Eradicating Mosquitoes with Drones and Artificial Intelligence

SynTel Corp — Batticaloa Pilot

Dengue remains one of the most persistent public health challenges in Sri Lanka, and traditional mosquito-breeding-site inspections can only go so far — especially in areas that are hard to reach, elevated, or otherwise unsafe to check manually. SynTel Corp's Batticaloa pilot project tackles this problem head-on by combining drone imagery, remote sensing, and artificial intelligence to identify, map, and prioritise potential mosquito breeding sites across selected high-risk areas of Batticaloa.

Project Overview

The system uses aerial drone imagery paired with AI-based detection models to spot breeding sites that field officers might otherwise miss — rooftops, gutters, elevated tanks, and other locations that are difficult or unsafe to inspect on foot. A demonstration web platform allows health authorities to visualise detection results, monitor drone operations, and manage field inspections in one place, complete with risk-level mapping, confidence scores, and downloadable inspection reports.

Core Objectives

Detecting and mapping breeding components. The AI model is trained to identify coconut shells, discarded tires, tins, containers, stagnant or sullage water, blocked roof gutters, elevated water tanks, and other water-holding objects — including those in hard-to-reach and elevated locations that manual inspections often overlook.

Strengthening community accountability and compliance. Drone- and AI-based inspections reduce the labour-intensive burden on field officers by covering larger areas in a shorter time and minimising the chance that breeding sites go undetected. Drones can also observe areas where physical access is restricted — locked premises, uncooperative property owners, or safety concerns such as guard dogs. This gives inspectors objective, georeferenced evidence they can review remotely, reducing confrontations with landowners while strengthening accountability, since documented findings leave far less room for denial or negotiation.

Building Institutional Support

Rolling out a drone- and AI-based mosquito surveillance program isn't just a technical exercise — it requires buy-in from the people responsible for public health decisions on the ground. Before a single drone took flight in Batticaloa, SynTel Corp made sure the right stakeholders were at the table.

Coordination discussions were held with the Regional Director of Health Services (RDHS) and the Commissioner of the Batticaloa Municipal Council to present the proposed drone- and AI-based dengue surveillance project. These meetings covered the project's goals, the priority areas identified for the pilot, and how the technology could support existing public health efforts — giving both institutions a chance to ask questions, weigh in on priority locations, and understand how the detection platform would fit into their existing inspection processes.

The coordination effort paid off: following these discussions, SynTel Corp received an official request from the RDHS to proceed with the proposed pilot activities. This institutional endorsement was a critical milestone, ensuring the pilot moved from planning into active fieldwork aligned with the priorities of Batticaloa's health and municipal leadership.

On the Ground with Public Health Inspectors

Technology alone doesn't solve public health problems — it has to work alongside the people who do this job every day. As part of the pilot, SynTel Corp's team conducted joint field inspections with local Public Health Inspectors (PHIs) to make sure the detection system was being built around real operational needs, not assumptions.

PHIs are the ones who walk properties, check gutters and containers, and make the day-to-day calls on where mosquitoes are breeding. Working alongside them gave the SynTel Corp team direct insight into:

• Their operational priorities — what matters most when inspecting a property for breeding risk
• The common mosquito breeding components they encounter most often in the field
• The physical access limitations they regularly run into, from locked gates to elevated or hard-to-reach structures
• The requirements for field verification — what PHIs would need to see and confirm before trusting a drone- and AI-generated detection

These joint inspections weren't just a courtesy visit — they directly shaped how the detection system needs to perform in practice, ensuring the AI model is trained to recognise the components that matter most and that eventual detection results can be meaningfully verified on the ground.

Pilot Study Workflow

1. Site Identification & Planning — reviewing, selecting, visiting, and mapping priority locations with support from local health authorities.
2. UAS Data Collection & AI Development — obtaining approvals, planning drone missions, capturing aerial imagery, and training the AI-based breeding-site detection model.
3. Deployment & Validation — displaying detection results on the platform, validating the model in new locations, and refining the system based on field feedback.

Where the Project Is Being Conducted

The pilot is being carried out across selected high-risk areas of Batticaloa, chosen in consultation with the Regional Director of Health Services, the Batticaloa Municipal Council, and Public Health Inspectors — covering multiple survey sites around Kallady, Navalady, and Kattankudy.

An Invitation to Partner

SynTel Corp is inviting partners to support the expansion of the Eradicating Mosquitoes with Drones program beyond Batticaloa, extending targeted mosquito-control solutions to high-risk communities across Sri Lanka. The next phase of this work aims to advance:

1. Autonomous drone-based detection of mosquito breeding sites and risk mapping
2. Targeted larvicide/pesticide application across high-risk breeding areas with improved safety and coverage
3. Integration of AI, weather forecasting, and biological control methods to support long-term prevention of dengue and other mosquito-borne diseases