Toward Sustainable Crop Monitoring: An RGB-Based Non-Destructive System for Predicting Chlorophyll Content in Peanut Leaves

Accurate assessment of plant photosynthetic responses under drought and high-temperature stress is critical for understanding crop resilience. Chlorophyll content is a key indicator of photosynthetic efficiency, but conventional methods are destructive and time-consuming. Here, we developed a non-destructive detection system that captures Red (R), Green (G), and Blue (B) values from peanut (Arachis hypogaea L.) leaves and predicts chlorophyll content using machine learning. We optimized sensor distance (3–6 mm) and found 3 mm provided the most reliable RGB readings. Among Bayesian ridge and linear regression models, linear regression performed best (coefficient of determination R2 = 0.93), yielding a robust predictive formula: chlorophyll = [−0.0308 × [2 × G − R − B] + 4.386]. Integration of this formula into the detection system enabled real-time estimation of chlorophyll as a proxy for photosynthetic status and stress response. By enabling low-cost, non-destructive and rapid chlorophyll monitoring, this framework can help support resource-efficient crop monitoring and high-throughput screening for stress-resilient cultivars, with potential relevance to sustainable production in water-limited environments.