Deep Learning-Based Transfer Learning for Classification of Cassava Disease

Ademir G. Costa Junior, Fábio S. da Silva, Ricardo Rios
2/26/2025
eess.IVcs.AIcs.CV

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Abstract

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.

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Cite this paper

@article{junior2025deep,
  title  = {Deep Learning-Based Transfer Learning for Classification of Cassava Disease},
  author = {Ademir G. Costa Junior and Fábio S. da Silva and Ricardo Rios},
  year   = {2025},
  eprint = {2502.19351},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.19351v1}
}

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