2023-04-12 ノースカロライナ州立大学(NCState)
実験では、このパッチは、トマトに感染したウイルスを検出し、病気の症状が現れるのを約1週間前に検出することができた。この技術は、農家が植物の病気を早期に発見し、病気の拡大を制限し、収穫物を保護することを可能にする。今後はパッチの無線化や野外でのテストが必要だという。
<関連情報>
- https://news.ncsu.edu/2023/04/plant-disease-detection-patch/
- https://www.science.org/doi/10.1126/sciadv.ade2232
植物生理の連続モニタリングのための軸葉表面装着型マルチモーダルウェアラブルセンサー Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring
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 and Qingshan Wei
Science Advances Published:12 Apr 2023
DOI:https://doi.org/10.1126/sciadv.ade2232
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.
