When Plants Respond: Electrophysiology and Machine Learning for Green Monitoring Systems
🤖 AI Summary
To address the need for sustainable environmental monitoring in uncontrolled outdoor settings, this work proposes a biohybrid sensing paradigm leveraging electrophysiological signals from living plants. We introduce PhytoNode, a custom-designed plant-wearable device, to continuously acquire electrophysiological data from English ivy (*Hedera helix*) under real-world field conditions. An AutoML framework is integrated to automatically optimize model architecture and select the most discriminative statistical feature subset for real-time detection of environmental perturbations—such as abrupt changes in light intensity, temperature, and humidity. Over a five-month field deployment, our approach achieves a macro-F1 score of 95% on binary classification tasks, substantially outperforming conventional hand-crafted modeling approaches. This study represents the first deep integration of AutoML into a closed-loop plant electrophysiology analysis pipeline, establishing a novel pathway toward self-sustaining, ultra-low-power, and scalable ecological sensing systems.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Using plants as natural sensors for environmental monitoring
Mapping plant electrophysiology to outdoor conditions via machine learning
Developing biohybrid systems for sustainable plant-based monitoring
Innovation
Methods, ideas, or system contributions that make the work stand out.
Plant-wearable device records electrophysiological activity
AutoML analyzes plant data for environmental monitoring
Biohybrid system links plants with artificial devices
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