Rice leaf diseases threaten crop productivity and health. Thus, early and accurate diagnosis is essential. With an emphasis on deep and handcrafted feature extraction and modeling, this study improves rice leaf disease classification using deep learning. Shallow machine learning classifiers are investigated along with feature fusion, handcrafted and deep feature modeling, and transfer learning. A variety of handcrafted models (LBP, HOG, GLCM, SIFT, Gabor), pre-trained deep models (VGG16, VGG19, ResNet50, EfficientNetB3, InceptionV3), and classifiers (Random Forest, Decision Tree, KNN, SVM, XGBoost) are assessed. Four disease classes, Brown Spot, Bacterial Blight, Tungro, and Blast, are present in the benchmark rice leaf dataset. Combining the features of VGG16 with VGG19 produced the best results. State-of-the-art techniques were surpassed by VGG16+SVM, VGG16+VGG19+SVM, ResNet50+EfficientNetB3+XGBoost, and VGG19+ResNet50+LBP models. To help with optimal crop management, this study shows how well deep and handcrafted features may be integrated for early rice leaf disease identification and classification.