Electrophysiology-based model for optimizing plant lighting regimes and yield

Overview

This solution applies electrophysiology—the measurement of electrical activity in living plants—to build predictive models that link lighting conditions to plant development. Illumination is a critical driver of plant growth, and growers know that optimal photoperiod, spectrum, and light intensity vary across cultivation stages. By recording and analyzing the electrical signals plants emit in response to light, this approach enables earlier and more precise detection of plant status than visual observation alone. The ultimate goal is to translate these signals into actionable lighting recommendations that maximize plant development and yield.

The platform serves greenhouse operators, indoor farms, perennial crop growers, and partners in crop treatment, biostimulant development, and plant breeding. By enabling real-time, continuous monitoring of crop health, it supports more efficient use of resources, higher yields, and reduced environmental impact.

Technical specifications

• Signal acquisition: Plant electrical signals recorded at 400 Hz sampling rate across different light-dark cycles (12/12 and 8/16 tested).
• Feature extraction: 26 features calculated per signal window (including min, max, amplitude, and additional micro and macro characteristics) across window sizes ranging from 15 seconds to 30 minutes.
• Normalization: Per-plant feature normalization compensates for natural inter-plant variability.
• Dataset scale: Initial validation produced 15,781 samples from 8 tomato plants monitored for one week under controlled phytotron conditions.
• Analysis approach: Comparative feature analysis distinguishes plant responses between lighting regimes; future work will use regression analysis to identify optimal electrophysiological features correlated with lighting.
• Supplementary measurements: Future validation will incorporate plant height, leaf count, leaf area, and chlorophyll fluorescence (PAM) alongside electrical signals for multi-modal correlation.

Technology readiness level

The technology has completed initial proof-of-concept validation in a controlled phytotron environment, demonstrating that electrophysiological signals can reliably distinguish plant responses to different light-dark cycles. The next phase of validation is planned with 48 tomato seedlings (16 per group across three light intensity levels: 25%, 100%, and 400% of typical greenhouse intensity) monitored over four weeks under conditions approaching real greenhouse settings. This expanded study will refine the model and identify the most predictive signal features for lighting optimization.