Size matters: From insecticide susceptibility to AI-driven mosquito metrics

Body size in mosquitoes reflects complex interactions between larval environmental conditions and adult functional traits, shaping behaviors, survival, and susceptibility to control measures. Despite its ecological and operational relevance, body size remains underutilized in routine vector surveillance due to practical limitations in measurement and standardization.
This work highlights a shift toward integrating trait-based biology with artificial intelligence to address that gap. By leveraging computer vision and machine learning, we develop an AI-driven approach to quantifying mosquito body size from field-collected specimens. This framework enables rapid, noninvasive, and scalable phenotyping, making body size a usable metric in real-world operational contexts.
Integrating AI-based phenotyping into mosquito surveillance would enable vector control programs to identify population shifts before they translate into public health threats. By transforming body size into a measurable and scalable trait, this approach shifts the paradigm from reactive interventions to proactive, data-driven strategies, enhancing the precision, efficiency, and foresight of vector monitoring systems.
This approach demonstrates the power of cross-disciplinary innovation, linking ecological insight with computational tools to strengthen and streamline mosquito control efforts.