Deep Learning-Based Transfer Learning for Classification of Cassava Disease
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915203597205504 |
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| author | Junior, Ademir G. Costa da Silva, Fábio S. Rios, Ricardo |
| author_facet | Junior, Ademir G. Costa da Silva, Fábio S. Rios, Ricardo |
| contents | This paper presents a performance comparison among four Convolutional Neural Network architectures (EfficientNet-B3, InceptionV3, ResNet50, and VGG16) for classifying cassava disease images. The images were sourced from an imbalanced dataset from a competition. Appropriate metrics were employed to address class imbalance. The results indicate that EfficientNet-B3 achieved on this task accuracy of 87.7%, precision of 87.8%, revocation of 87.8% and F1-Score of 87.7%. These findings suggest that EfficientNet-B3 could be a valuable tool to support Digital Agriculture. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_19351 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning-Based Transfer Learning for Classification of Cassava Disease Junior, Ademir G. Costa da Silva, Fábio S. Rios, Ricardo Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition I.5.1; I.5.4 This paper presents a performance comparison among four Convolutional Neural Network architectures (EfficientNet-B3, InceptionV3, ResNet50, and VGG16) for classifying cassava disease images. The images were sourced from an imbalanced dataset from a competition. Appropriate metrics were employed to address class imbalance. The results indicate that EfficientNet-B3 achieved on this task accuracy of 87.7%, precision of 87.8%, revocation of 87.8% and F1-Score of 87.7%. These findings suggest that EfficientNet-B3 could be a valuable tool to support Digital Agriculture. |
| title | Deep Learning-Based Transfer Learning for Classification of Cassava Disease |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition I.5.1; I.5.4 |
| url | https://arxiv.org/abs/2502.19351 |