Computer vision in aquaculture: a case study of juvenile fish counting. Issue 1 (1st January 2023) - Babu, Krishna Moorthy; Bentall, Daniel; Ashton, David T.; Puklowski, Morgan; Fantham, Warren; Lin, Harris T.; Tuckey, Nicholas P. L.; Wellenreuther, Maren; Jesson, Linley K.
ABSTRACT: In aquaculture breeding or production programmes, counting juvenile fish represents a considerable cost in terms of the human hours needed. In this study, we explored the use of two state-of-the-art machine learning architectures (Single Shot Detection, hereafter SSD and Faster Regions with convolutional neural networks, hereafter Faster R-CNN) to augment a manual image-based juvenile fish counting method for the Australasian snapper ( Chrysophrys auratus ) bred at The New Zealand Institute for Plant and Food Research Limited. We tested model accuracy after tuning for confidence thresholds and non-maximal suppression overlap parameters, and implementing a bias correction using a Poisson regression model. Validation of image data showed that after tuning, bias-corrected SSD and Faster R-CNN models had mean absolute percent errors (MAPE) of less than 10%, with SSD having MAPE of less than 5%. Comparison of the results with those from manual counts showed that, while manual counts are slightly more accurate (MAPE = 1.56), the machine learning methods allow for more rapid assessment of counts and thus facilitating a higher throughput. This work represents a first step for deploying machine learning applications to an existing real-life aquaculture scenario and provides a useful starting point for further developments, such as real-time counting of fish or collecting additional phenotypic data from the source images.