The hammerhead sharks (family Sphyrnidae), particularly found in the waters of Indonesia, are iconic species recognized by their distinctive cephalofoil head. These sharks play a critical role in marine ecosystems as apex predators, yet most species in this family are listed as "Critically Endangered" on the IUCN Red List due to overfishing and habitat degradation. Their body parts, such as fins and carcasses are highly sought after in local and international markets, leading to significant pressure on their populations. Accurate species identification is essential for monitoring trade and implementing conservation efforts. However, traditional identification methods are often subjective, time-consuming, and reliant on expert knowledge.

This study, branded as SeeShark, addresses these challenges by developing a computer vision-based identification tool tailored for hammerhead shark species in the TPI Brondong, Lamongan, East Java. Using images of carcasses, fins, teeth, and skin collected from markets, the project aims to create a robust dataset for training a machine learning model. The performance of this model will be evaluated against traders' identification accuracy to assess its efficiency and reliability. By providing a fast, cost-effective, and scalable identification tool, this research not only supports sustainable trade monitoring but also contributes to the conservation of hammerhead sharks by enhancing data accuracy and fostering awareness of their critically endangered status

Monitor the progress of this project at www.seeshark.org