A Combination of Data Augmentation Techniques for Mango Leaf Diseases Classification

Abstract

Mango is one of the most traded fruits in the world. Therefore, mango production suffers from several pests and diseases which reduce the production and quality of mangoes and their price in the local and international markets. Several solutions for automatic diagnosis of these pests and diseases have been proposed by researchers in the last decade. These solutions are based on Machine Learning (ML) and Deep Learning (DL) algorithms. In recent years, Convolutional Neural Networks (CNNs) have achieved impressive results in image classification and are considered as the leading methods for image classification. However, one of the most significant issues facing mango pests and diseases classification solutions is the lack of availability of large and labeled datasets. Data augmentation is one of solutions that has been successfully reported in the literature. This paper deals with data augmentation techniques namely blur, contrast, flip, noise, zoom and affine transformation to know, on the one hand, the impact of each technique on the performance of a ResNet50 CNN using an initial small dataset, on the other hand, the combination between them which gives the best performance to the DL network. Results show that the best combination classifying mango leaf diseases is ‘Contrast & Flip & Affine transformation’ which gives to the model a training accuracy of 98.54% and testing accuracy of 97.80% with an f1_score > 0.9.