This study explores the transformative impact of artificial intelligence (AI) in ecosystem monitoring, specifically object detection with YOLO (You Only Look Once), emphasising the search for optimal tools and model efficiency. The shift from manual counting to AI-based detection significantly reduces time investment. Methodologically, the YOLO model is employed, and comprehensive training strategies are outlined. The threefold data division ensures unbiased evaluation, and diverse configurations are explored for optimal model performance. Key metrics, including IoU, Precision, Recall, and mAP, along with tools like confusion matrices, contribute to a thorough understanding of the model’s capabilities. Additionally, the model itself serves as a semi-automatic labelling tool.