Teramoto Masaki, Shi Lin, Seimiya Naruhito, Takei Kuniharu
Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan.
ACS Sens. 2025 Aug 22;10(8):6195-6205. doi: 10.1021/acssensors.5c01912. Epub 2025 Aug 11.
Smart agriculture, which integrates sensing, robotics, and AI technologies, offers a promising paradigm to optimize plant growth and resource efficiency. However, current biological monitoring systems are often bulky, invasive, or require multiple discrete devices, which limits their applicability during early stage plant growth. This study proposes a fully integrated, multimodal flexible sensor system capable of minimally invasive and continuous monitoring of key physiological and environmental parameters, i.e., stem growth rate, soil moisture, and soil pH. A kirigami-structured ultraflexible strain sensor is directly attached to the plant stem to monitor elongation with minimal mechanical interference, achieving the Young's modulus of 5.2 kPa and a strain range up to 340%. An impedance-based soil water sensor inserted into the soil exhibited a sensitivity of 8.1%/% within the 20-30% soil water content range. A polyaniline-based electrochemical pH sensor embedded in the soil showed a sensitivity of 49.2 mV/pH, determined from potentials immediately after irrigation events. Unlike conventional approaches, the proposed system realizes accurate, real-time monitoring even during the most sensitive stages of plant development. Notably, the system achieved continuous simultaneous monitoring of all parameters over 80 h under realistic conditions in a controlled growth chamber. The simultaneous acquisition of stem-based growth metrics and soil environmental data allows for a comprehensive understanding of plant physiology and supports precision agriculture. This work provides a scalable and minimally invasive platform for next-generation ecosystem-driven plant monitoring, thereby paving the way for data-informed, energy-efficient crop management strategies in sustainable agriculture.
智能农业集成了传感、机器人和人工智能技术，为优化植物生长和资源利用效率提供了一种很有前景的模式。然而，当前的生物监测系统往往体积庞大、具有侵入性，或者需要多个离散设备，这限制了它们在植物生长早期阶段的适用性。本研究提出了一种完全集成的多模态柔性传感器系统，能够对关键生理和环境参数进行微创和连续监测，即茎生长速率、土壤湿度和土壤pH值。一种采用剪纸结构的超柔性应变传感器直接附着在植物茎上，以最小的机械干扰监测伸长情况，其杨氏模量为5.2 kPa，应变范围高达340%。插入土壤中的基于阻抗的土壤水分传感器在20 - 30%的土壤含水量范围内灵敏度为8.1%/%。嵌入土壤中的基于聚苯胺的电化学pH传感器在灌溉事件后立即根据电位测定，灵敏度为49.2 mV/pH。与传统方法不同，所提出的系统即使在植物发育最敏感阶段也能实现准确、实时监测。值得注意的是，该系统在可控生长室内的实际条件下，在80小时内实现了对所有参数的连续同步监测。同时获取基于茎的生长指标和土壤环境数据，有助于全面了解植物生理学，并支持精准农业。这项工作为下一代生态系统驱动的植物监测提供了一个可扩展的微创平台，从而为可持续农业中基于数据的节能作物管理策略铺平了道路。
