The University of Florida says researchers created a Strawberry Advisory System to help growers better understand when to spray fungicides.
The university said the system works with data generated by Florida Automated Weather Network stations near strawberry fields. The Strawberry Advisory System uses leaf wetness duration to help growers estimate the risk of their fruit getting infected with a fungal disease.
The research team including Won Suk “Daniel” Lee, a professor of agricultural and biological engineering; and Natalia Peres, a professor of plant pathology, published their research recently which shows how artificial intelligence can improve leaf wetness detection.
Continual moisture and temperatures higher than 65 degrees can indicate that diseases such as Botrytis and Anthracnose are possible, the university said. The Strawberry Advisory System’s algorithm uses images to detect wetness.
Researchers discovered nearly 96% of the time, the algorithm found moisture on the reference plate compared to manual observations, according to the university. The system also showed a nearly 84% accuracy rate when the research team compared it with current sensors and models.
“Ultimately, we want to replace the current wetness sensors with an imaging system because the current sensors are difficult to calibrate and not always reliable,” Lee said in a university blog post. “Using the AI system, we can detect wetness and consequently forecast the disease better, so we can help growers. The implementation of this advanced detection system within SAS may improve decisions about fungicide applications and may facilitate the implementation of leaf wetness detection for disease forecasting to other crop systems.”
