Walleye (Sander vitreus, Mitchill 1818) age and sex classification using innovative supervised and unsupervised machine learning and soft computing methodologies

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Walleye (Sander vitreus) is a freshwater perciform native to Northern America and Canada with high commercial value. Stocking programs for the species are unable to supply the significant demand, creating stock management problems and an ecosystemic decline. For sustainable fisheries, the age and sex estimation are fundamental to marine stock management. Biological methods for age determination and sex estimation were used in the past, conventional observations and techniques are substituted with machine learning methods, currently underused. The aim of this study is to evaluate the performance of an ensemble of Machine Learning (ML) methods for age and sex classification of walleye. Based on the performance of the ensemble models, appropriate evaluation decides on the age criterion with near optimal performance using supervised and unsupervised ML algorithms on the given fish datasets. The innovation of this work lies in the fact that the species age or sex estimation is based on a paramount involvement of models that diminish the proprietary idiosyncrasies of individual methods on specific datasets. For the same models two approaches were used: applied model algorithms developed both in R and Python (Orange Rapid Application Development environment) programming language provided high accuracy percentages. Among all methodologies, the Decision Tree method provided the best classification, for age estimation with 96% and 98% accuracy using the R and Orange models respectively. For the sex classification, Random Forest performed the best using the R-model with 94% accuracy, outperformed by the AdaBoost model in Orange with 97.6% accuracy. When compared, the models which were developed in Python generally performed better than the corresponding R-models both in age and sex classification. Overall, the proposed ML approach is emphasized as a rapid economic solution to standard fish classification with high accuracy, and thus could be integrated in stock assessment for a more sustainable aquaculture management.