Classifying stages in the gonotrophic cycle of mosquitoes from images using computer vision techniques

The ability to distinguish between the abdominal conditions of adult female mosquitoes has important utility for the surveillance and control of mosquito-borne diseases. However, doing so requires entomological training and time-consuming manual effort. Here, we design computer vision techniques to determine stages in the gonotrophic cycle of female mosquitoes from images. Our dataset was collected from 139 adult female mosquitoes across three medically important species – Aedes aegypti, Anopheles stephensi, and Culex quinquefasciatus – and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid and gravid). From these mosquitoes and stages, a total of 1,959 images were captured on a plain background via multiple smartphones. We then trained and validated an EfficientNet-B0-based model. With unseen data, the overall classification accuracy achieved by the model was 93.59%. Furthermore, we also assessed the explainability of our AI model, by implementing Grad-CAMs - a technique that highlights pixels in an image that were prioritized for classification. We observe that the highest significance was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. To the best of our knowledge, this work is the first to use computer vision techniques to identify the stages of the gonotrophic cycle of mosquitoes, and we discuss some potential practical applications of our techniques.