Can I read Deep Learning-Based Transfer Learning for Classification of Cassava Disease on EtoBox?

Deep Learning-Based Transfer Learning for Classification of Cassava Disease by Junior, Ademir G. Costa; da Silva, Fábio S.; Rios, Ricardo is a scholarly article available to read on EtoBox.

What is Deep Learning-Based Transfer Learning for Classification of Cassava Disease about?

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.

Author

Junior, Ademir G. Costa; da Silva, Fábio S.; Rios, Ricardo

Published

2025

Language

EN