Thai Antipyretic Medicinal Plant Recognition Using CNNs Integrated into a LINE Chatbot
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Abstract
This study developed a leaf-image recognition system for Thai antipyretic medicinal plants using deep convolutional neural networks (CNNs). Recognizing medicinal plants can be difficult because several species have similar leaf shapes and textures. An automatic recognition tool may help non-expert users identify medicinal plants more consistently. The main contributions are the collection of a 20-species Thai antipyretic herb leaf dataset, the evaluation of eight CNN architectures, and the integration of the selected model into a LINE Chatbot prototype.
A new dataset of leaves from 20 medicinal plants for antipyretics found in southern Thailand was created.
Data augmentation was applied to increase the number and variability of training images. Eight CNN architectures, namely AlexNet, ResNet, VGG, DenseNet, GoogLeNet, EfficientNet, MobileNet, and SqueezeNet, were evaluated for recognizing 20 Thai antipyretic herb species. Lastly, a CNN architecture, which is the best performing, was integrated with a LINE Chatbot for practical application. The experimental results showed that SqueezeNet achieved the highest accuracy of 92.8% with a processing time of 0.004 seconds per image. The SqueezeNet model was therefore selected for integration into the LINE Chatbot prototype.
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