A Deep Learning-Based Method for Paddy Leaf Disease Detection and Growth Stage-Specific Treatment Recommendation

Paddy leaf diseases significantly affect rice yield and quality, making early detection and proper treatment essential for precision agriculture. This study proposes a deep learning-based decision support system for paddy leaf disease detection, growth-stage prediction, and stage-specific treatment recommendations. The dataset used in this study comprises paddy leaf images collected from multiple sources and categorized by growth stage and disease class. The dataset was divided into training (80%), validation (10%), and test (10%) sets to ensure proper model evaluation. For growth stage prediction, a lightweight Convolutional Neural Network (CNN) model was developed, while disease classification was performed using transfer learning models, including VGG16, ResNet50, InceptionV3, and MobileNetV2. An ensemble method based on average probability voting was used to improve classification performance. The models were evaluated using accuracy, precision, recall, and F1-score on an independent test set. The experimental results show that the ensemble model achieved higher accuracy compared to individual models, demonstrating improved robustness and generalization. The proposed system was implemented as a Streamlit web application that provides disease detection, growth-stage prediction, and treatment recommendations. The proposed integrated framework can support farmers and agricultural experts in making timely and accurate disease management decisions.