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

Abstract

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.

Plant phenotyping relevance

ピーナッツ葉のRGBセンサーと機械学習によるクロロフィル含量の非破壊推定システムを開発・最適化・検証しており、植物形質取得手法が研究の中心である。

Code and data availability

The article describes a custom Arduino/GY-33 RGB chlorophyll detection system, 147 leaf RGB/chlorophyll measurements, and scikit-learn regression modeling, but no public phenotype dataset, image/sensor data deposit, author analysis code, or trained model checkpoint is disclosed. Supplementary materials (Tables S1–S3, S

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