Analysis of Plant Electrical Signals on an IoT Platform

The waves emitted by plants, also known as plant bioelectrical signals, are associated with electrophysiological phenomena within the plant. These signals can reflect the plant's state, health condition, and responses to environmental factors. This study integrates traditional sensors—such as temperature, humidity, light, sound, and imaging—with Galvanic Skin Response (GSR) to decode the significance of the plant's emitted frequencies. The research leverages an IoT-based data collection platform, analyzing the data to classify and evaluate these signals, thereby providing insights into the correlation between plantemitted frequencies and plant conditions. Future phases of this research will include the application of supervised ML algorithms, such as Random Forest or SVM, for automated classification of plant conditions based on GSR and environmental signals.