Apple Leaf Disease Classification Using Image Dataset: a Multilayer Convolutional Neural Network Approach

Abstract

Agriculture is one of the prime sources of economic growth in Russia; the global apple production in 2019 was 87 million tons. Apple leaf diseases are the main reason for annual decreases in apple production, which creates huge economic losses. Automated methods for detecting apple leaf diseases are beneficial in reducing the laborious work of monitoring apple gardens and early detection of disease symptoms. This article proposes a multilayer convolutional neural network (MCNN), which is able to classify apple leaves into one of the following categories: apple scab, black rot, and apple cedar rust diseases using a newly created dataset. In this method, we used affine transformation and perspective transformation techniques to increase the size of the dataset. After that, OpenCV crop and histogram equalization method-based preprocessing operations were used to improve the proposed image dataset. The experimental results show that the system achieves 98.40% training accuracy and 98.47% validation accuracy on the proposed image dataset with a smaller number of training parameters. The results envisage a higher classification accuracy of the proposed MCNN model when compared with the other well-known state-of-the-art approaches. This proposed model can be used to detect and classify other types of apple diseases from different image datasets.