Automated image analysis as a tool to measure individualised growth and population structure in Chinook salmon (Oncorhynchus tshawytscha)

Abstract

Abstract Selective breeding programmes depend on high‐quality measurements of phenotype and genotype with repeated individualised phenotype measurements throughout the life cycle being optimal. Recent advances in electronics and computer vision technologies offer opportunities to improve both the quality, quantity and individualisation of repeated phenotypic measurements, but remain underutilised in aquaculture breeding programmes. In this study, we compare manual measurements of phenotypic traits of Chinook salmon (Oncorhynchus tshawytscha) with digital images and an automated software analysis pipeline written in the Python® programming language using the OpenCV machine vision library. Manual measurements of length, girth and weight of passive integrated transponder‐tagged individuals were compared with image‐based measures of 738 individuals over a time span from June–December 2019. Linear regressions showed strong correlations between manual and automated measurements for fork length, girth and weight (R2 = 0.989, R2 = 0.918, R2 = 0.987, respectively). Image‐based software measurements proved powerful for tracking general population changes in growth over the study period while retaining insights about subpopulations deviating from the average (e.g. losing weight). Taken together, our study demonstrates that image‐analysis can be used to estimate fish growth traits with a high degree of precision, requires reduced labour and demonstrates that additional knowledge can be gained through tracking individuals throughout production to harvest.

Keywords

• breeding programme
• computer vision
• growth
• morphometric software
• population dynamics
• salmon