LarvaTracker: A Robot Solution for Locating and Treating Mosquito Breeding Sites

Abstract

This study developed LarvaTracker, an autonomous robotic system designed to identify and treat mosquito breeding sites, specifically targeting the larvae of Aedes aegypti and Aedes albopictus. The platform integrates environmental sensors for monitoring pH, turbidity, dissolved oxygen, and temperature with a YOLOv8 object detection model to recognize high-risk containers such as bamboo stumps, tires, and plant pots. Built on a hardware architecture of Raspberry Pi 5 and ESP32 with a solar-powered supply, the system utilizes an autonomous navigation module and a precision sprayer to execute targeted larvicide application with minimal human intervention. Field testing conducted at Bamboo Park in Lubao, Pampanga, demonstrated high detection accuracy, successful dispensing, and reliable real-time data reporting via a web-based dashboard. These results confirm LarvaTracker’s effectiveness in reducing potential habitats and supporting public health efforts against vector-borne diseases. Future enhancements should focus on expanding the object detection dataset and integrating predictive weather data for dynamic patrol scheduling, establishing LarvaTracker as a sustainable and promising automated intervention for outdoor mosquito management.