UF Scientists Use AI Algorithm To Improve Strawberry Disease Detection

UF/IFAS scientists search for ways to help growers control diseases that can damage strawberries. For over a decade, Florida farmers have used the UF/IFAS-designed Strawberry Advisory System (SAS) to tell them when to spray fungicides to prevent plant diseases.

SAS works with data generated by Florida Automated Weather Network stations near farms – in this case, near strawberry fields. SAS uses leaf wetness duration to help growers estimate the risk of their fruit getting infected with a fungal disease.

In a published research, Won Suk “Daniel” Lee, a professor of agricultural and biological engineering and Natalia Peres, a professor of plant pathology, show how artificial intelligence (AI) can improve leaf wetness detection. A system developed by the researchers took photos of a reference plate, which detects water more directly than the method currently used in SAS.

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