Performance Comparison of Different Attention-Based Transfer Learning Models for Automatic Leaf Disease Classification

In today’s context, the early detection of leaf diseases is paramount, saving considerable human effort and time. This paper addresses the concerned domain by utilizing some popular pre-trained deep learning models: DenseNet201, DenseNet121, ResNet50V2, ResNet101V2, VGG16, VGG19, MobileNetV1, and MobileNetV2. Subsequently, these models undergo training on the potato leaf disease and fruit infection disease datasets, with DenseNet121, MobileNetvV1, and ResNet101V2 identified as the most effective performers. For improving the detection performance, attention blocks are introduced into the models which significantly enhance their respective accuracy scores to 82%, 85%, and 86%, and also 97%, 96%, and 88% for potato leaf disease and fruit infection disease datasets respectively which are the highest when compared with some state-of-the-art methods.