Automated within tank fish mass estimation using infrared reflection system

Fish size and mass information during different growth stages is important for precise feeding regime management, oxygen consumption calculations, antibiotic prescription and improving fish welfare, but also to facilitate decisions on grading, harvesting and time to harvest. The main purpose of this study was to develop an automatic system to estimate fish mass using fish dorsal geometrical features and machine learning algorithms such as random forest (RF) and support vector machine (SVM). To develop the model, Kinect as a RGB-D camera was used to acquire depth map and top view images of 295 farmed seabass (Dicentrarchus labrax, L.) of different sizes. Eight dorsal geometric features were extracted and used for model development. Ten-fold cross validation was used to optimize and validate the models. Comparison of models was made in term of the coefficient of determination (Rcv2) and Root Mean Square Error of prediction (RMSEP) of cross validation. Both models exhibited significant prediction, however, SVM algorithm with Rcv2 of 0.872 (p < 0.01) and RMSE of 0.13 gave a slightly better prediction of weight compared to RF with Rcv2 of 0.868 (p < 0.01) and RMSE of 0.13 was the highest R2. Subsequently, Infrared reflection system (IREF) which is composed of a NIR range camera with an external illuminator, was also used in this study to acquire 20 fish dorsal images inside the tank. Like validation results, both algorithms had the significant prediction, however, despite the validation results, RF with R2=0.84p<0.01andRMSE= 0.12 was better in comparison to SVM with R2=0.72(p<0.01)andRMSE= 0.16. The study demonstrated that seabass geometrical dorsal features together with machine learning algorithms could be used for mass predictions. Furthermore, the IREF system can be used as a reliable, inexpensive, stress-free and accurate sensor for monitoring and estimating fish mass during cultivation within the tank.