Artificial intelligence helps strawberry growers control disease

In Florida, strawberry growers have for many years been using the SAS (Strawberry Advisory System) designed by UF/IFAS (University of Florida Institute of Food and Agricultural Sciences) to tell them when to spray fungicides to prevent disease. 

 

SAS works with data generated by automatic stations installed near strawberry fields. The aim is to determine leaf wetness duration (LWD), a critical factor in assessing the risk of fungal diseases such as botrytis fruit rot and anthracnose. But these measurements come from sensors whose accuracy and reliability are limited, and whose calibration is difficult.

 

Scientists at UF/IFAS recently published the results of their research, which shows that artificial intelligence can improve leaf wetness detection (LWD). The innovative algorithm, using high-resolution imaging and deep learning technologies, had a 96% success rate in detection compared to manual observations, and an 84% accuracy rate compared to current SAS sensors and models.

 

These results will lead to the replacement of current humidity sensors by this imaging system, which will be integrated into SAS.