Plant Bioelectrical Signals for Environmental and Emotional State Classification

TL;DR

This study explores using bioelectrical signals from a plant to detect environmental changes and human emotions, achieving moderate accuracy with a single plant setup.

Contribution

The novel use of plant bioelectrical signals for dual classification of environmental and emotional states is presented.

Findings

Lamp on/off detection achieved 85.4% accuracy using a CNN model.

Emotion classification reached 73% accuracy for happy and sad states.

Results are preliminary and require replication for robustness.

Abstract

In this study, we present a pilot investigation using a single Purple Heart plant (Tradescantia pallida) to explore whether bioelectrical signals for dual-purpose classification tasks: environmental state detection and human emotion recognition. Using an AD8232 ECG sensor at 400 Hz sampling rate, we recorded 3 s bioelectrical signal segments with 1 s overlap, converting them to mel-spectrograms for ResNet18 CNN (Convolutional Neural Network) classification. For lamp on/off detection, we achieved 85.4% accuracy with balanced precision (0.85–0.86) and recall (0.84–0.86) metrics across 2767 spectrogram samples. For human emotion classification, our system achieved optimal performance at 73% accuracy with 1 s lag, distinguishing between happy and sad emotional states across 1619 samples. These results should be viewed as preliminary and exploratory, demonstrating feasibility rather than definitive evidence of plant-based emotion sensing. Replication across plants, days, and experimental sites will be essential to establish robustness. The current study is limited by a single-plant setup, modest sample size, and reliance on human face-tracking labels, which together preclude strong claims about generalizability.

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