Published Paper Details:

Betel vine leaves diseases caused by regular endangerment to bacteria which causes a huge yield loss globally. In recent years, deep learning has shown great potential in the field of image classification. Machine learning, the latest breakthrough in computer vision, is encouraging for fine-grained disease classification, as the method uses SVM classifier and Gaussian mixture model for image segmentation. Disease detection and classifications are considered as the two hardest works to the recognition of Betel vine disease. Betel vine is an important crop in many tropical countries and its classification is important for various applications such as variety recognition and quality control. However, due to its complex and diverse structures, betel vine classification remains a challenging task. One popular deep learning architecture is ResNet, which has been widely used for image classification tasks and has achieved good performance on various benchmark datasets. Thus, this work uses ResNet architecture to identify and classify the betel vine disease. Dataset of betel vine images and preprocessed the data to prepare it for use with the ResNet101 architecture. ResNet101 architecture is a promising method for betel vine classification. The model performs well in terms of precision and recall demonstrating its effectiveness for betel vine classification.