Whale Optimized Deep Model for Paddy and Maize Leaf Disease Detection

Abstract

Plant disease management is an essential process to minimize loss in the field of agriculture. Plant leaf disease detection(PLDD) technology helps the formers to reduce the loss of quality yield production. This study focuses on detecting paddy and maize plant leaf disease-detecting methods. It contributes to a PLDD method to improve detection accuracy and overall performance. The PLDD method introduced a whales-optimized artificial neural network (WOANN) method for classifying the four Maize and four rice leaf disease-related classes. The WOANN uses the whale optimizer's food-searching functionalities to improve the performance and detection accuracy of the dense net model. This WOANN classifies the maize leaf light, maize grey leaf spot, maize common rust, rice bacterial leaf blight, rice bacterial leaf steak, rice brown spot, and healthy leaves of both Maize and paddy. It uses the Hilbert-Schmidt independent criterion lasso correlation algorithm to support the WOANN classifier in selecting the significant features. The performance analysis shows that the WOANN-based approach achieves a maximum of 99.35% detection accuracy for maize leaf disease and 99.13% for paddy leaf disease. Its efficiency analysis shows that the WOANN-based approaches achieve a maximum accuracy rate than comparison approaches.