FolioClip: Comprehensive Plant Health Monitoring and Early Stress Detection with Real-Time Multimodal Wearable Sensing and Online Machine Learning

Abstract

Wearable plant sensing systems for simultaneous biochemical and physiological monitoring with real-time multimodal data analysis remain limited. Here, we present FolioClip, a multimodal wearable patch that continuously monitors leaf temperature, humidity, light, CO 2 , and three volatile organic compounds (VOCs) with high selectivity. Its bookmark-inspired design enables secure attachment to plant leaves of diverse morphologies and it integrates a flexible printed circuit board for wireless data transmission. We also develop FolioOmni, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection. The integrated FolioClip–FolioOmni platform detects and classifies nine stresses, including light, water, CO 2 , mechanical cut, P. infestans , and A. alternata , in tomato plants with 92% accuracy. Notably, P. infestans on tomato was detected within 15.5 h post-inoculation, earlier than quantitative polymerase chain reaction (qPCR) (~4 days) and visual phenotyping (~7 days), highlighting the potential of integrating multimodal wearable sensing and online ML for precision agriculture.

Plant phenotyping relevance

葉の生理・健康状態とストレスを測定するウェアラブルセンシング装置および機械学習解析基盤を開発し、複数ストレスで性能評価しているため、植物フェノタイピング手法が中心である。

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