Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops

Abstract

Abstract Timely identification and management of plant diseases are critical for sustaining crop yields and ensuring food security. This study proposes an innovative approach to detect Rhizoctonia aerial blight (RAB) caused by Rhizoctonia solani, a prevalent disease-causing substantial loss in soybean crops in Uttarakhand, India. By integrating smartphone imageries into sophisticated algorithms, our automated system offers a scalable solution for disease detection. We evaluated nine machine learning algorithms, including logistic regression, Support Vector Machine (SVM), VGG-16 (with and without augmentation), ResNet-18 (with, without augmentation and larger image sizes) and ResNet-34 (with and without augmentation), for disease classification. Results demonstrate the effectiveness of our approach, with classification accuracies ranging from 66.77% (logistic regression) to 95.64% (ResNet-34). Particularly, the ResNet-34 model with data augmentation achieved the highest accuracy, showcasing its potential for accurate disease detection. Leveraging advanced computer technologies and smartphone imaging, this study presents a practical solution for enhancing crop management practices and minimizing yield losses due to diseases. The source code for our implementation is available in supportive file.

Keywords

• Soybean crop diseases
• Machine learning
• Disease detection
• Smartphone imaging