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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.