可穿戴计算机
持续监测
计算机科学
跟踪(教育)
生物系统
实时计算
环境科学
遥感
生物
嵌入式系统
工程类
心理学
教育学
运营管理
地质学
作者
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,Qingshan Wei
出处
期刊:Science Advances
[American Association for the Advancement of Science (AAAS)]
日期:2023-04-12
卷期号:9 (15)
被引量:42
标识
DOI:10.1126/sciadv.ade2232
摘要
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.
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