Principal Investigator
. In the evolving landscape of aquaculture, maintaining optimal fish health is pivotal for sustainability and productivity. Traditional health monitoring methods are labor-intensive and subject to human error, leading to inconsistencies in early disease detection and treatment. Utilizing the data obtained from James Cook University Singapore Aquaculture Laboratory consisting of videos of seabass (Dicentrarchus labrax) movements. We implemented a YOLOv8-based detection system to facilitate faster and more accurate detection of seabass under typical farming conditions. Our results demonstrate the effectiveness of this approach, achieving a mean Average Precision (mAP) at the 50th percentile intersection over union (IoU) threshold of 0.824 and a mAP between the 50th and 95th percentiles of 0.358. Future work will focus on expanding this detection system to perform real-time behaviour analysis through fish trajectory tracking under various environmental conditions, enhancing early disease detection capabilities.
15 Oct 2024 - 20 Oct 2024
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Seabass fish;Fish Behaviour Analysis;Deep Learning;Early Disease Detection,;Real-time Monitoring
JCU Singapore
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