Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, USA.
Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695, USA.
Sci Adv. 2023 Apr 14;9(15):eade2232. doi: 10.1126/sciadv.ade2232. Epub 2023 Apr 12.
Wearable plant sensors hold tremendous potential for smart agriculture. 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 (VOCs), temperature, and humidity are integrated into a single platform. The abaxial leaf attachment position is selected on the basis of 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 as early as 4 days after inoculation. The model also evaluates different sensor combinations for early disease detection and predicts that minimally three sensors are required including the VOC sensors.
可穿戴植物传感器在智慧农业中具有巨大的潜力。我们报告了一种新型的下位叶附接式多模态可穿戴传感器，该传感器通过跟踪植物及其微环境的生化和生物物理信号，实现了对植物生理的连续监测。用于检测挥发性有机化合物(VOCs)、温度和湿度的传感器集成到单个平台上。根据气孔密度选择检测叶片的背面附着位置，以提高传感器信号强度。该多功能平台可实现各种胁迫监测应用，从跟踪植物水分流失到早期检测植物病原体。还开发了一种机器学习模型来分析多通道传感器数据，以便在接种后 4 天内尽早定量检测番茄斑萎病毒。该模型还评估了不同的传感器组合用于早期疾病检测，并预测至少需要三个传感器，包括 VOC 传感器。
