Speaker
Description
Volatile organic compounds (VOCs) are increasingly recognized as key modulators of insect behavior, yet their impact on courtship dynamics remains understudied. Here, we present a deep learning-based behavioral tracking system tailored to detect and quantify mating events in Hermetia illucens (Black Soldier Fly) under controlled experimental conditions. This tool enables high-throughput, fine-scale analysis of behavioral responses to environmental variables. Select VOCs, previously shown to elicit physiological responses in BSF, were chosen for behavioral testing to probe their influence on mating activity. The system is currently being applied to assess the effects of VOC exposure, cage density, and sex ratio on mating rates. By integrating computer vision with behavioral ecology, this approach opens new avenues for investigating the role of semiochemicals in insect reproduction and lays the groundwork for chemically informed strategies in insect mass-rearing and behavioural research.
