United States: University of Florida uses AI to improve disease detection in strawberry crops

Published 2024년 9월 12일

Tridge summary

Researchers at the University of Florida have developed a system using artificial intelligence (AI) to improve the detection of leaf wetness on strawberry plants, which is crucial in preventing the spread of damaging diseases such as botrytis and anthracnose. The system, known as the Strawberry Advisory System (SAS), uses data from the Florida Automated Weather Network and identifies the risk of fungal diseases based on the duration of leaf wetness. The AI system was developed by Professors Natalia Peres and Won Suk “Daniel” Lee and has an accuracy rate of nearly 84%, nearly doubling that of the current SAS sensors and models. The study, which involved over 9,400 images, was conducted at various research centers and farms in Florida and showed nearly 96% accuracy in finding moisture on the reference plate compared to manual observations.

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Original content

Research published by University of Florida scientists shows that artificial intelligence (AI) can improve detection of leaf wetness on strawberry plants. Although the season for this popular fruit begins in December, UF/IFAS works year-round to find ways to control diseases that affect this crop. Continuous moisture and temperatures above 65°F combined give growers a signal of which damaging diseases, such as botrytis and anthracnose, are imminent. To help the state's growers, the University created the Strawberry Advisory System (SAS), a tool that works with data generated by Florida Automated Weather Network stations near growing areas and identifies the duration of leaf wetness to calculate the risk of their fruit becoming infected with a fungal disease. Growers use the tool to know when they should spray fungicides to prevent plant diseases. Professor of Plant Pathology Natalia Peres and Professor of Agricultural and Biological Engineering Won Suk “Daniel” Lee developed a ...

Source: MXfruit