Herbal Plant Classification System Using Deep Learning Technology on a Mobile Application: A Case Study of Udai Charoen Subdistrict, Khuan Kalong District, Satun Province
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Abstract
This study aimed to develop a herbal plant classification system based on Deep Learning technology implemented on a mobile application, and to evaluate both the system’s performance and user satisfaction. The application was developed using the Flutter framework for mobile development, PHP for data management, and TensorFlow for constructing the Deep Learning model. A total of 42 medicinal plants were collected, comprising 2,100 images. The system employed a Custom CNN Architecture consisting of four convolutional layers, and its performance was tested using the Adam optimizer. The results demonstrate that the herbal plant classification system achieved an accuracy of 85.7%. In addition, experts evaluated the system’s performance at the highest level ( = 4.28, S.D. = 0.14), and users reported the highest level of satisfaction ( = 4.21, S.D. = 0.61). These findings indicate that the developed classification system is feasible for real-world use and can contribute significantly to the preservation of medicinal plants.
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