Plant Bioelectrical Signals for Environmental and Emotional State Classification

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.

Plant phenotyping relevance

植物の生体電気信号をセンサーで取得し、スペクトログラムとCNNで環境状態を分類する手法を開発・評価しており、植物の生理状態に基づく表現型取得が中心です。ただし、人間の感情分類は植物表現型ではありません。

Code and data availability

The paper's Data Availability Statement provides explicit public URLs for both the phenotype/bioelectrical signal dataset (figshare project) and the authors' analysis code (GitHub), directly reproducing this paper's plant-phenotyping measurements and computational analysis.