Classification and recognition of soybean leaf diseases in Madhya Pradesh and Chhattisgarh using Deep learning methods

Abstract

Soybean is a major economic crop worldwide. So proper disease control measures must be implemented to reduce losses. These diseases can significantly affect the yield and quality of soybeans. Machine vision and pattern recognition technologies can help accurately diagnose crop diseases and minimize financial losses for soybean farmers. Many research papers discuss the use of deep learning algorithms for imagebased disease detection, including for soybean crops based on CNN, SVM, KNN, etc. However, lacking a well-curated dataset for soybean diseases is a challenge. Additionally, many existing research papers focus more on demonstrating the approach’s feasibility rather than providing solutions to the specific problems faced in a particular region. The proposed deep learning-based classification system for soybean leaf diseases can help identify Angular Leaf spots, Bacterial blight, Soybean Rust, and Downy mildew. An image dataset was created, and image-enhancing techniques were applied during pre-processing. The proposed classifier system achieved an efficiency of 83.9%, 93.01%, and 71.98% in classifying diseases using CNN, Resnet-V2, and KNN classifiers, respectively.