Deep Learning-Based Transfer Learning for Classification of Cassava Disease

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.

@article{junior2025_2502.19351,
  title={ Deep Learning-Based Transfer Learning for Classification of Cassava Disease },
  author={ Ademir G. Costa Junior and Fábio S. da Silva and Ricardo Rios },
  journal={arXiv preprint arXiv:2502.19351},
  year={ 2025 }
}