Automatic Detection and Classification of Apple Leaves Diseases Using MobileNet V2

Abstract

Apple orchards in the Imouzzer Kandar region (Morocco) suffer from numerous leaf diseases causing extreme yield losses. Early diagnosis favors the control of these diseases by optimizing the use of chemical products and reducing environmental impacts. In this context, we propose the deployment of an automated detection and classification system for these apple leaf diseases. We have pre-trained the convolutional neural network MobileNet V2 and evaluated its performance across several hyperlearning parameters to recognize the symptoms of the eight most common diseases in the region. The results show that MobileNet V2 is more than 98% effective in identifying these diseases. This encourages us to introduce this valuable tool to farmers looking to improve the quality of their crops.