A novel approach for Plant disease Classification through Neural Network-Based Color Feature Analysis

plant disease identification using machine vision, which is a challenge in terms of maximizing both the quality and quantity of plant growth. The infection makes plants susceptible to disease. This needs continuous monitoring by experts, which is prohibitively expensive in large farms, but in some instances, erroneous observations by farmers culminate in poor diagnoses. Consequently, we need a fast and accurate plant disease diagnosis predicted to increase the area under cultivation, eliminate heavy losses, and ensure high accuracy. The focus must be on identifying early symptoms of plant disease using computer vision. In order to solve this problem, deep learning can combine machine learning and pattern recognition, two hottest topics in this field. We propose a novel method to identify different plant diseases using deep convolutional neural networks (CNNs). In this study, we propose an image-based classification approach for rice plant diseases, focusing solely on color features. We investigated 12 distinct color spaces and derived 4 features from each color channel, resulting in a total of 48 features. The accuracy of this model is much higher than that of traditional machine learning models. Using the best-performing model, we achieved a classification accuracy of 96.03%. The simulation results show that the proposed method for identifying plant diseases is effective and feasible.