Comparison of CNN Architectures for Thai Medicinal Plant Classification
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Abstract
Thai medicinal plants are essential to traditional healthcare and local livelihoods. However, many Thai medicinal plants have similar morphological characteristics such as shape, colour, and texture. This problem leads to misidentification and misclassification. Image classifiers utilizing convolutional neural networks (CNNs), which are a class of deep learning models, provide a scalable substitute for manual classification. This study aims to evaluate and compare the performance of three CNN architectures (DenseNet-121, EfficientNet-B3, and MobileNetV2) for classifying 10 species of Thai medicinal plants. The dataset comprises 5,000 leaf images representing 10 species (500 images per species). This study partitioned the dataset into 80% training set and a 20% test set. To enhance model generalization, we applied data augmentation techniques-specifically rotation, flipping, and colour manipulation. Furthermore, we utilized TensorFlow and Keras on Google Colab with GPU acceleration to train the models. Evaluation metrics include accuracy, precision, recall, F1 score, model size, inference time, and CPU utilization. The results highlight a trade-off between accuracy and efficiency: DenseNet-121 achieved the highest accuracy at 96.0% and a Matthews Correlation Coefficient (MCC) of 0.9558. Statistical analysis confirmed that DenseNet-121 significantly outperformed the other architectures (p < 0.05), albeit with a higher inference time (579.22 s). Notably, EfficientNet-B3 and MobileNetV2 both achieved an accuracy of 93.4%, with MobileNetV2 performing the best in terms of model size (11.07 MB) and inference time (3.86 s). In conclusion, DenseNet-121 is the most accurate model, while MobileNetV2 is best suited for real-time applications due to its lightweight and rapid inference time. EfficientNet-B3 offers an optimal balance between accuracy and computational efficiency.
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