Enhancing vector control: AI-based identification and counting of Aedes albopictus (Diptera: Culicidae) mosquito eggs

Background: Dengue fever poses a significant global public health concern, necessitating the monitoring of Aedes mosquito population density. These mosquitoes serve as the disease vectors, making their surveillance crucial for dengue prevention. The objective of this study was to address the difficulty associated with identifying and counting mosquito eggs of wild strains during the monitoring of Aedes albopictus (Diptera: Culicidae) density via ovitraps in field surveys. Methods: We constructed a dataset comprising 1729 images of Ae. albopictus mosquito eggs from wild strains and employed the Segment Anything Model to enhance the applicability of the detection model in complex environments. A two-stage Faster Region-based Convolutional Neural Network model was used to establish a detection model for Ae. albopictus mosquito eggs. The identification and counting process involved applying the tile overlapping method, while morphological filtering was employed to remove impurities. The model’s performance was evaluated in terms of precision, recall, and F1 score, and counting accuracy was assessed using R-squared and root mean square error (RMSE). Results: The experimental results revealed the model’s remarkable identification capabilities, achieving precision of 0.977, recall of 0.978, and an F1 score of 0.977. The R-squared value between the actual and identified egg counts was 0.997, with an RMSE of 1.742. The average detection time for a single tile was 0.48 s, which was more than 10 times as fast as the human–computer interaction method in counting an entire image. Conclusions: The model demonstrated excellent performance in recognizing and counting Ae. albopictus mosquito eggs, indicating great application potential. This study offers novel technological support for enhancing vector control effectiveness and public health standards.