Researchers from North Carolina State University have developed an electronic patch that can be applied to the leaves of plants to monitor crops for different pathogens – such as viral and fungal infections – and stresses such as drought or salinity. In testing, the researchers found the patch was able to detect a viral infection in tomatoes more than a week before growers would be able to detect any visible symptoms of disease.
“This is important because the earlier growers can identify plant diseases or fungal infections, the better able they will be to limit the spread of the disease and preserve their crop,” says Qingshan Wei, corresponding author of a paper on the work and an assistant professor of chemical and biomolecular engineering at NC State.
“In addition, the more quickly growers can identify abiotic stresses, such as irrigation water contaminated by saltwater intrusion, the better able they will be to address relevant challenges and improve crop yield.”
The technology builds on a previous prototype patch, which detected plant disease by monitoring volatile organic compounds (VOCs) emitted by plants. Plants emit different combinations of VOCs under different circumstances. By targeting VOCs that are relevant to specific diseases or plant stress, the sensors can alert users to specific problems.
“The new patches incorporate additional sensors, allowing them to monitor temperature, environmental humidity, and the amount of moisture being ‘exhaled’ by the plants via their leaves,” says Yong Zhu, co-corresponding author of the paper and Andrew A. Adams Distinguished Professor of Mechanical and Aerospace Engineering at NC State.
The patches themselves are small – only 30 millimeters long – and consist of a flexible material containing sensors and silver nanowire-based electrodes. The patches are placed on the underside of leaves, which have a higher density of stomata – the pores that allow the plant to “breathe” by exchanging gases with the environment.
The researchers tested the new patches on tomato plants in greenhouses, and experimented with patches that incorporated different combinations of sensors. The tomato plants were infected with three different pathogens: tomato spotted wilt virus (TSWV); early blight, which is a fungal infection; and late blight, which is a type of pathogen called an oomycete. The plants were also exposed to a variety of abiotic stresses, such as overwatering, drought conditions, lack of light, and high salt concentrations in the water.
The researchers took data from these experiments and plugged them into an artificial intelligence program to determine which combinations of sensors worked most effectively to identify both disease and abiotic stress.
“Our results for detecting all of these challenges were promising across the board,” Wei says. “For example, we found that using a combination of three sensors on a patch, we were able to detect TSWV four days after the plants were first infected. This is a significant advantage, since tomatoes don’t normally begin to show any physical symptoms of TSWV for 10-14 days.”
The researchers say they are two steps away from having a patch that growers can use. First, they need to make the patches wireless – a relatively simple challenge. Second, they need to test the patches in the field, outside of greenhouses, to ensure the patches will work under real-world conditions.
“We’re currently looking for industry and agriculture partners to help us move forward with developing and testing this technology,” Zhu says. “This could be a significant advance to help growers prevent small problems from becoming big ones, and help us address food security challenges in a meaningful way.”
The paper, “Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring,” is published in the open-access journal Science Advances. First author of the paper is Giwon Lee, a former postdoctoral researcher at NC State, now on faculty at Kwangwoon University in South Korea. The paper was co-authored by Tatsiana Shymanovich, a postdoctoral researcher at NC State; Oindrila Hossain, Sina Jamalzadegan, Yuxuan Liu and Hongyu Wang, who are Ph.D. students at NC State; Amanda Saville, a research technician at NC State; Rajesh Paul, a former Ph.D. student at NC State; Dorith Rotenberg and Anna Whitfield, who are both professors of entomology and plant pathology at NC State; and Jean Ristaino, William Neal Reynolds Distinguished Professor of Entomology and Plant Pathology at NC State.
The work stems from the Emerging Plant Disease and Global Food Security research cluster at NC State. This interdisciplinary program is focused on developing new knowledge and tools to better understand and counter emerging infectious plant diseases.
The work was done with support from the U.S. Department of Agriculture under grant number 2019-67030-29311 and USDA APHIS Farm Bill grant number 3.0096; and from the National Science Foundation, under grant numbers 1728370 and 2134664.
Note to Editors: The study abstract follows.
“Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring”
Authors: Giwon Lee, Oindrila Hossain, Sina Jamalzadegan, Yuxuan Liu, Hongyu Wang, Amanda C. Saville, Tatsiana Shymanovich, Rajesh Paul, Dorith Rotenberg, Anna E. Whitfield, Jean B. Ristaino, Yong Zhu and Qingshan Wei, North Carolina State University
Published: April 12, Science Advances
Abstract: Wearable plant sensors hold tremendous potential for smart agriculture. Here, we report a lower leaf surface-attached multimodal wearable sensor for continuous monitoring of plant physiology by tracking both biochemical and biophysical signals of the plant and its microenvironment. Sensors for detecting volatile organic compounds (VOC), temperature, and humidity are integrated into a single platform. The abaxial leaf attachment position is selected based on the stomata density to improve the sensor signal strength. This versatile platform enables various stress monitoring applications, ranging from tracking plant water loss to early detection of plant pathogens. A machine learning model was also developed to analyze multichannel sensor data for quantitative detection of tomato spotted wilt virus (TSWV) as early as four days after inoculation. The model also evaluates different sensor combinations for early disease detection, and predicts minimally three sensors are required including the VOC sensors.
This post was originally published in NC State News.