AI Technology on the Job To Help Squelch Downy Mildew in Watermelon
Attempting to stay ahead of problematic plant pathogens is always a challenge for farmers. Florida watermelon growers need all the help they can get to keep downy mildew in check. University of Florida researchers believe advanced ag tech tools aided by artificial intelligence can be a game changer in that difficult task.
In newly published research, Yiannis Ampatzidis, UF/IFAS Associate Professor of agricultural and biological engineering, used spectral reflectance — the energy a surface reflects at specific wavelengths — of plant canopies and machine learning to quickly and efficiently detect downy mildew in several stages of the disease.
“If left unchecked, downy mildew can destroy a farmer’s entire crop within days,” says Ampatzidis. “That’s why it gets the nickname ‘wildfire.’ It spreads rapidly and scorches leaves.”
Ampatzidis and his research team successfully detected downy mildew in several stages of severity. They developed two methods utilizing hyperspectral imaging and AI — one in the laboratory and the other using drones for field detection.
“Our most important result was finding downy mildew in its earliest stage, which is critical to growers’ ability to manage this disease,” he says.
Downy mildew does not affect stems or fruit directly. But it can defoliate the plants, leaving fruit exposed to sun damage, making it unmarketable.
For next steps in his research, Ampatzidis says he wants to develop a simple and inexpensive drone-based sensor to improve detection of downy mildew in watermelon plants.
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