Deep Learning-Based Transfer Learning for Classification of Cassava Disease
This is an incremental improvement for digital agriculture, offering a tool to aid in disease detection for cassava farmers.
The paper tackled cassava disease classification using transfer learning with CNN architectures, finding that EfficientNet-B3 achieved 87.7% accuracy and similar metrics on an imbalanced dataset.
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.