A Resource-Constrained Embedded Aedes Aegypti Mosquito Classifier for the Edge

Vector-borne diseases have been a challenge to public health for decades. This work addresses the Aedes aegypti mosquito, one of the leading carriers of vector-borne diseases such as dengue. We propose to use classifiers based on neural networks and cheap IoT sensors to quickly identify mosquitoes of interest, thus allowing a fast response and preventing the spread of vector-borne diseases. Our implementation uses cheap resource-constrained microcontrollers, such as the ESP32 family, to implement mosquito detection solutions based on machine learning. We then analyze the technical aspects, such as our intelligent models' numerical error analysis, resource degradation time, and data accuracy optimization. Our results can direct solutions for applying technologies such as neural networks, the Internet of Things, and machine learning to combat vector-borne diseases, focusing on dengue, and explore the practical implementation of these solutions on low-cost hardware.