AI_Ampatzidis” shows Ampatzidis working in his lab. Courtesy, UF/IFAS photography.

Southern Scientists Use Artificial Intelligence To Reduce Costs, Labor On Farms

AI_Ampatzidis” shows Ampatzidis working in his lab. Courtesy, UF/IFAS photography.
AI_Ampatzidis” shows Ampatzidis working in his lab. Courtesy, UF/IFAS photography.

Scientists throughout the South are using artificial intelligence (AI) to help growers save labor costs and time, spray with precision, detect diseases, control food quality, maintain animal health and help grow wheat. 

University of Florida Institute of Food and Agricultural Sciences invention assesses plant stress from ground, air 

Among the University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) scientists helping growers save time and money is Yiannis Ampatzidis, an associate professor of agricultural and biological engineering. Ampatzidis invented Agroview, technology that uses images from drones, satellites and the ground.  

Read: Agricultural Safety Center, UF/IFAS Release Heat-Related Illness Toolkit

Agroview assesses plant stress, and it counts and categorizes plants based on their

height and canopy area. The technology also estimates plant-nutrient content. It can reduce data collection, analysis time and cost by up to 90% compared to the manual data collection, Ampatzidis said. 

“Growers in Florida and across the United States use this technology to predict yield, to detect stressed plant zones earlier and to develop maps for precision and variable-rate fertilizer applications,” said Ampatzidis, a faculty member at the Southwest Florida Research and Education Center. “The maps can help growers apply fertilizers optimally, reduce application cost and reduce environmental impact.” 

Removing bruised strawberries  

Another labor-saving use of AI comes from Ampatzidis and Won Suk “Daniel” Lee, a professor of agricultural and biological engineering on the UF main campus. Together, they’ve developed technology that distinguishes between ripe and bruised strawberries.  

Read: University Of Florida Scientists Use AI Algorithm To Improve Strawberry Disease Detection

Killing weeds, not crops 

Weeds can crowd out growing crops. But sometimes, when farmers spray herbicides or fungicides to kill the weeds, they end up damaging crops as well. That’s why UF/IFAS scientist Nathan Boyd devised a precision weed sprayer. 

Boyd, a professor of horticultural sciences at the UF/IFAS Gulf Coast Research and Education Center (GCREC), worked with Arnold Schumann, a professor of soil, water and ecosystem sciences at the Citrus Research and Education Center, to design the sprayer

Boyd uses images of weeds to train computers to identify them. Growers can use those pictures to know when, where and how to control pests. Using this form of precision agriculture, data from Boyd and Schumann have helped farmers reduce pesticide use by up to 90%. 

Read: 2024 Winner Of Agricultural-Environmental Leadership Award In Florida Announced

At Virginia Tech, researchers are also interested in identifying and vanquishing weeds without harming crops. 

U.S. growers spend approximately $6 to $8 billion annually using herbicides, in addition to rising labor costs for specialty crops.  

Vijay Singh, assistant professor in the College of Agriculture and Life Sciences and Virginia Cooperative Extension specialist at the Eastern Shore Agricultural Research and Extension Center, is leading a project to automate the process of drone-spray technology and machine learning to conduct real-time weed detection. The project aims to standardize the process and bring drone technology to the farmers’ field, saving time and money. 

“Testing of unmanned aerial systems is the first step, but the overall goal of these technologies is to automate the process and conduct real-time weed detection and spray applications, which we will achieve in the next few years,” Singh said. 

At Virginia Tech, scientists employ machine-learning and image-processing techniques that help growers effectively identify and distinguish between several types of weeds, map their precise locations and conduct real-time weed detection. 

University of Georgia’s College of Agricultural and Environmental Sciences develops fresh food tech with augmented reality  

Christopher Kucha, an assistant professor of food processing and engineering and head of the College of Agricultural and Environmental Sciences (CAES) Precision Food Systems Lab, uses precision sensing technologies and AI to create digital technologies including “digital twins” and augmented reality for food processing and quality control. 

A digital twin is a virtual model of a physical object, like a food item, that researchers can use to simulate its life cycle and assess production processes. 

“Amazon now uses augmented reality programs to show people what the products they are buying might look like in real life, and we are doing something somewhat similar with our digital twins,” Kucha said. “We collect data on a piece of food, its processing operations, distribution and shelf life and then develop the digital counterpart of those processes or products to optimize unit operations and quality monitoring in the supply chain.” 

Ebenezer Olaniyi, a graduate research assistant in Kucha’s lab, added that the imaging system should improve the sustainability of the food industry by setting standards for better, faster processes that cut down on costs and waste. 

Animal health management app at Fort Valley State University 

Small and limited-resource farmers in the southern United States and South Africa will soon have immediate access to their own personal veterinarian and agronomist with just a click of the finger. AI and precision agriculture are on the rise as scientists explore emerging technologies for farmers to save money and increase productivity. 

For that reason, researchers at Fort Valley State University (FVSU) in Georgia are using a $750,000 grant from the U.S. Department of Agriculture’s (USDA) National Institute of Food and Agriculture (NIFA) to develop a precision animal health management app.

The purpose of this innovative project is to use geographic information systems (GIS) technology and AI computer modeling to develop an automated, cell phone-based decision support system for farmers in the United States and South Africa to improve animal health in their small ruminants (sheep and goats). 

For example, a farmer can take pictures of an ailing goat’s eyes with a cell phone and then send the images through the downloaded free app. The farmer will receive immediate information on how to improve the goat’s health if it needs deworming. The research team found that many limited-resource farmers have access to mobile phone but may not have the funds or access to a veterinarian who is an expert in caring for small ruminants.  

Oklahoma State University improves crop production by embracing AI 

ExtensionBot, a chatbot app developed by Oklahoma State University (OSU) and the Extension Foundation, is currently in its pilot phase and will launch later this year.  

ExtensionBot will give the public unlimited access to information in all Extension areas, such as community health, family and consumer sciences, 4-H youth development, and agricultural and natural resources. Its narrative interface combined with AI technology improves accessibility and use of Extension content that currently exists online.  

Another OSU app in the beginning stages of development is BudgetBot, an AI-powered advisor that simplifies decision-making for underserved, small- and medium-sized agricultural producers by providing easy access to research-based information. Using advanced AI software, BudgetBot connects to structured and unstructured data sources, delivering clear, actionable insights in text and visual formats. Designed to overcome complex, hard-to-use farm budgeting solutions, the app offers real-time data on commodity prices, production inputs and performance metrics. 

Finally, researchers in the OSU Department of Plant and Soil Sciences are in the testing phase of a variety selection tool for wheat producers. Although such tools already exist, OSU researchers are throwing AI into the mix to increase the tool’s efficiency. Wheat producers can ask questions about specific production systems and manipulate wheat harvest data to illustrate production systems. 

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This article was written with contributions from Erin Yates, Latasha Ford, Alana Martin and Alisa Gore. 

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