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Earlier detection of tar spot could save cornstalks

Earlier detection of tar spot could save cornstalks

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Tar spot on leaf

Tar spot can spread quickly, causing up to 50% decrease in crop yields. Researchers are working to more quickly predict and quantify the disease.

Dispatching unmanned-aerial vehicles equipped with remote-sensing technology and taking smartphone images on the ground, Michigan State University researchers are helping farmers more quickly predict and quantify tar spot in corn.

“The fungus that causes tar spot likes cool temperatures and wet leaves,” Addie Thompson, an assistant professor in the department of plant, soil and microbial sciences at Michigan State, said in a news release. “It can spread quickly, causing as much as a 50% decrease in crop yields.”

It was found for the first time in Midwest corn crops in 2015.

Thompson started her research by looking for corn crops that could be tar-spot resistant. She screened 800 different varieties in summer 2019.

Thompson and colleagues also conducted remote-sensing tests using unmanned-aerial vehicles equipped with spectral sensors to identify signs of tar spot over an entire cornfield. The images revealed changes within the corn plants.

Tar spot causes black lesions to form on the plants’ leaves. But before that happens, the fungus may cause biochemical changes within the crop that could be used for identifying the problem before symptoms are widespread. If left too long, severe tar spot can weaken the strength of the corn stalks, causing them to fall.

Thompson’s goal is to identify tar-spot disease early so farmers can examine specific areas before there’s damage to crop yields.

Thompson and her team have been taking smartphone images of leaves in more than 1,000 plots, along with disease-severity ratings. With that data the team is training computer models to automatically identify and quantify spots on leaves.

“I’d like to see this be transferable so that any farmer in any field using any phone can identify and quantify tar spot severity,” Thompson said.

“This technology also will be helpful for breeders to precisely assess experimental varieties in the field. In the future we hope to use imagery from

hyperspectral sensors to identify areas in the field with tar spot, potentially even before the black lesions are widely visible on the leaves.”

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