Study on the Detection of Chlorophyll Content in Tomato Leaves Based on RGB Images

Chlorophyll is a key substance in plant photosynthesis, and its content detection methods are of great significance in the field of agricultural AI. These methods provide important technical support for crop growth monitoring, pest and disease identification, and yield prediction, playing a crucial role in improving agricultural productivity and the level of intelligence in farming. This paper aims to explore an efficient and low-cost non-destructive method for detecting chlorophyll content (SPAD) and investigate the feasibility of smartphone image analysis technology in predicting chlorophyll content in greenhouse tomatoes. This study uses greenhouse tomato leaves as the experimental object and analyzes the correlation between chlorophyll content and image color features. First, leaf images are captured using a smartphone, and 42 color features based on the red, green, and blue (R, G, B) color channels are constructed to assess their correlation with chlorophyll content. The experiment selects eight color features most sensitive to chlorophyll content, including B, (2G &minus: R &minus: B)/(2G + R + B), GLA, RGBVI, g, g &minus: b, ExG, and CIVE. Based on this, this study constructs and evaluates the predictive performance of multiple models, including multiple linear regression (MLR), ridge regression (RR), support vector regression (SVR), random forest (RF), and the Stacking ensemble learning model. The experimental results indicate that the Stacking ensemble learning model performs the best in terms of prediction accuracy and stability (R2 = 0.8359, RMSE = 0.8748). The study confirms the feasibility of using smartphone image analysis for estimating chlorophyll content, providing a convenient, cost-effective, and efficient technological approach for crop health monitoring and precision agriculture management. This method helps agricultural workers to monitor crop growth in real-time and optimize management decisions.