Identification of apple leaf diseases based on improved VGG19 network

Apples are well adapted to most climates and are grown all over the world. Some diseases are encountered during the growing of apples.⁰ These disease problems can be manifested through leaf changes. This paper aims to improve the recognition accuracy of various diseases by improving the vgg19 network and transfer learning methods. In this study, we introduce a transfer learning approach and a more stable approach to the optimal solution through a decay strategy of learning rates, utilizing the capabilities of the VGG19 architecture to efficiently classify various apple leaf diseases. The performance of the model was calculated on the verification data set after training, and the accuracy rate was more than 99%. A series of detailed evaluations were made on the test set to confirm the excellent accuracy of the developed model.