Double vision: 2D and 3D mosquito trajectories can be as valuable for behaviour analysis via machine learning

作者：Yasser M. Qureshi, Vitaly Voloshin, Catherine E. Towers, James A. Covington, David P. Towers · 发表于：Parasites & Vectors · 年份：2024 · DOI：10.1186/s13071-024-06356-9 · 被引用次数：7 · 研究领域：Neurobiology and Insect Physiology Research、Insect Pheromone Research and Control、Insect Resistance and Genetics

BACKGROUND: Mosquitoes are carriers of tropical diseases, thus demanding a comprehensive understanding of their behaviour to devise effective disease control strategies. In this article we show that machine learning can provide a performance assessment of 2D and 3D machine vision techniques and thereby guide entomologists towards appropriate experimental approaches for behaviour assessment. Behaviours are best characterised via tracking-giving a full time series of information. However, tracking systems vary in complexity. Single-camera imaging yields two-component position data which generally are a function of all three orthogonal components due to perspective; however, a telecentric imaging setup gives constant magnification with respect to depth and thereby measures two orthogonal position components. Multi-camera or holographic techniques quantify all three components. METHODS: In this study a 3D mosquito mating swarm dataset was used to generate equivalent 2D data via telecentric imaging and a single camera at various imaging distances. The performance of the tracking systems was assessed through an established machine learning classifier that differentiates male and non-male mosquito tracks. SHAPs analysis has been used to explore the trajectory feature values for each model. RESULTS: The results reveal that both telecentric and single-camera models, when placed at large distances from the flying mosquitoes, can produce equivalent accuracy from a classifier as well as ...