Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques

In this study, predictions of length-weight relationships and meat productivity were generated by machine learning models using measurement data of male and female crayfish in the narrow-clawed crayfish population living in Apolyont Lake. The data set was created using the growth performance and morphometric characters obtained from 1416 crayfish in different years to determine the length-weight relationship and length-meat yield. Statistical methods, artificial intelligence, and machine learning are used due to the difficulty of constructing mathematical models in multi-parameter and multivariate problems. In recent studies, artificial intelligence and machine learning methods give higher accuracy than other prediction methods in large data sets with complex structures. No previous studies have been conducted on such population parameters. The analysis results show that most of the models designed as an alternative to traditional estimation methods in future planning studies in sustainable fisheries, aquaculture, and natural sources management are valid for machine learning and artificial intelligence. Seven different machine learning algorithms were applied to the data set and the length-weight relationships and length-meat yields were evaluated for both male and female individuals. Support Vector Regression (SVR) has achieved the best prediction performance with 0.996 and 0.992 values for the length-weight of males and females, with 0.996 and 0.995 values for the length-meat yield of males and females. The results showed that the SVR outperforms the others for all scenarios regarding the accuracy, sensitivity, and specificity metrics.