Overcoming Data Limitations in Thai Herb Classification with Data Augmentation and Transfer Learning

Abstract

Despite the medicinal significance of traditional Thai herbs, their accurate identification on digital platforms is a challenge due to the vast diversity among species and the limited scope of existing digital databases. In response, this paper introduces the Thai traditional herb classifier that uniquely combines transfer learning, innovative data augmentation strategies, and the inclusion of noisy data to tackle this issue. Our novel contributions encompass the creation of a curated dataset spanning 20 distinct Thai herb categories, a robust deep learning architecture that intricately combines transfer learning with tailored data augmentation techniques, and the development of an Android application tailored for real-world herb recognition scenarios. Preliminary results of our method indicate its potential to revolutionize the way Thai herbs are digitally identified, holding promise for advancements in natural medicine and computer-assisted herb recognition.