Global Journal of Computer Science and Technology, G: Interdisciplinary, Volume 23 Issue 1

© 2023. Demba Faye, Idy Diop, Nalla Mbaye & Doudou Dione Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY reference this article if parts of the article are reproduced in any manner. Applicable licensing terms are at https://creativecommons.org/licenses/by-nc-nd/4.0/ Global Journal of Computer Science and Technology Interdisciplinary Volume 23 Issue 1 Version 1.0 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Online ISSN: 0975-4172 Abstract- Mango is one of the most traded fruits in the world. Therefor 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 th 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 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 a 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. Keywords: data augmentation, mango, disease, classification, deep learning, resnet GJCST-G Classification: DDC Code: 813.54 LCC Code: PS3553.I78 ACombinationofDataAugmentationTechniquesforMango . This research/review article is distributed under the terms of the -NC-ND 4.0). You must give appropriate credit to authors and . Year 2023 & Print ISSN: 0975-4350 e, mango production suffers from e leading methods for image . This paper deals with data augmentation techniques n initial small dataset, on the other hand, 50. LeafDiseasesClassification Strictly as per the compliance and regulations of: : G A Combination of Data Augmentation Techniques for Mango Leaf Diseases Classification By Demba Faye, Idy Diop, Nalla Mbaye & Doudou Dione University Cheikh Anta DIOD of Dakar