ResNet-20: A Deep Learning Approach for Accurate Classification and Identification of Legume Leaf Diseases
TL;DR
This study suggests a deep learning-based method utilizing the ResNet-20 model, which demonstrated the model’s reliable classification abilities and the ROC curve illustrated the model’s exceptional ability to differentiate between healthy and unhealthy leaves.
Abstract
Background: 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. Methods: 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. Result: 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.