Imagine using an artificial intelligence (AI) platform similar to ChatGPT to get crop-damage information after a major storm and comparing it to previous seasons. That’s what University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) AI scientist Nikolaos Tziolas plans to provide for farmers.
His project will be funded with a new $297,000 grant from the U.S. Department of Agriculture National Institute of Food and Agriculture. The conversational AI system unlocks the value of satellite imagery by making it easily accessible and understandable to non-expert users, such as farmers and Extension agents, through a simple chat-based interface. The result will be a web-based platform that works with smartphones and computers, said Tziolas, a faculty member at the UF/IFAS Southwest Florida Research and Education Center.
Through this intuitive platform, users will easily find answers to basic questions, he said. For instance, they can identify flooded areas or compare crop health before and after a storm and receive accurate and timely insights. The system will enhance satellite imagery and provide highly detailed information for decision-making.
Tziolas compares the technology to ChatGPT, in which farmers can interact with an “AI assistant” that understands farming. Growers will eventually be able to use the technology to determine where storms damaged their crops and find the locations of the worst flooding.
“Imagine typing something like, ‘How much of my farm is flooded?,’ or ‘How did my crops do, compared to last year?’ and getting answers with maps and numbers tailored to your fields,” he said.
Extreme weather events, such as hurricanes, can severely disrupt agricultural systems, impacting food production and livelihoods, said Tziolas, an assistant professor of soil, water, and ecosystem sciences. For example, Florida experienced a credible range of losses between $190.4 million and $642.7 million in agricultural damage during last year’s Hurricane Milton.
Using current practices, growers go to their fields after a hurricane to check their crops, but this takes a lot of time. Some farmers also use drones to check larger areas faster, but that method costs a lot of money, and it only gives a snapshot of damage — not how things change over time.
“Traditional methods for assessing such damage are often slow, complex and expensive, limiting their effectiveness in time-sensitive disaster response efforts,” Tziolas said. “This project aims to address these challenges by developing an AI conversational platform that enables farmers, policymakers and other non-experts to assess crop damage and monitor recovery, using satellite data and artificial intelligence.”
“By making advanced technology accessible and actionable, this platform will help users reduce costs and improve resilience to extreme weather events in the future — by knowing where to plant,” he said.
Source: UF/IFAS
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