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

Ge, Kui; Li, Huan; Fan, Xinqi; Wang, Yixuan; Zhao, Juan; Huang, Jiatong; Tian, Changcheng

doi:10.3390/su18021001

Open AccessArticle

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

by

Kui Ge

^*,

Huan Li

,

Xinqi Fan

,

Yixuan Wang

,

Juan Zhao

,

Jiatong Huang

and

Changcheng Tian

^*

School of Food and Biological Engineering, Bengbu University, Bengbu 233030, China

^*

Authors to whom correspondence should be addressed.

Sustainability 2026, 18(2), 1001; https://doi.org/10.3390/su18021001

Submission received: 19 November 2025 / Revised: 12 January 2026 / Accepted: 16 January 2026 / Published: 19 January 2026

(This article belongs to the Special Issue Adoption of New Technologies and Practices for Sustainable and Smart Agriculture)

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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.

Keywords: peanut; plant stress; color sensing; resource-efficient monitoring; chlorophyll prediction

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MDPI and ACS Style

Ge, K.; Li, H.; Fan, X.; Wang, Y.; Zhao, J.; Huang, J.; Tian, C. Toward Sustainable Crop Monitoring: An RGB-Based Non-Destructive System for Predicting Chlorophyll Content in Peanut Leaves. Sustainability 2026, 18, 1001. https://doi.org/10.3390/su18021001

AMA Style

Ge K, Li H, Fan X, Wang Y, Zhao J, Huang J, Tian C. Toward Sustainable Crop Monitoring: An RGB-Based Non-Destructive System for Predicting Chlorophyll Content in Peanut Leaves. Sustainability. 2026; 18(2):1001. https://doi.org/10.3390/su18021001

Chicago/Turabian Style

Ge, Kui, Huan Li, Xinqi Fan, Yixuan Wang, Juan Zhao, Jiatong Huang, and Changcheng Tian. 2026. "Toward Sustainable Crop Monitoring: An RGB-Based Non-Destructive System for Predicting Chlorophyll Content in Peanut Leaves" Sustainability 18, no. 2: 1001. https://doi.org/10.3390/su18021001

APA Style

Ge, K., Li, H., Fan, X., Wang, Y., Zhao, J., Huang, J., & Tian, C. (2026). Toward Sustainable Crop Monitoring: An RGB-Based Non-Destructive System for Predicting Chlorophyll Content in Peanut Leaves. Sustainability, 18(2), 1001. https://doi.org/10.3390/su18021001

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

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