Accurate Wound and Lice Detection in Atlantic Salmon Fish Using a Convolutional Neural Network

Abstract

The population living in the coastal region relies heavily on fish as a food source due to their vast availability and low cost. This need has given rise to fish farming. Fish farmers and the fishing industry face serious challenges such as lice in the aquaculture ecosystem, wounds due to injuries, early fish maturity, etc. causing millions of fish deaths in the fish aquaculture ecosystem. Several measures, such as cleaner fish and anti-parasite drugs, are utilized to reduce sea lice, but getting rid of them entirely is challenging. This study proposed an image-based machine-learning technique to detect wounds and the presence of lice in the live salmon fish farm ecosystem. A new equally distributed dataset contains fish affected by lice and wounds and healthy fish collected from the fish tanks installed at the Institute of Marine Research, Bergen, Norway. A convolutional neural network is proposed for fish lice and wound detection consisting of 15 convolutional and 5 dense layers. The proposed methodology has a test accuracy of 96.7% compared with established VGG-19 and VGG-16 models, with accuracies of 91.2% and 92.8%, respectively. The model has a low false and true positive rate of 0.011 and 0.956, and 0.0307 and 0.965 for fish having lice and wounds, respectively.

Keywords

• fish wound detection
• lice detection
• aquatic salmon fish
• machine learning
• convolutional neural network