FishAgePredictioNet: A multi-stage fish age prediction framework based on segmentation, deep convolution network, and Gaussian process regression with otolith images

Abstract

Fish ageing is a vital component of fisheries management as it enables the evaluation of fish population status and supports the development of sustainable management strategies. However, traditional methods of age determination through otolith analysis by experts are resource-intensive and time-consuming. Therefore, there is a growing demand for more cost-effective and automated techniques to accurately determine fish age. In accordance with this purpose, we proposed a multistage framework for fish age prediction using otolith images. First, otoliths in the images were detected using the Faster Region-based Convolutional Neural Networks (RCNN) model, which includes 8 convolution layers, and the detected otoliths were clipped. Subsequently, using a pre-trained neural network based on the transfer learning approach, deep features were extracted separately from the right and left otolith images, and all the obtained features were combined. Finally, these combined features are given as input to the Gaussian process regression model. To evaluate the performance of the proposed architecture, 4109 images of right and left otoliths belonging to Greenland halibut (flatfish, Reinhardtius hippoglossoides) were used. The proposed architecture produced 1.83 MSE, 0.98 R-squared, 1.35 RMSE, and 10.29 MAPE scores in the experimental studies. As a result, the proposed model achieved superior performance compared with previous studies. Our findings show that our FishAgePredictioNet system could help experts predict fish age based on otolith images.