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Researcher uses automated imaging technology for fast detection of destructive soybean pest

Lei Zhang, assistant professor of plant nematology, is on the lookout for the soil-borne roundworm called soybean cyst nematode (SCN). Zhang can’t see the microscopic parasite with his eyes, but technology available in the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue might.

With grant support from the Indiana Soybean Alliance, Zhang and PhD student Vijay Kunwar, collaborating with the AAPF team and Jianxin Ma, professor of agronomy and Indiana Soybean Alliance Endowed Chair in Soybean Improvement, are working on developing methods for the early and fast detection of SCN infection of soybean roots using two advanced imaging technologies: hyperspectral imaging and chlorophyll fluorescence imaging.

The team’s efforts to find soybean root infections by SCN earlier and faster may help reduce this parasite’s devastating damage to soybean crops, contributing both to breeding resistance and better management options.

Nematodes comprise a large and diverse family of roundworms, some of which are parasitic in plants, animals and humans. “We are focusing on the plant-parasitic nematodes,” Zhang says. SCN, or Heterodera glycines, is one of over 4,100 such plant-parasitic nematode species. “The nematodes are very tiny; we cannot see them for most of their life stages,” he says.

This particular nematode, SCN, attacks the roots of soybeans. “SCN is the number-one pest of soybean in the U.S., causing soybean annual yield losses estimated at $1.5 billion,” Zhang says. Even without obvious aboveground symptoms, SCN could reduce soybean yields up to 30%. “If you see aboveground symptoms, there’s not much the growers can do during the growing season.”

The Zhang lab teamed with the Purdue Plant and Pest Diagnostic Lab, crop pathologist and associate professor of botany and plant pathology Darcy Telenko and soybean growers to survey 124 Indiana fields throughout the state. They found SCN in every soil sample in the survey in varying population densities.

Indiana soybean growers have planted soybean varieties with the same source of genetic resistance against SCN from the soybean Plant Introduction (PI) line PI 88788 for over 30 years, so the nematode has had plenty of time to evolve. The Indiana survey also showed that more than 80% of SCN populations tested can overcome this soybean resistance, Zhang says. “So there is a critical need to find new sources of resistance for breeding soybean with effective resistance to the virulent SCN populations.”

To achieve the goal, scientists need quicker and easier methods to evaluate nematode infection of soybean plants. The current method — extracting nematodes from the soil and soybean roots and counting them — is time-consuming and labor-intensive. In the lab, Zhang infects soybean plants by putting nematode eggs into the soil they’re growing in. He waits 30-35 days for the nematode to infect the plant and develop to adult stages, tracks its path from the roots and manually counts the number of parasites to quantify the level of infection.

“Basically, with automated phenotyping, we’re trying to make this a faster process, targeting the nematode infection and detecting it earlier, maybe in 10 to 14 days after nematode inoculation. The other thing is, the phenotyping is non-destructive,” he says. “We don’t have to take down the plant and count the nematodes.”

Nematode infection of roots can negatively impact the photosynthesis efficacy. Chlorophyll fluorescence imaging can be used to measure photosynthesis efficiency of soybean leaves. Hyperspectral imaging looks at wavelength patterns and possibly emissions associated with a nematode infection. “We’re using both to see which one works better,” Zhang says.

Instead of roots, the hyperspectral and fluorescence imaging methods focus on leaves. Even before root symptoms become obvious, the parasitic infection reduces photosynthesis. “This might be a subtle change — our eyes may not catch it — but using this kind of the hyperspectral imaging or fluorescence camera, we might be able to catch it,” Zhang says. “Based on leaf response, we try to tell if there is a nematode infection on soybean roots in the soil and the level of infection.”

Hyperspectral imaging in the phenomics center focuses on different wavelengths that somehow may be associated with a nematode infection. For example, the researchers are studying if leaf reflections at certain wavelength ranges of infected plants is higher or lower than control plants with no infection.

Zhang’s team might be able to reduce the time required for SCN detection to within 10 days of SCN inoculation in a controlled environment, so the project is still ongoing, he says. Still, the study shows promise for soybean resistance discovery and breeding purposes. Next steps include testing different levels of the nematode inoculums, different soybean varieties or breeding lines.

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