Crop disease diagnosis forms one of the most significant facets of precision farming, which owes a great boost to the power of machine learning. This work presents a new method through which the use of the Tomato Leaf Disease Dataset to classify the tomato leaf’s disease is achieved. The database contains 1609 images for ten disease classes: Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, Spider Mites, Target Spot, Tomato Yellow Leaf Curl Virus, Tomato Mosaic Virus, and Healthy Leaves. The features are extracted through the combination of Thepade’s Sorted Block Truncation Coding (TSBTC) and Haralick Moments (GLCM) to improve texture and intensity description. Extracted features are categorized into multi-level classification (2-ary, 3-ary, 4-ary, 5-ary). The results achieved are stored in an Excel file and re-run using the support of Weka tool where classifiers like Naïve Bayes, Logistic Regression, Sequential Minimal Optimization (SMO), J48, Random Forest, and Random Tree are employed. The study identifies the optimal model so that accurate and automated diagnosis of crop disease can be performed.