Computer vision system using deep learning to predict rib and loin yield in the fish Colossoma macropomum

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Abstract

Computer vision system (CVSs) are effective tools that enable large-scale phenotyping with a low-cost and non-invasive method, which avoids animal stress. Economically important traits, such as rib and loin yield, are difficult to measure; therefore, the use of CVS is crucial to accurately predict several measures to allow their inclusion in breeding goals by indirect predictors. Therefore, this study aimed (1) to validate CVS by a deep learning approach and to automatically predict morphometric measurements in tambaqui and (2) to estimate genetic parameters for growth traits and body yield. Data from 365 individuals belonging to 11 full-sib families were evaluated. Seven growth traits were measured. After biometrics, each fish was processed in the following body regions: head, rib, loin, R + L (rib + loin). For deep learning image segmentation, we adopted a method based on the instance segmentation of the Mask R-CNN (Region-based Convolutional Neural Networks) model. Pearson's correlation values between measurements predicted manually and automatically by the CVS were high and positive. Regarding the classification performance, visible differences were detected in only about 3% of the images. Heritability estimates for growth and body yield traits ranged from low to high. The genetic correlations between the percentage of body parts and morphometric characteristics were favorable and highly correlated, except for percentage head, whose correlations were unfavorable. In conclusion, the CVS validated in this image dataset proved to be resilient and can be used for large-scale phenotyping in tambaqui. The weight of the rib and loin are traits under moderate genetic control and should respond to selection. In addition, standard length and pelvis length can be used as an efficient and indirect selection criterion for body yield in this tambaqui population.

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