Age Assessment Using Chum Salmon Scale by Neural Networks and Image Processing

Abstract

In its long history, chum salmon has been propagated by hatching and stocking. Age assessment is necessary to monitor the migration status of chum salmon, and it is desirable to increase the number of assessments in order to obtain precise and accurate knowledge about salmon as a resource. This study introduced an automated age assessment system that uses information technologies, such as artificial neural networks and image processing, to improve efficiency compared to manual assessment. Specifically, we first developed a method for extracting scale regions from scale replica sample images and creating a database. We then used a resting zone detection technique based on semantic segmentation, allowing us to automate evaluation using scaled images as part of the age assessment method. Age assessment from resting zone images was realized by an image processing method based on the skills of the staff of the Japanese Fisheries Research Institute, such as an age counting method using circular structures. Experimental results show that our proposed method outperforms other approaches; salmon age assessment was accurate to within an error of 0.5 years. Our method encompasses automatic data processing of scale images, resting zone detection, and age assessment, and will contribute to the efficiency of the Fisheries Research Institute in terms of both work efficiency and salmon data research.

Keywords

• Age assessment
• scale analysis
• fisheries data
• deep learning
• neural network
• image processing