用植物电生理信号实现户外环境智能监测
When Plants Respond: Electrophysiology and Machine Learning for Green Monitoring Systems
- 给常春藤佩戴可穿戴设备，实时采集电生理数据
- 五个月实测中分类准确率达95%，自动机器学习效果更优
- 适合生态监测、智慧农业等可持续场景应用
活体植物不仅维持生态平衡，还能作为自然传感器，传递其内部生理状态和周围环境信息。通过将植物与人工装置集成，构建新型生物-混合系统以实现生理信号双向流动。我们为常春藤（Hedera helix）配备名为PhytoNode的可穿戴设备，持续记录其电生理活动，并在非受控户外环境中建立电生理模式与环境条件的映射关系。历时五个月的数据收集后，采用先进的自动化机器学习（AutoML）方法进行分析。分类模型表现优异，在二分类任务中最高达到95%的宏观F1分数。相比人工调参，AutoML更具优势，且选择统计特征子集进一步提升了精度。该生物混合系统成功实现了在恶劣真实环境下的植物电生理长期监测，推动了可扩展、自维持且植株融合的可持续环境监测系统发展。
原文摘要 · Abstract (English)
Living plants, while contributing to ecological balance and climate regulation, also function as natural sensors capable of transmitting information about their internal physiological states and surrounding conditions. This rich source of data provides potential for applications in environmental monitoring and precision agriculture. With integration into biohybrid systems, we establish novel channels of physiological signal flow between living plants and artificial devices. We equipped *Hedera helix* with a plant-wearable device called PhytoNode to continuously record the plant's electrophysiological activity. We deployed plants in an uncontrolled outdoor environment to map electrophysiological patterns to environmental conditions. Over five months, we collected data that we analyzed using state-of-the-art and automated machine learning (AutoML). Our classification models achieve high performance, reaching macro F1 scores of up to 95 percent in binary tasks. AutoML approaches outperformed manual tuning, and selecting subsets of statistical features further improved accuracy. Our biohybrid living system monitors the electrophysiology of plants in harsh, real-world conditions. This work advances scalable, self-sustaining, and plant-integrated living biohybrid systems for sustainable environmental monitoring.
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