Innovative Machine Learning Techniques for Accurate Detection of Bacterial Blight in Rice Agriculture

Bacterial blight is a devastating disease that can significantly impact rice production. Early and accurate detection of the disease is crucial for effective management. This research proposes a deep learning-based approach, specifically utilizing Convolutional Neural Networks (CNNs), to identify and classify different severity levels of bacterial blight in rice plants. The proposed model involves a multi-step process, including data preprocessing, feature extraction, and classification. The CNN architecture is designed to capture the visual characteristics of infected plants, enabling accurate disease diagnosis. The model’s performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. By implementing this automated disease detection system, farmers can make timely decisions regarding disease management strategies, leading to improved crop yields and reduced economic losses. This research contributes to the advancement of precision agriculture and sustainable food production.