Prediction of Pathogen Causing Rice Plant Disease and Recommendation using Enhanced Machine Learning Technique

Plant diseases pose significant challenges to agricultural productivity, impacting both the quality and quantity of crop yields. Early detection and effective management of these diseases are essential for mitigating their detrimental effects on agricultural output. However, traditional methods of disease monitoring and diagnosis are often costly and require continuous expert intervention. To address these challenges, this study proposes a novel approach that combines image processing techniques and advanced machine learning methodologies for the accurate identification and classification of rice leaf diseases. By utilizing datasets comprising images of diseased rice leaves, environmental data, and historical disease reports, the proposed system utilizes Convolutional Neural Networks (CNNs) for image analysis and feature extraction. Subsequently, a Support Vector Machine (SVM) classifier is employed for disease detection and classification based on the extracted features. The integration of image processing and machine learning technologies offers a cost-effective and efficient solution for early disease detection and management in agriculture. By automating the process of disease identification, the proposed system facilitates timely interventions, thereby minimizing crop losses and promoting agricultural sustainability.