Vision system for tuna species classification aboard fishing vessels

Abstract

To ensure the sustainable management of fisheries, the precise monitoring of fish stocks is required to maintain their long-term reproductive capacity and prevent the risk of overfishing. This paper explores advanced methods for the detection and classification of tuna species onboard fishing vessels, using machine learning and computer vision systems. These practices enable automated identifications of various species of tuna, providing critical data to support sustainable fishing practices. By integrating such tools into fishing operations, this approach aims to enhance both the accuracy and efficiency of stock assessments, and contribute to the preservation of marine ecosystems.