Kim Dongpil, Zarei Mohammad, Lee Siyoung, Lee Hansol, Lee Giwon, Lee Seung Goo
Department of Horticultural Science, Chungnam National University, Daejeon, 34134, Republic of Korea.
Department of Chemistry, University of Ulsan, Ulsan, 44610, Republic of Korea.
Adv Sci (Weinh). 2025 Apr;12(16):e2414748. doi: 10.1002/advs.202414748. Epub 2025 Mar 24.
Monitoring crops' biotic and abiotic responses through sensors is crucial for conserving resources and maintaining crop production. Existing sensors often have technical limitations, measuring only specific parameters with limited reliability and spatial or temporal resolution. Wearable sensing systems are emerging as viable alternatives for plant health monitoring. These systems employ flexible materials attached to the plant body to detect nonchemical (mechanical and optical) and chemical parameters, including transpiration, plant growth, and volatile organic compounds, alongside microclimate factors like surface temperature and humidity. In smart farming, data from real-time monitoring using these sensors, integrated with Internet of Things technologies, can enhance crop production efficiency by supporting growth environment optimization and pest and disease management. This study examines the core components of wearable standalone systems, such as sensors, circuits, and power sources, and reviews their specific sensing targets and operational principles. It further discusses wearable sensors for plant physiology and metabolite monitoring, affordability, and machine learning techniques for analyzing multimodal sensor data. By summarizing these aspects, this study aims to advance the understanding and development of wearable sensing systems for sustainable agriculture.
通过传感器监测作物的生物和非生物反应对于节约资源和维持作物产量至关重要。现有传感器往往存在技术局限性，仅能测量特定参数，可靠性以及空间或时间分辨率有限。可穿戴传感系统正成为植物健康监测的可行替代方案。这些系统采用附着在植物主体上的柔性材料来检测非化学（机械和光学）和化学参数，包括蒸腾作用、植物生长和挥发性有机化合物，以及诸如表面温度和湿度等微气候因素。在智能农业中，使用这些传感器进行实时监测的数据与物联网技术相结合，可以通过支持生长环境优化以及病虫害管理来提高作物生产效率。本研究考察了可穿戴独立系统的核心组件，如传感器、电路和电源，并综述了它们的特定传感目标和工作原理。它还讨论了用于植物生理和代谢物监测的可穿戴传感器、可承受性以及用于分析多模态传感器数据的机器学习技术。通过总结这些方面，本研究旨在推动对可持续农业可穿戴传感系统的理解和发展。
