Plant Bioelectrical Signals for Environmental and Emotional State Classification
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biosensors-15-00744.pdf
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2.04 MB
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Adobe PDF
Checksum (MD5)
f5683d86d9e7aa35da5f45db3b489a5e
Author(s)
Gloor, Peter A.
Date Issued
November 5, 2025
Journal
Biosensors
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Gloor, P. A. (2025). Plant Bioelectrical Signals for Environmental and Emotional State Classification. Biosensors, 15(11), 744.
Version
Final published version
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.
MIT Department
System Design and Management Program.
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/bios15110744
