Rice Leaf Disease Detection Using Machine Learning Technique

Abstract

This study explores the application of deep learning models for the detection of rice leaf diseases, a critical issue impacting global rice production and food security. The research focuses on five advanced deep learning architectures: Convolutional Neural Network (CNN), Xception, VGG19, MobileNetV2, and InceptionResNetV2. Utilizing a dataset comprising 6,420 images across four disease categories—Brown Spot, Tungro, Bacterial Blight, and Blast—each model was trained and evaluated to determine its accuracy and effectiveness in disease classification. The proposed methodology encompasses data collection, labeling, image processing, model selection, training, evaluation, and testing. Results demonstrated that the CNN model achieved the highest accuracy at 98.44%, followed closely by MobileNetV2 at 97.82%, VGG19 at 96.57%, InceptionResNetV2 at 95.43%, and Xception at 95.07%. These high accuracies underscore the potential of deep learning models in early disease detection, which is crucial for timely intervention and effective crop management. Comparative analysis with traditional machine learning approaches such as Support Vector Machines (SVM) and Decision Trees, which typically yielded lower accuracies between 81.8% and 97%, highlights the superior performance of deep learning techniques. Furthermore, the study discusses the ethical considerations, including data privacy, accessibility for small-scale farmers, and the need for unbiased models, ensuring equitable benefits across diverse agricultural contexts.