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

this combination as a data augmentation technique to this dataset will allow us to achieve excellent results in mango leaf disease classification using a deep learning model such as ResNet50. Then, this model will be deployed in mobile and web applications to allow mango growers to diagnose diseases in their crops without expert intervention. A cknowledgements The authors would like to thank IRD (Institut de Recherche pour le Développement) SENEGAL for access to their server which was used in this study. R eferences R éférences R eferencias 1. Kusrini, K., Suputa, S., Setyanto, A., Agastya, I. M. A., Priantoro, H., Chandramouli, K., & Izquierdo, E. (2020). Data augmentation for automated pest classification in Mango farms. Computers and Electronics in Agriculture, 179, 105842. doi:10.10 16/j.compag.2020.105842 2. N. F. Rosman, N. A. Asli, S. Abdullah, and M. Rusop. Review: Some common disease in mango. AIP Conference Proceedings 2151, 020019 (2019); https://doi.org/10.1063/1.5124649 3. Shih-Lun Wu, Hsiao-Yen Tung, Yu-Lun Hsu. (2020). Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights. Computer Vision and Pattern Recognition. 4. Fuentes, A., Yoon, S., Kim, S., & Park, D. (2017). A Robust Deep-Learning-Based Detector for Real- Time Tomato Plant Diseases and Pests Recognition. Sensors, 17(9), 2022. 5. Szegedy, C., Vanhoucke, V., Io ff e, S., Shlens, J., and Wojna, Z. (2016). "Rethinking the in ception architecture for computer vision," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Las Vegas), 2818-2826. 6. Nabodip Sutrodhor, Molla Rashied Hussein, Md. Firoz Mridha, Prokash Karmokar, Tasrif Nur. “Mango Leaf Ailment Detection using Neural Network Ensemble and Support Vector Machine”. International Journal of Computer Applications (0975 – 8887) Volume 181 – No. 13, August 2018. DOI:10.5120/ijca2018917746 7. S. Arivazhagan, S.Vineth Ligi. (2018). Mango Leaf Diseases Identification Using Convolutional Neural Network. International Journal of Pure and Applied Mathematics Volume 120 No. 6 2018, 11067-11079. 8. Saleem, R.; Shah, J.H.; Sharif, M.; Yasmin, M.; Yong, H.-S.; Cha, J. Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique. Appl. Sci. 2021, 11, 11901. https://doi.org/10.3390/app 112411901 9. Mia, M. R., Roy, S., Das, S. K., & Rahman, M. A. (2020). Mango leaf disease recognition using neural network and support vector machine. Iran Journal of Computer Science, 3(3), 185–193. doi: 10.1007/s 42044-020-00057-z 10. Simonyan, K., Zisserman, A., 2014. A8_ Large-Scale Image Recognition. Very Deep Convolutional Networks for Large-Scale Image Recognition. https://doi.org/10.48550/arXiv.1409.1556. 11. Shorten, C., Khoshgoftaar, T.M. A survey on Image Data Augmentation for Deep Learning. J Big Data 6, 60 (2019). https://doi.org/10.1186/s40537-019- 0197-0 12. R. Dandavate and V. Patodkar, "CNN and Data Augmentation Based Fruit Classification Model," 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I- SMAC), 2020, pp. 784-787, doi: 10.1109/I- SMAC49090.2020.9243440 13. M. A. Agastya and A. Setyanto, "Classification of Indonesian Batik Using Deep Learning Techniques and Data Augmentation," 2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE), 2018, pp. 27-31, doi: 10.1109/ICITISEE.2018.8720990. 14. S. Bargoti and J. Underwood, "Deep fruit detection in orchards," 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 3626- 3633, doi: 10.1109/ICRA.2017.7989417. 15. S. -L. Wu, H. -Y. Tung and Y. -L. Hsu, "Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights," 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020, pp. 446-453, doi: 10.1109/ICMLA51294.2020.00076. 16. Zhang, YD., Dong, Z., Chen, X. et al. Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimed Tools Appl 78, 3613–3632 (2019). https://doi.org/10.1007/s11042-017-5243-3 17. Supekar, A. D., & Wakode, M. (2020). Multi- Parameter Based Mango Grading Using Image Processing and Machine Learning Techniques. INFOCOMP Journal of Computer Science, 19(2), 175–187. Retrieved from https://infocomp.dcc.ufla. br/index.php/infocomp/article/view/756 18. FAO. 2022. Major Tropical Fruits: Preliminary results 2021. Rome. 19. Faye, D. , Diop, I. and Dione, D. (2022) Mango Diseases Classification Solutions Using Machine Learning or Deep Learning: A Review. Journal of Computer and Communications, 10, 16-28. doi: 10.4236/jcc.2022.1012002. 20. He, Kaiming et al. “Deep Residual Learning for Image Recognition.” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015): 770-778. 21. Ali L, Alnajjar F, Jassmi HA, Gocho M, Khan W, Serhani MA. Performance Evaluation of Deep CNN- A Combination of Data Augmentation Techniques for Mango Leaf Diseases Classification © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue I Version I 9 ( )G Year 2023