Giwon Lee from North Carolina State University and colleagues have now developed a wearable plant sensor patch for continuous plant-health monitoring with improved sensitivity, selectivity and environmental stability. The team uses newly developed materials including volatile organic compound (VOC)-sensing materials, which can detect plant VOCs in real time, as well as gold-coated silver nanowires (Au@AgNWs) that show high stability against humidity and solvent exposure. The multimodal wearable sensor incorporates sensors for VOCs, temperature and humidity, enabling biochemical and biophysical signals to be measured simultaneously. The real-time sensor data were processed by an unsupervised machine learning framework, which can quantitatively differentiate diseased plants from healthy plants and identify the best sensor combinations to reduce the sensor cost. Under various abiotic stresses such as drought, overwatering, salinity and darkness, the sensor patch can detect early stress-response signals. The wearable sensor patch was tested by monitoring tomato spotted wilt virus (TSWV) and early blight (Alternaria linariae) on live tomato plants. In the growth chamber, the wearable sensor patch detects pathogen infection signals earlier than conventional molecular diagnostic methods or visual assessment techniques.
Global climate change impacts the occurrence of pathogens in agricultural and natural ecosystems, increasing the risk of exposure to new pests and pathogens. Precision agricultural technologies offer more efficient ways for crop disease detection and management while reducing negative environmental effects. Plant wearable sensors are one of the emerging technologies that can monitor plant-health conditions in real time and help detect plant diseases at an early stage. This study presents a versatile data-analytics-coupled sensor platform that simultaneously monitors multiple biochemical and biophysical signals and has the potential to be used for field applications for plant-health monitoring.
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