Evaluating AI-based mosquito monitoring technologies: a field study in Zhejiang Province, 2025.

Objectives: This study aimed to assess the effectiveness of intelligent mosquito surveillance devices implemented in Zhejiang Province and to compare these metrics with those obtained from traditional surveillance methods. Methods: Intelligent surveillance devices, light traps, and BG-traps were operated concurrently at the same locations. Statistical comparisons were conducted using one-way ANOVA with Dunnett's T3 tests, while the Dynamic Time Warping algorithm was employed to evaluate similarities in density trends. Results: A total of 278 trap-nights using light traps and 278 trap-hours of BG-traps captured 11,349 and 1,524 mosquitoes, respectively, yielding mean densities of 14.56 mosquitoes/(trap·night) and 6.34 mosquitoes/(trap-hour). Additionally, 991 and 646 trap-days of Chengwen Jingling traps and CAMMADE traps recorded 12,003 and 1,180 mosquitoes, respectively, based on backend data, with mean densities of 5.62 mosquitoes/(trap-day) and 1.83 mosquitoes/(trap-day). The light trap produced significantly higher mosquito densities than all other methods ( Conclusions: AI-based mosquito monitoring technologies exhibit significant potential for advancing vector surveillance; however, current technical limitations in trapping efficacy and algorithm accuracy necessitate a hybrid approach integrating AI with conventional methods in the near term, rather than a full replacement.