Digital image analysis as a tool for phenotyping in Nile tilapia selective breeding
Authors:   , Cardoso Alex Júnio da Silva 
Published: July 2022

Phenotyping is an important step for successful animal selective breeding. Computer vision systems, such as digital image analysis, paired with machine learning (e.g., artificial neural networks, ANN), have the potential to be used in precision aquaculture and genetic improvement programs. Digital image analyses are suitable for determining morphometric traits (e.g., length, height, and width) and traits difficult to measure using traditional techniques, such as body areas, while reducing animal handling. Furthermore, images can provide explanatory variables for posterior prediction of growth, carcass, and fillet traits through ANN models. Therefore, we aimed to (i) develop a fast and straightforward method to measure the length, height, and body areas of Nile tilapia using digital image analysis, (ii) estimate genetic parameters for these traits, and (iii) apply image traits in machine learning algorithms to predict body weight (BW), carcass weight (CW), fillet weight (FW), and fillet yield (FY). The fish used in the study belonged to the 10th and 11th generation of the Nile tilapia breeding program (TILAMAX strain) of the Universidade Estadual de Maringá. In the first study, 656 fish (366 days old at harvest, BW of 414 ± 98 g) were photographed and subjected to image analysis to measure the trunk area (TA), head area (HA), caudal fin area (CFA), and fillet area (FA). Heritability estimates (h 2 ) for BW, TA, HA, CFA, and FA were 0.25, 0.23, 0.26, 0.21, and 0.25, respectively. Genetic correlations between the traits were positive and high, ranging from 0.70 to 0.98. We highlight the genetic correlation between BW and TA (r G = 0.98) and FA (r G = 0.97). Given the observed results, it can be concluded that selecting for body areas obtained by digital image analysis can lead to indirect genetic gains in weight and other areas. However, genetic correlations of these body areas with fillet weight and fillet yield were unknown. For the second study, 1,161 fish (427 days old at harvest, BW of 1,093 ± 346 g) were photographed. Body lengths (3 sections), heights (5 sections), TA, HA, FA, and total area (TOT) were measured from the coordinate values (x and y values in the center of each pixel) of 20 pre-set landmarks on the surface of fish images using the free R software. The proposed method allowed to measure 12 traits in 46 s. The h 2 for lengths and heights were moderate to high, ranging from 0.22 to 0.37. The h 2 values for TA, HA, FA, and TOT were 0.26, 0.35, 0.25, and 0.27, respectively. Positive and moderate to high genetic correlations were observed between morphometric traits and BW (0.66 to 0.98), FW (0.50 to 0.91), and CW (0.77 to 0.98). We highlight the genetic correlation of TA with BW (r G = 0.98), FW (r G = 0.91), and CW (r G = 0.96). The TA/TOT ratio showed a positive and moderate genetic correlation (0.54) with FY. We investigated five supervised machine learning methods for predicting BW, CW, FW, and FY using image traits: multiple linear regression, feed-forward artificial neural network, deep learning, Bayesian regularization for feed-forward neural networks, and random forests. To verify the effectiveness of prediction methods, we used a 10-fold cross-validation procedure with 5 replicates, and the folds were randomly split to provide the training (n = 1045) and validation (n = 116) datasets. Pearson’s correlation coefficient (r), mean absolute error (MAE), and root mean square error (RMSE) between predicted and observed values were calculated. In general, the Bayesian regularization model showed better performance and accuracy in predicting BW (r = 0.99, MAE = 39.54, RMSE = 54.70), CW (r = 0.98, MAE = 27.82, RMSE = 40.03), and FW (r = 0.96, MAE = 23.26, RMSE = 33.42). For FY prediction, all evaluated models had low performance and accuracy (r = 0.29, MAE = 1.55, RMSE = 2.24). The findings demonstrate that digital image analysis is a promising tool for measuring morphometric traits in Nile tilapia, given its non-invasive nature, fast operation, and low cost. Additionally, it was found that body areas can be used as selection criteria, particularly in future studies on body shape changes, with positively correlated responses to FW and positive, albeit lower, correlations with FY. Finally, the Bayesian regularization for the feed-forward neural network method showed the best performance in predicting BW, CW, and FW in Nile tilapia from image traits as predictor variables. Keywords: Morphometric traits. Computer vision. Genetic parameters. Irregular polygons. Artificial neural networks.