1Department of Computer Applications, Bharati Vidyapeeth (Deemed to be University)Pune, Institute of Management and Rural Development Administration, Sangli-416 416, Maharashtra, India
2Department of Computer Science and Application, Dr. Vishwanath Karad MIT World Peace University, Pune-411 045, Maharashtra, India
3Department of Computer Applications, Kasegaon Education Society's Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale-415 414, Maharashtra, India
4School of Computer Science and Applications, Dr. Vishwanath Karad MIT World Peace University, Pune-411 038, Maharashtra, India
5Department of Computer Applications, Bharati Vidyapeeth (Deemed to be University), Pune, Yashwantrao Mohite Institute of Management, Karad-416 416, Maharashtra, India
6Department of Computer Science and Engineering, Kasegaon Education Society's Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale-415 414, Maharashtra, India
The necessity for effective and precise disease detection techniques is highlighted by the rising demand for legumes. Convolutional neural networks (CNNs), a type of deep learning, provide a potent way to diagnose plant diseases. CNNs make it possible to accurately identify illnesses in real time by quickly evaluating enormous amounts of plant pictures. By giving farmers proactive tools for monitoring crop health, cutting losses and enhancing food quality, automated detection systems can improve agricultural practices.
To categorize bean leaves, this study suggests a deep learning-based method utilizing the ResNet-20 model. To increase model generalization and lessen overfitting, data augmentation techniques such as scaling, rotation and flipping were employed. The model was trained on a dataset of labelled images and its performance was assessed using categorization metrics, confusion matrix, ROC curve and Matthews Correlation Coefficient.
The ResNet-20 model's test accuracy was 76.15%. Additional performance indicators such as metrices demonstrated the model's reliable classification abilities. The ROC curve further illustrated the model's exceptional ability to differentiate between healthy and unhealthy leaves.
Agriculture benefits, Convolutional neural network, Image analysis, ResNet-20 algorithm
