Prototipo de identificación del mosquito AEDES con TINYML

This project presents the development of an innovative system for the automatic identification of the Aedes mosquito by implementing TinyML technology. The system integrates specialized hardware to capture the mosquito's acoustic signals, a machine learning model for low-power devices, and a mobile application for data visualization and management. The proposed solution uses the Edge Impulse platform for model training and optimization, enabling accurate and real-time identification of the mosquito. The prototype is designed to operate autonomously in field conditions, significantly contributing to entomological surveillance and vector control. This prototype seeks to optimize current strategies for the prevention and control of Aedes-borne diseases, such as dengue, zika, and chikungunya, especially in resource limited regions. Early detection of mosquitoes allows health authorities to make informed decisions quickly and effectively, such as implementing targeted control measures or alerting the population in at-risk areas. It is expected that this prototype will contribute to reducing the incidence of Aedes-borne diseases and improving the quality of life of communities, especially in those with limited resources.

• Huvudupphovsman: Muñoz Villarreal, Marco Antonio (author)
• Materialtyp: bachelorThesis
• Publicerad: 2025
• Ämnen: aedes, TinyML, edge impulse, identificación acústica, vigilancia entomológica, aprendizaje automático, salud pública, aplicación móvil
• Länkar: https://repositorio.puce.edu.ec/handle/123456789/45458