Enhancing Convolutional Neural Network Accuracy for Herbal Leaf Classification Using Squeeze and Excitation Attention - Utomo, Ragil Gigih; Mardhiyyah, Rodhiyah (Journal of Scientific Research, Education, and Technology (JSRET), 2025)
Accurate identification of herbal plant species is crucial for human health, but remains challenging, even for experts. This study addressed this need by developing a Convolutional Neural Network (CNN) model integrated with an attention mechanism for reliable herbal leaf classification. The research implemented the MobileNetV2 architecture, which was enhanced by incorporating the Squeeze-and-Excitation (SE) attention module. The dataset consisted of 1,500 images across 10 classes of herbal leaves, split into 80% for training, 10% for validation, and 10% for testing. Both the native CNN and the enhanced CNN (CNN-AM) models were trained using TensorFlow and evaluated using standard metrics like accuracy, precision, recall, and F1-score. The comparison results decisively demonstrated the effectiveness of the attention mechanism. Integrating Squeeze-and-Excitation significantly improved performance. The average accuracy of the model increased from 68% to 72%, while the average loss decreased from 1.03 to 1.02. The best-performing CNN-AM model achieved a strong 86% accuracy with a 0.53 loss. These findings confirm that the Squeeze-and-Excitation attention mechanism effectively enhances herbal leaf classification performance, offering a promising foundation for developing reliable and efficient identification systems.