Rice disease classification using feed-forward neural network: Comparative analysis between hybrid and single feature extraction algorithms

Rice diseases significantly reduce crop productivity and pose a serious threat to global food security. Early and accurate identification of rice leaf diseases is therefore essential to enable timely intervention and effective disease management. Recent studies have applied Artificial Neural Networks (ANNs) with single feature extraction or direct imaging methods; however, these approaches often suffer from limited feature representation, poor generalization, high computational cost, and limited interpretability. Moreover, the comparative effectiveness of hybrid feature extraction strategies remains insufficiently explored. To address these challenges, this study proposes a novel hybrid feature extraction algorithm, GreyTexFWave, which integrates Gray Level Co-occurrence Matrix (GLCM), Gray Level Dependence Matrix (GLDM), Texture, Fast Fourier Transform (FFT), and Discrete Wavelet Transform (DWT) features to capture both spatial and frequency-domain characteristics of rice leaf images. The extracted features are classified using a Feed-Forward Neural Network (FFNN). Experiments were conducted on a balanced dataset containing images of bacterial leaf blight, stemborer, and tungro diseases. Model performance was evaluated using accuracy, sensitivity, precision, and F-measure, and compared against individual feature extraction methods. The proposed GreyTexFWave-based FFNN achieved an average accuracy of 94.84 ± 0.04%, with 94.9% sensitivity, 88.7% precision, and 91.7% F-measure, outperforming all single-feature extraction approaches. A paired t-test further confirmed that the performance improvements are statistically significant. The results demonstrate that hybrid feature extraction substantially enhances rice disease classification performance. The proposed approach offers a practical and interpretable solution for early rice disease detection, supporting precision agriculture and reducing yield losses through timely disease management.