Precise Biophysical Mechanism of Plant–Human Electrical Communication

Determine the precise biophysical mechanism underlying plant–human electrical communication that produces the observed plant bioelectric responses to nearby humans, as documented in this study’s experiments with Ocimum basilicum and related setups measuring differential leaf–soil voltages and classifying human emotional states from mel-spectrograms using deep learning.

Background

Across multiple experiments from 2020–2025, the study reports that plants—especially Ocimum basilicum—exhibit measurable bioelectric responses correlated with human proximity, movement, voice, and emotional states. A ResNet50-based classifier achieved 97% accuracy in distinguishing human emotions from plant voltage spectrograms, and control models with shuffled labels performed near chance, suggesting genuine biological signals rather than artifacts.

To explain these findings, the authors propose an evolutionary early warning system hypothesis in which plants detect approaching herbivores before contact. However, the exact biophysical mechanism is unresolved. The discussion mentions candidate modalities such as bioelectric field detection from neural/muscular activity, detection of chemical gradients from respiration or skin emissions, thermal and airflow cues, and even potential quantum biological effects, underscoring the need for definitive mechanistic identification.