Mobile process sensor technology developed to identify plant diseases
North Carolina State University researchers have developed a mobile process sensor that uses analytics to identify plant diseases in the field by sampling airborne volatile organic compounds (VOCs) plants release through their leaves with a mobile instrumentation device.
Researchers at North Carolina State University have developed a mobile process sensor that allows farmers to identify plant diseases in the field. The handheld instrumentation device, which is plugged into a smartphone, uses analytics by sampling the airborne volatile organic compounds (VOCs) that plants release through their leaves.
“All plants release VOCs as they ‘breathe,’ but the type and concentration of those VOCs changes when a plant is diseased,” said Qingshan Wei, an assistant professor of chemical and biomolecular engineering. “Each disease has its own signature profile of VOCs. So, by measuring the type and concentration of VOCs being released by the plant, you can determine whether a plant is diseased and – if it is diseased – which disease it has.
“Our contribution here is the creation of a device that can be plugged into a smartphone and used to make those VOC measurements quickly in the field,” Wei said, who is also a faculty member in NC State’s Emerging Plant Disease and Global Food Security cluster.
Current disease identification techniques rely on molecular assays, which take hours to perform and – most importantly – have to be done in a lab. Getting a sample to the lab, where the sample may have to wait to be tested, can delay disease identification by days or weeks.
The handheld technology allows farmers to identify plant diseases in the field. Courtesy: Zheng Li, NC State University[/caption]
In proof-of-concept testing, the researchers demonstrated the device’s ability to detect and classify 10 plant VOCs down to the parts-per-million level. They were able to detect the late blight pathogen that caused the Irish famine two days after tomato plants were inoculated with the pathogen. Researchers could also distinguish tomato late blight from two other important fungal pathogens that produce similar symptoms on tomato leaves. In addition, the researchers showed they could detect the pathogen Phytophthora infestans in tomato leaves with greater than 95% accuracy.
While the proof of concept worked, the researchers aren’t done yet. “There are two areas where we could make it even better,” Wei said. “First, we would like to automate the pattern analysis using software for the smartphone, which would make it easier for farmers to make disease determinations.
“Second, we envision the development of customized reader strips that are designed to measure the VOCs associated with other diseases specific to a given crop. Different crops in different regions face different threats, and we could develop paper strips that are tailored to address those specific concerns.”
Matt Shipman, research communications lead, North Carolina State University. Edited by Chris Vavra, production editor, Control Engineering, CFE Media, email@example.com.
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