Identifying losers: Automatic identification of growth-stunted salmon in aquaculture using computer vision

Abstract

During the production of salmonids in aquaculture, it is common to observe growth-stunted individuals. The cause for the so-called “loser fish syndrome” is unclear, which needs further investigation. Here, we present and compare computer vision systems for the automatic detection and classification of loser fish in Atlantic salmon images taken in sea cages. We evaluated two end-to-end approaches (combined detection and classification) based on YoloV5 and YoloV7, and a two-stage approach based on transfer learning for detection and an ensemble of classifiers (e.g., linear perception, Adaline, C-support vector, K-nearest neighbours, and multi-layer perceptron) for classification. To our knowledge, the use of an ensemble of classifiers, considering consolidated classifiers proposed in the literature, has not been applied to this problem before. Classification entailed the assigning of every fish to a healthy and a loser class. The results of the automatic classification were compared to the reliability of human classification. The best-performing computer vision approach was based on YoloV7, which reached a precision score of 86.30%, a recall score of 71.75%, and an F1 score of 78.35%. YoloV5 presented a precision of 79.7%, while the two-stage approach reached a precision of 66.05%. Human classification had a substantial agreement strength (Fleiss’ Kappa score of 0.68), highlighting that evaluation by a human is subjective. Our proposed automatic detection and classification system will enable farmers and researchers to follow the abundance of losers throughout the production period. We provide our dataset of annotated salmon images for further research.

Keywords

• Aquaculture
• Growth-stunted fish
• Fish welfare
• Machine learning
• Object detection
• Object classification