Computer vision based individual fish identification using skin dot pattern

Precision fish farming is an emerging concept in aquaculture research and industry, which combines new technologies and data processing methods to enable data-based decision making in fish farming. The concept is based on the automated monitoring of fish, infrastructure, and the environment ideally by contactless methods. The identification of individual fish of the same species within the cultivated group is critical for individualized treatment, biomass estimation and fish state determination. A few studies have shown that fish body patterns can be used for individual identification, but no system for the automation of this exists. We introduced a methodology for fully automatic Atlantic salmon (Salmo salar) individual identification according to the dot patterns on the skin. The method was tested for 328 individuals, with identification accuracy of 100%. We also studied the long-term stability of the patterns (aging) for individual identification over a period of 6 months. The identification accuracy was 100% for 30 fish (out of water images). The methodology can be adapted to any fish species with dot skin patterns. We proved that the methodology can be used as a non-invasive substitute for invasive fish tagging. The non-invasive fish identification opens new posiblities to maintain the fish individually and not as a fish school which is impossible with current invasive fish tagging.

Hubungan Parameter Biofisik Perairan dengan Hasil Tangkapan Ikan Pelagis Kecil di Perairan Pulau Ternate

This research was experimental fishing methods and regression analysis to examine the effect and relationship between biophysical parameters namely sea surface temperature and chlorophyll-a and the results of small pelagic fish conversations in Ternate Island, the results of the study showed that the biosphysical parameters including sea surface temperature (SPL) and chlorophyll-a have a significant influence on the distribution of small pelagic fish, with the correlation coefficient values of each sea surface temperature parameter and chlorophyll-a with the results of the pelagic fish tagging are 0.6121 and 0.7042