Soybean Leaf Disease Detection System Using Convolutional Neural Network

Soybean cultivation contributes significantly to global food security and industrial supply chains. However, soybean leaf diseases significantly affect crop yield and quality, and traditional manual disease identification methods are often slow, error-prone, and dependent on expert knowledge to be effective. In this study, a Convolutional Neural Network (CNN)-based automated soybean leaf disease detection system was developed. The model was trained on a dataset containing six disease classes using image preprocessing, augmentation, and classification techniques. The proposed system achieved a training accuracy of 95.79% and a validation accuracy of 84.06%, demonstrating strong generalization on unseen images. A web-based interface was implemented using Flask to provide real-time image uploads and disease prediction. The proposed system is efficient, scalable, and beneficial for farmers to detect diseases early, reduce crop losses, and promote smart agriculture.