Auto-metric graph neural network for paddy leaf disease classification

Abstract

In traditional methods, rice plant diseases can be identified individually by experts, but laboratory testing takes a lot of time. As a result, it reduces agricultural productivity and causes economic losses for farmers. To address these, rapid and effective systems for identifying and classifying rice plant diseases need to be developed. This article proposes an Auto-metric Graph Neural Network (AGNN) for the classification of paddy leaf disease. Initially, images are gathered via the dataset of Rice Leaf Disease Image Samples. The collected images are preprocessing by utilizing Anisotropic Diffusion Filter-Based Unsharp Masking with crispening. These preprocessed paddy leaf images are supplied to Bayesian fuzzy clustering for the purpose of segmentation. The segmented pictures are fed to AGNN for categorizing paddy leaf infection image as Bacterial Blight, Blast, Brown Spot and Tungro. The paddy leaf disorder classification-AGNN technique attains accuracies of 8.65%, 10.76%, 18.34% and 13.19% compared to the existing methods.