Hyperspectral imaging and deep learning for parasite detection in white fish under industrial conditions

Abstract Parasites in fish muscle present a significant problem for the seafood industry in terms of both quality and health and safety, but the low contrast between parasites and fish tissue makes them exceedingly difficult to detect. The traditional method to identify nematodes requires removing fillets from the production line for manual inspection on candling tables. This technique is slow, labor intensive and typically only finds about half the parasites present. The seafood industry has struggled for decades to develop a method that can improve the detection rate while being performed in a rapid, non-invasive manner. In this study, a newly developed solution uses deep neural networks to simultaneously analyze the spatial and spectral information of hyperspectral imaging data. The resulting technology can be directly integrated into existing industrial processing lines to rapidly identify nematodes at detection rates (73%) better than conventional manual inspection (50%).