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arXiv · 2502.19351

Deep Learning-Based Transfer Learning for Classification of Cassava Disease

Ademir G. Costa Junior·Fábio S. da Silva·Ricardo Rios
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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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Ademir G. Costa Junior, Fábio S. da Silva, Ricardo Rios. 2025-02-26. Deep Learning-Based Transfer Learning for Classification of Cassava Disease. https://doi.org/10.5753/eniac.2024.244378

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