Feature extraction reduces high-dimensional image data into compact and informative descriptors, improving classification efficiency and performance. In this study, moisture content classification is performed using color image analysis. RGB channels are first separated, and 26 features are extracted from each channel, including statistical measures (mean, standard deviation, variance, median, minimum, maximum, skewness, kurtosis, Shannon entropy, and Renyi entropy) and texture features derived from Gray-Level Co-occurrence Matrix (GLCM). GLCM features contrast, correlation, energy, and homogeneity—are computed across four orientations (0°, 45°, 90°, and 135°). To enhance performance, Chi-square-based feature selection is applied to rank and select the most discriminative features. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is used for classification. Feature-level fusion of RGB channel combinations is also investigated. The model is evaluated using 4-fold and 10-fold cross-validation. The proposed method achieved a highest classification accuracy of 91.54 and 90.42 for 3-class and 5-class classification of moisture content, respectively. Results demonstrate that optimal feature selection and channel combinations significantly improve classification accuracy and robustness.