TEM Journal | 2026
Authors: Kajornkasirat S.; Puangsuwan K.; Limrattanabunjong C.; Nopnorb P.
DOI: 10.18421/TEM151-01
Journal: TEM Journal
Year: 2026
Publisher: UIKTEN - Association for Information Communication Technology Education and Science
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
– This study is focused on enhancing the development of skills and promoting a comprehensive understanding of health science education by providing an accessible educational tool aligned with current technology trends. Users access the mobile application to classify mosquito larvae species by uploading pictures of the larvae to the system. The system provides a 3D model of mosquito larvae that users can view via AR (Augmented Reality) using their mobile camera. TensorFlow was used to train a mosquito larvae identification model for 50 rounds, achieving a 92% accuracy. AR can be used to give users a close-up look at the different features of mosquito larvae, such as their segmented body, siphon, and bristles. This can help users to better understand the biology and behavior of mosquito larvae, and to learn to identify their species. These systems can be trained on a labeled dataset of mosquito larvae images to learn to identify different species of mosquitoes, including Aedes and Culex spp. Once trained, these systems can be used to identify mosquito larvae quickly and accurately. Automated mosquito larval identification systems have the potential to play a significant role in the prevention and control of mosquito-borne diseases. © (2026), (UIKTEN - Association for Information Communication Technology Education and Science). All rights reserved.
3D model; augmented reality; classification; machine learning; Mosquito larvae