Machine Learning-Driven SPAD Estimation from RGB Images via Color–Texture Fusion and Its Correlation with Potassium Levels in Walnut Seedlings

Rapid, non-destructive estimation of leaf chlorophyll content (SPAD) is crucial for assessing plant photosynthetic health and nutrient status. However, conventional methods rely on specialized instruments (e.g., SPAD meters and hyperspectral sensors) which are costly, cumbersome, or unsuitable for large-scale field deployment. While RGB image analysis offers a low-cost alternative, most existing approaches depend solely on color features, which are susceptible to environmental interference and lack robustness across growth stages. To address these limitations, this study proposes a novel machine learning framework that fuses both color and texture features from smartphone-captured RGB images for accurate SPAD estimation in walnut seedlings and explores its linkage with potassium nutrition. ‘Wen 185’ walnut seedlings were subjected to seven potassium concentration treatments to induce a chlorophyll gradient. From the leaf images, 22 color indices and 8 texture features based on the Gray-Level Co-occurrence Matrix (GLCM) were extracted. Prediction models were built and compared using Random Forest (RF), XGBoost, and a Support Vector Machine (SVM), with two fusion strategies: data-level and feature-level fusion. Results demonstrated that the RF model with feature-level fusion achieved optimal performance (validation set: R2 = 0.939, RMSE = 0.014, and RPD = 4.539), significantly outperforming models using single-feature types. SHAP analysis identified normalized red, normalized blue, and green-band correlation as the most influential features. This work fills a critical gap by establishing a robust, cost-effective, and interpretable method for SPAD monitoring using ubiquitous RGB imagery. Furthermore, the strong correlation between image-predicted SPAD and potassium levels confirms the method’s high potential for early and non-destructive diagnosis of potassium deficiency in orchard management.