PERFORMANCE COMPARISON OF CONVOLUTIONAL NEURAL NETWORK-BASED DEEP LEARNING MODELS FOR RHIZOME IMAGE CLASSIFICATION IN THE ZINGIBERACEAE FAMILY
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Abstract
Herbal plants have been an essential part of traditional medicine for centuries. They have been used to treat various ailments and promote overall health. Despite significant advances in modern medicine, herbal plants have played a crucial role and been extensively employed across numerous sectors. A major challenge in their application is the accurate identification and classification of species, as herbs within the same family often exhibit remarkably similar physical characteristics. Such similarities can cause misclassification, leading to ineffective products or potential health risks. This study evaluated and compared four convolutional neural network models including ResNet-50, VGG-19, DenseNet201, and InceptionV3 for classifying rhizome images of four species in the Zingiberaceae family: Turmeric, Zedoary, Plai, and Wild turmeric. This aimed to properly adjust their parameter and evaluated their efficiency of model. The researchers collected and publicly released a dataset of 2,111 images then applied data augmentation to increase training diversity. After that, hyperparameter tuning was performed to optimize model performance. Experimental results were demonstrated that DenseNet201 with 200 training epochs, outperformed the other models, achieving the highest classification accuracy. These findings were suggested that the proposed model has been highly suitable for practical use in herbal industries, aiding species identification, product standardization, and quality control.
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