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

Fig. 5: The architecture of ResNet50 [21] Since the dataset is not enough to train robustly the DL model, data augmentation process is carried out. This ask concerns only training and validation data [22]. Test data remains equal to 500 images. In the first step, after training phase, results show that the DL model overfits on all datasets except ‘Original & Contrast’ which gives a training and testing accuracy of 90.56% and 86.23% respectively (Table 3, Fig. 7). In the second step, training the model on the combined datasets yielded the results in Table 4. The model is not overfitted on the ‘Contrast & Flip’ (training: 95.29%; testing: 91.39%) and ‘Flip_Zoom’ (training: 93.15%; testing: 90.59) datasets. These two datasets are the best ones in the second step since they give best results to the DL model. Finally, affine transformation strategy is applied to ‘Contrast & Flip’ and ‘Flip_Zoom’ datasets. Results show that ‘Contrast & Flip’ gives the best performances with an accuracy of 97.80% and a f1_score> 0.9 (Table 5, Fig. 8, Fig. 9). Fig. 6: Training result of the original dataset 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 6 ( ) Year 2023 G