Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring

Abstract

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.

Plant phenotyping relevance

植物の生理状態・病害状態を連続取得するウェアラブル多モーダルセンサープラットフォームの開発が中心であり、機械学習による病害の定量検出も含むため。

Code and data availability

The supplied article blocks describe a multimodal wearable plant sensor study with machine learning (PCA) analysis of sensor data, but contain no public phenotype/trait dataset, sensor data deposit, author analysis code, or trained model with an availability statement or public URL. The only URLs present are author ORC

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