Hybrid deep learning with improved Salp swarm optimization based multi-class grape disease classification model

Abstract

The recent revolutions in Computer Vision (CV) and Artificial Intelligence (AI) techniques have found many applications in grapevine and smart agriculture processes. Recently, Deep Learning (DL) techniques like Convolutional Neural Networks (CNN) have been broadly applied in smart agriculture, leaf disease recognition, and scene perception. In this background, the current study develops a Hybrid Deep Learning with Improved Salp Swarm Optimization-based Multi-class Grape Disease Classification (HDLISSA-MGDC) model. The proposed HDLISSA-MGDC model focuses on the classification of grape leaf images into four distinct classes such as black measles, black rot, Isariopsis leaf spot and healthy. Initially, the Median Filtering (MF) technique is applied for image pre-processing, which eliminates the noise present in the images. In addition, the HDLISSA-MGDC model designs a feature extractor with the help of Dilated Residual Network (DRN) and Adam optimizer. For grape disease classification, the Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model is employed in this study. Finally, the ISSA is exploited to adjust the hyperparameter values of the CNN-GRU method. The proposed HDLISSA-MGDC method was simulated using the plant leaf disease dataset. The simulation results show the significant performance of the proposed HDLISSA-MGDC model.