Robust and efficient rice leaf disease recognition leveraging Bayesian multi-scale edge-guided quantised networks enhanced by Tactical Unit Algorithm-based optimisation

Rice leaf diseases significantly reduce crop yield and quality, making early and accurate diagnosis essential for sustainable agriculture. Although deep learning (DL) methods have improved disease identification, precise multiclass classification remains challenging because of image noise, complex textures and varying environmental conditions. To overcome these limitations, this study proposes a Bayesian Multi-Scale Edge-Guided Quantised Network optimised by the Tactical Unit Algorithm (BMEGQN-TUA) for effective rice leaf disease detection. Initially, Fast Gradient Domain Guided Image Filtering (FGDGIF) is applied to remove noise from Rice Leafs and Rice Leaf Diseases datasets. Then, a Maximum-Entropy Regularised Decision Transformer-based U-Net (MERDTB-U-Net) performs accurate segmentation of colour, region and edge information. Important colour, texture, shape and edge features are selected using the Black-Winged Kite Algorithm (BWKA). The proposed BMEGQN integrates Multi-Scale Edge-Guided Attention Network (MSEGAN) with Bayesian Asymmetric Quantised Neural Network (BAQNN) to classify six disease categories: Brown Spot, Healthy, Hispa, Leaf Blast, Bacterial Leaf Blight and Leaf Smut. Finally, the Tactical Unit Algorithm optimises network parameters for improved convergence and classification efficiency. Experimental results demonstrate superior performance with 99.8% accuracy, 99.5% precision, 99.7% recall, 99.8% specificity, 99.8% F1-score and 0.98 MCC, confirming the robustness and reliability of the proposed framework.