Performance analysis of deep learning architectures for plant leaves disease detection

Abstract

Plant disease is a major concern in agriculture field. Different pathogens are causes of plant disease. Manual inspection sometimes can cause errors in the disease detection over large cultivation area. Large number of resources and high level of expertise is required for correct prediction of diseases. Sometime the experts are not available in the nearby. Some diseases are so common and have same symptoms even the different experts may give different opinion about the same disease.In this work different deep architectures are studied and tested for plant leaves disease detection. For carrying out the study plant village dataset of 20640 images are used which represents 15 class and 3 species namely pepper, potato, and tomato. Total images in the dataset are divided in three sets i.e., 70% for training, 20% for validation and 10% for testing purpose. MATLAB R2019b is utilized for experimentation. Appropriate value of hyper parameter is determined initially by varying number of epochs, learning rate. Performance of Adam optimizer is compared with Sgdm Optimizer. Finally, accuracy of eight deep learning architectures is calculated at 16 & 32 minibatch size.