Python-based acoustic detection of Penaeus vannamei feeding behavior.

The global expansion of shrimp aquaculture has positioned this sector as a significant contributor to seafood supply. Efficient production hinges on effective feeding management. This study presents Python-based computational tools for the acoustic monitoring of white shrimp (Penaeus vannamei) feeding activities. We compare the proposed Python implementation for automatic detection of shrimp feeding sounds with a reference detection method using Raven Pro®, examining various shrimp sizes and stocking densities. Our comparative analysis shows substantial agreement between the two methods, particularly for shrimp weighing more than 1 g across different density groups. Although Raven Pro® is a well-established bioacoustic software tested under laboratory conditions, the Python-based methods proposed here offer practical applications in real farming scenarios and can be integrated into dedicated hardware. These techniques not only enhance feeding practices but also demonstrate the potential of computational tools in advancing aquaculture management. • A Python method for detection of shrimp acoustic feeding activity is presented. • The proposed method was compared with a reference detector of Raven Pro®. • An agreement was found between click detectors across shrimp sizes and densities. • Python-based detector enables computation in resource-limited aquaculture settings. [ABSTRACT FROM AUTHOR]