High-accuracy tomato leaf disease classification using adaptive squeeze-and-excitation blocks with improved DRSN

As one of the most valuable crops in the world, tomatoes are essential to the economies of many countries. However, these harvests are still vulnerable to multiple diseases that can diminish and even eradicate the production of healthy crops; thus, it is imperative to accurately and promptly identify these diseases. Consequently, a rapid and precise approach for the classification of plant diseases is presented by the deep learning (DL) method. Therefore, in this study, we have presented an automatic tomato leaf disease classification using convolutional neural network with squeeze-and-excitation blocks (CN 2 -SE) with the improved deep residual shrinkage network method. The suggested method consists of preprocessing, segmentation, feature extraction, and classification stages. Data are first preprocessed using a median filter; followed by preprocessed images are segmented using the enhanced fuzzy C-means clustering (EFCM) method. Next, the proposed convolutional neural network with squeeze-and-excitation blocks (CN 2 -SE) is used to extract significant features. The extracted features are inputted into a proposed IDRSN classifier to classify a tomato leaf as diseased or non-diseased. To enhance the classifier effectiveness, the proposed adaptive golden eagle optimization (AGEO) algorithm is used to optimize the network’s initial weights and biases. According to the results, the suggested method outperforms existing methods in terms of classification accuracy.