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

Fig. 7: Training result of the dataset ‘Original & Contrast’ Following the results presented previously, in the first step, the model overfitted in the generated datasets, except‘Original and Contrast’ dataset which resulted in an accuracy of 86.23%. Concerning data augmentation strategies namely blur, contrast, noise and zoom, the best cominations for classifying mango leaf diseases are ‘Contrast & Flip’ and ‘Flip & Zoom’, according to the results in the second step. These two strategies yielded accuracies of 91.39% and 90.59% respectively. In the final step, applying the ‘Affine Transformation’ strategy to the datasets generated by these two strategies revealed that the best combination for mango leaf diseases classification is ‘Contrast & Flip & Affine Transformation’ since it yielded an accuracy of 97.80%. Table 3: Results of the first step Original & Blur Original & Contrast Original & Flip Original & Noise Original & Zoom Training Accuracy (%) 98.25 90.56 95.35 76.36 92.76 Testing Accuracy (%) 84.21 86.23 80.60 34.84 84.80 Result overfitting ok overfitting overfitting overfitting Table 4: Results of the second step Blur & Contrast Blur & Flip Blur & Noise Blur & Zoom Contrast & Flip Contrast & Noise Contrast & Zoom Flip & Noise Flip & Zoom Noise & Zoom Training Accuracy (%) 87.28 94.14 78.29 92.08 95.29 84.25 88.71 78.80 93.15 82.48 Testing Accuracy (%) 65.45 85.30 63.32 65.82 91.39 31.74 58.23 645.85 90.59 78.47 Result overfitting overfitting overfitting overfitting ok overfitting overfitting overfitting ok overfitting 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 7 ( )G Year 2023