Artificial Intelligence: Helps Detect Watermelon Disease Quickly, Accurately

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The needs of Florida watermelon producers motivate Yiannis Ampatzidis to use artificial intelligence (AI) to detect pathogens early and accurately.

Artificial Intelligence

One such disease, downy mildew, spreads like wildfire, said Ampatzidis, a University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) associate professor of agricultural and biological engineering.

In newly published research, Ampatzidis used spectral reflectance —  the energy a surface reflects at specific wavelengths — of plant canopies and machine learning to quickly and efficiently detect downy mildew throughout multiple stages of the disease.

Hopefully, farmers can take advantage of this technology.

“If left unchecked, downy mildew can destroy a farmer’s entire crop within days. That’s why it gets the nickname ‘wildfire.’ It spreads rapidly and scorches leaves,” said Ampatzidis, a faculty member at the Southwest Florida Research and Education Center.

Ampatzidis and his research team successfully detected downy mildew in several stages of severity.

“Our most important result was finding downy mildew in its earliest stage, which is critical to growers’ ability to manage this disease,” he said.

Ampatzidis and his research team developed two methods, utilizing hyperspectral imaging and AI — one in the laboratory and the other using UAVs (drones) for field detection.

Downy mildew does not affect stems or fruit directly. But it can defoliate the plants, leaving fruit exposed to sun damage, rendering it unmarketable. That’s critical because, as of 2019, Florida farmers harvested watermelon from 24,500 acres in Florida, with a packinghouse value of $161 million.

As long as consumers continue to buy watermelon — and those who grow the fruit want to reap a good harvest — Ampatzidis will continue to find ways to find pathogens that could damage the fruit.

As next steps in his Artificial Intelligence research, Ampatzidis wants to develop a simple and inexpensive drone-based sensor to improve detection of downy mildew in watermelon plants.

Source: UF/IFAS News