Capsule Networks for Rice Leaf Disease Classification

Deep Learning is a high-performance machine learning approach that combines supervised machine learning and feature learning. It is built of a sophisticated models with numerous hidden layers and neurons to create advanced image processing models. DL has proven its effectiveness and resilient in different fields including big data, computer vision, image processing, and many others. In agriculture, rice leaf infections are a frequent and pervasive issue that lower crop and output. This research proposed a reduced form of Capsule Network (Caps NET), a form convolutional neural network, for the classification of rice leaf disease. The goal of the suggested Caps NET model was to assess the suitability of various feature learning models and enhance deep learning models' capacity to learn about rice leaf disease classification. Caps NET was fed images of both healthy and infected leaves. High classification performance was obtained with the ideal configuration (FC1 (960), FC2 (768), and FC3 (4096)), which had 96.66% accuracy, 97.25% sensitivity, and 97.49% specificity.