How AI Is Simplifying Hurricane Crop Damage Reports
Imagine using a generative artificial intelligence (AI) platform similar to ChatGPT to get crop damage information after a hurricane. That’s what University of Florida AI Scientist Nikolaos Tziolas plans to provide to farmers statewide.
With a new $297,000 grant from the USDA-National Institute of Food and Agriculture, Tziolas aims to develop an interactive tool for farmers to assess crop damage after major storms and compare it to previous seasons.
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, says 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 says. 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 says.
Extreme weather events, such as hurricanes, can severely disrupt agricultural systems, impacting food production and livelihoods, says 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.
For additional information on Tziolas’ project using AI to accurately assess crop damage after natural disasters, please read the original article written by Brad Buck and found on the University of Florida | Institute of Food and Agricultural Sciences website.
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