Radial basis function Neural Network optimized with Salp Swarm algorithm espoused paddy leaf disease classification

Abstract

Diseases in rice often cause yield losses of 20–40% in crop production and are closely linked globally. Disease detection is difficult to quickly plan treatments and decrease crop loss. Diagnosis of rice diseases till done manually. Attain AI-assisted detection of disease, here proposed radial function Neural Network optimized with Salp-Swarm approach (PLDC-RBFNN-SSA) for rice leaf disease segmentation. First, we extract rice leaf images from a dataset containing rice leaf disease image samples. Then the preprocessing augmentation process is performed using a random transformation method such as: Simple image rotation as well as mirror functions implemented to every images. B. Rotate Right 90°, Rotate Left 90°, Flip Vertically, Flip Horizontally, Rotate 180°. These preprocessed output images are then passed to Black Widow's k-means clustering method to segment regions of interest (ROI) for rice leaf disease. The segmented output image is then sent to an adaptive grayscale co-occurrence matrix windowing algorithm (GLCMWAA) to extract radiation features. Extracted features then input into a radial function Neural Network optimized with Salp-Swarm algorithm to classify rice leaf images into bacterial blight, blast, brown spot and tungro. The proposed method is implemented in MATLAB and the performance of the proposed PLDC-RBFNN-SSA approach achieves 15.25%, 18.98%, 20.5% and 24.85% higher accuracy. Computation time is reduced by 50.2%, 48.2%, 38.26%, 20.2% compared with existing methods. Finally, the simulation results demonstrate that the proposed PLDC-RBFNN-SSA method is more efficient and accurate to obtain the optimal global solution for detecting and classifying leaf diseases in rice breeding is shown.