is one of the 25 most important district countries in the state. In 2024, around 1.1 million hectares of land with cotton were planted in Georgia, and, based on the census of 2022 Agricultural County profiles, Jenkins County took 173rd place from the 765 counties that produced cotton in the USA.
Cotton is an important part of the Georgia’s agricultural industry. Daeshjea McGee
The state benefits from fertile soils, a subtropical to temperative climate and plenty of natural resources, all of which support a flourishing agricultural industry. The same conditions also promote pests and diseases.
The farmers in Jenkins County look like many farmers with numerous insect infestation, including stink bobs, cotton bollow, corn earworms, cloudy plant bugs and aphids. The farmers use pesticides strongly. Without precise data on the errors, farmers use more pesticides than likely what risk the health of the residents and add costs.
There are some tools for integrated pest management, such as the Georgia Cotton Insect Advisor app, are not widespread and are limited to certain errors. Other methods such as traditional manual scouting and the use of adhesive are labor -intensive and time -consuming, especially in the hot summer climate.
Our research team faced the methods of recognizing AI-based early pests with existing integrated pest management practices and the insect advice app. The aim was to significantly improve pest detection, reduce the level of pesticide and reduce insecticide consumption on cotton companies in Jenkins County. The work compares various methods for monitoring insects and evaluates the pesticide level in both the areas and near the semi-city areas.
We have selected eight large cotton fields that were operated by local farmers in Millen, four active and four control locations to collect environmental samples before the farmers began planting cotton and applying pesticides.
Pest insects are identified by AI if they fly through a light sensor in this case. Daeshjea McGee
The team was supported by a new AI-based insect monitoring system called Farmsensor from Farmensense. The system uses an algorithm for machine learning, which has been trained in order to recognize the unique wing beats of every type of pest. The specialized trap is equipped with optical infrared sensors that project an invisible infrared light beam – referred to as a light curtain – through the receipt of a triangular tunnel. A sensor monitors the light curtain and uses the algorithm for machine learning to identify any pest species as insects.
Flight sensor provides information on the prevalence of targeted insects and offers farmers an alternative to traditional manual insects scouting. The information enables farmers to adapt their pesticide frequency so that they meet the needs.
What we learned
Here are three things that we have learned so far:
1. Potential for the prediction for pest control – AI tools can help farmers to determine exactly where the pest outbreaks are likely – before they take place. This means that you can only treat the areas you need and save time, work and pesticide costs. It is a shift from ceiling sprays to precision breeding – and it is a skill that farmers can use after the season.
2. Stronger decision -making for farmers – The preliminary results show that the proposed sensors can effectively monitor insect populations that are specific for cotton companies. Even after the sensors have disappeared, farmers who use them can better recognize the pests. This is because the AI dashboards and mobile apps help you to see how the pest population grow over time and react to different field conditions. Researchers can also access this data via satellite -based monitoring platforms on their computers in order to further improve cooperation and learning.
3 .. Build local AGTech talent – The training of students and farmers for KI peculiar detection protects more than just cotton plants. It builds digital competence, opens doors for Agtech’s career and the preparation of the communities for future innovations. The same tools could help the local governments to manage mosquitoes and ticks and open up more AGTech innovations.
Blueprint for rural innovations
By using AI for the early detection of early detection and reduction in the use of the pesticide, the project is aimed at reducing the harmful residues in local soil and air and at the same time supporting more sustainable agriculture. This pilot project could be a blueprint for how rural communities use AI in general to promote agriculture, reduce the risks of public health and to build local specialist knowledge.
It is also important that this work promotes more bourgeois AI applications – which is based on real needs of the community, which others can take over and adapt elsewhere. AI and innovation do not have to be urban or companies to have a significant effect, nor do they need advanced technology degrees to be innovative. With the right partnerships, small cities can also use innovations for economic and community growth.
This article will be released from the conversation, a non -profit, independent news organization that brings you facts and trustworthy analyzes to help you understand our complex world. It was written by: debra lam, Georgia Institute of Technology; Atin Adhikari, Georgia Southern Universityand James E. Thomas, Georgia Southern University
Read more:
The authors do not work for a company or an organization that benefits from this article and have not published any relevant affiliations about their academic appointment.
