Identification of Shark Species Based on Their Dry Dorsal Fins through Image Processing

Shark populations worldwide have suffered a decline that has been primarily driven by overexploitation to meet the demand for meat, fins, and other products human consumption. International agreements, such as CITES, are fundamental regulating international trade of shark specimens and/or ensure their survival. The present study suggests algorithms identify dry fins 37 species participating in fin from 14 countries, demonstrating high sensitivity specificity image processing. first methodology used non-linear composite filter using Fourier transform each species, we obtained 100% specificity. second was neural network achieved an efficiency 90%. proved be most robust because it supported lower-quality images (e.g., noise background); can recognize independent rotation scale, taking processing times order few seconds fins. Thus, implementation this approach support governments complying with CITES regulations preventing illegal trade.