Tsetse Fly Detection and Sex Classification Model Enrichment Employing <scp>YOLOv8</scp> and <scp>YOLO11</scp> Architecture

ABSTRACT The sterile insect technique (SIT) represents a highly effective and promising method for combating tsetse fly‐related infections, which involves the release of sterilized male tsetse flies in the assigned zones. However, tsetse fly rearing poses specific challenges, particularly in the tsetse sex separation, as this process is labor‐intensive and incurs significant costs. Here, we report a simple model that classifies tsetse flies by sex using an object detection model based on the YOLO algorithm. This paper also conducted a comparative analysis of YOLOv8 and YOLO11 deep learning models, focusing on their efficacy in tsetse fly detection and classification using a range of performance metrics and statistical analysis. The findings reveal that the classification accuracy of YOLO11 stands at 97.6%, whereas YOLOv8 achieves 95.6%. The classification precision of YOLO11 in identifying tsetse flies is 88.6%, while that of YOLOv8 is 85.9%. Additionally, YOLO11 demonstrates an inference speed of 13.0 ms, slightly faster than YOLOv8's 13.4 ms in tsetse sex detection. Moreover, YOLO11 outperformed YOLOv8 in both F1 score and [email protected]–0.9, a success attributed to its enhanced architectural design. However, statistical tests indicate there is no significant difference between the two models, achieving p values ≥ 0.05 for all metrics. This study adds value to tsetse rearing and fly‐based disease control by offering automated tsetse sex detection insights into its practical uses in real‐world contexts. Furthermore, this research enriches the understanding of the two models with tsetse flies as the focal point and recommends a more effective and accurate detection approach. Finally, integrating the model with the mobile object detection Android app will reduce tsetse sex sorting dependency on experienced technical experts and enhance tsetse rearing productivity.