UAV-Based Mosquito Larval Detection with Minimal Training Data: From Controlled Environments to Turbid Field Conditions

If water surfaces are left untreated, mosquito larvae can develop into adult vectors within a few weeks, posing a significant risk of infectious disease. Consequently, technology for weekly on-site detection of mosquito larvae is required to control such situations effectively. Using autonomous UAV-captured images based on water surface mapping, this study establishes a technological foundation to detect mosquito larvae using deep learning, targeting automated larval source management for vector control. To enable local entomologists to build and adapt models using free, no-code cloud services, determining the minimum viable training dataset is essential. Therefore, we built an object detection model using Google Cloud Platform's Vertex AI with an extremely limited annotated dataset comprising 10 images and 371 annotations in total, of which 8 images (300 annotations) were allocated for training, to evaluate feasibility under resource-constrained conditions. The model was tested on two external datasets: controlled indoor images (JIHS, Japan) and field photographs from natural breeding sites (Malawi). Results demonstrated that while the model achieved high Recall (75.0–100.0%) and Precision (56.3–57.1%) in controlled environments (F1 0.643–0.727), Precision dropped substantially to 9.1–13.3% in turbid field conditions due to false positives from environmental noise, such as floating particles. These findings indicate that dataset diversification incorporating diverse backgrounds is essential for real-world deployment.