Vol.15, No.1, February 2026.                                                                                                                                                                          ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Identification of Mosquito Species from Larvae Using Machine Learning and Augmented Reality

Siriwan Kajornkasirat, Kritsada Puangsuwan, Chayanin Limrattanabunjong, Phuwanath Nopnorb

© 2026 Siriwan Kajornkasirat, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 15, Issue 1, Pages 7-17, ISSN 2217-8309, DOI: 10.18421/TEM151-01, February 2026.

Received:  09 February 2025.

Revised:  11 August 2025.
Accepted: 24 September 2025.
Published: 27 February 2026.

Abstract:

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.

Keywords – Mosquito larvae, classification, machine learning, augmented reality, 3D model.

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