Abstract

Rice plant disease detection is crucial in agriculture to prevent crop loss and enhance productivity. Traditional manual inspection methods often lead to inaccuracies, delays in diagnosis, and excessive pesticide use. To address these challenges, this study proposes an Artificial Layered Fuzzy Neural Network-based African Vulture Optimization (ALFNN-AVO) algorithm for early and accurate detection of rice plant diseases. The proposed framework integrates multiple advanced techniques, including Cross Fusion former (CF former) for feature extraction, Squeeze Excitation (SE) fusion for enhancing feature representation, and Spatial Fuzzy C-Means (SPFCM) for precise segmentation of affected plant regions. Furthermore, an Artificial Layered Depth Separable Neural Network (ALDSNN) is employed for multi-class classification of rice plant diseases. The Differential Bitwise African Vultures Optimization Algorithm (DBAVOA) is introduced to optimize the hyperparameters, ensuring improved convergence and classification performance. Experimental results validate the efficiency of the proposed model, achieving an accuracy of 98.87% and an execution time of 0.09 minutes, outperforming existing methodologies. The findings demonstrate that the proposed framework offers a reliable and computationally efficient solution for real-time rice plant disease detection, contributing to sustainable agricultural practices.

Keywords: Rice plant disease detection; Cross Fusion former; Squeeze Excitation; Spatial Fuzzy C-Means; Artificial Layered Depth Separable Neural Network; African Vulture Optimization