Weekly milk prediction on dairy goats using neural networks

Can in vitro embryo production be estimated from semen variables in Senepol breed by using artificial intelligence?

Thoroughly analyzing the sperm and exploring the information obtained using artificial intelligence (AI) could be the key to improving fertility estimation. Artificial neural networks have already been applied to calculate zootechnical indices in animals and predict fertility in humans. This method of estimating the results of reproductive biotechnologies, such as in vitro embryo production (IVEP) in cattle, could be valuable for livestock production. This study was developed to model IVEP estimates in Senepol animals based on various sperm attributes, through retrospective data from 290 IVEP routines performed using 38 commercial doses of semen from Senepol bulls. All sperm samples that had undergone the same procedure during sperm selection for in vitro fertilization were evaluated using a computer-assisted sperm analysis (CASA) system to define sperm subpopulations. Sperm morphology was also analyzed in a wet preparation, and the integrity of the plasma and acrosomal membranes, mitochondrial potential, oxidative status, and chromatin resistance were evaluated using flow cytometry. A previous study identified three sperm subpopulations in such samples and the information used in tandem with other sperm quality variables to perform an AI analysis. AI analysis generated models that estimated IVEP based on the season, donor, percentage of viable oocytes, and 18 other sperm predictor variables. The accuracy of the results obtained for the three best AI models for predicting the IVEP was 90.7, 75.3, and 79.6%, respectively. Therefore, applying this AI technique would enable the estimation of high or low embryo production for individual bulls based on the sperm analysis information.

In vivo prediction of abdominal fat and breast muscle in broiler chicken using live body measurements based on machine learning

The purpose of this study was to predict the carcass characteristics of broilers using support vector regression (SVR) and artificial neural network (ANN) model methods. Data were obtained from 176 yellow feather broilers aged 100-day-old (90 males and 86 females). The input variables were live body measurements, including external measurements and B-ultrasound measurements. The predictors of the model were the weight of abdominal fat and breast muscle in male and female broilers, respectively. After descriptive statistics and correlation analysis, the datasets were randomly divided into train set and test set according to the ratio of 7:3 to establish the model. The results of this study demonstrated that it is feasible to use machine learning methods to predict carcass characteristics of broilers based on live body measurements. Compared with the ANN method, the SVR method achieved better prediction results, for predicting breast muscle (male: R² = 0.950; female: R² = 0.955) and abdominal fat (male: R² = 0.802; female: R² = 0.944) in the test set. Consequently, the SVR method can be considered to predict breast muscle and abdominal fat of broiler chickens, except for abdominal fat in male broilers. However, further revaluation of the SVR method is suggested.

Estimation of milk yield based on udder measures of Pelibuey sheep using artificial neural networks

Udder measures have been used to assess milk yield of sheep through classical methods of estimation. Artificial neural networks (ANN) can deal with complex non-linear relationships between input and output variables. In the current study, ANN were applied to udder measures from Pelibuey ewes to estimate their milk yield and this was compared with linear regression. A total of 357 milk yield records with its corresponding udder measures were used. A supervised learning was used to train and teach the network using a two-layer ANN with seven hidden structures. The globally convergent algorithm based on the resilient backpropagation was used to calculate ANN. Goodness of fit was evaluated using the mean square prediction error (MSPE), root MSPE (RMSPE), correlation coefficient (r), Bayesian’s Information Criterion (BIC), Akaike’s Information Criterion (AIC) and accuracy. The 15–15 ANN architecture showed that the best predictive milk yield performance achieved an accuracy of 97.9% and the highest values of r² (0.93), and the lowest values of MSPE (0.0023), RMSPE (0.04), AIC (− 2088.81) and BIC (− 2069.56). The study revealed that ANN is a powerful tool to estimate milk yield when udder measures are used as input variables and showed better goodness of fit in comparison with classical regression methods.

Visible and Near-Infrared Multispectral Features in Conjunction with Artificial Neural Network and Partial Least Squares for Predicting Biochemical and Micro-Structural Features of Beef Muscles

The objective of this study was to determine the potential of multispectral imaging (MSI) data recorded in the visible and near infrared electromagnetic regions to predict the structural features of intramuscular connective tissue, the proportion of intramuscular fat (IMF), and some characteristic parameters of muscle fibers involved in beef sensory quality. In order to do this, samples from three muscles (Longissimus thoracis, Semimembranosus and Biceps femoris) of animals belonging to three breeds (Aberdeen Angus, Limousine, and Blonde d’Aquitaine) were used (120 samples). After the acquisition of images by MSI and segmentation of their morphological parameters, a back propagation artificial neural network (ANN) model coupled with partial least squares was applied to predict the muscular parameters cited above. The results presented a high accuracy and are promising (R² test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited R²P = 0.99 and RMSEP = 0.103 g × 100 g⁻¹ DM.

Comparison of the decision tree, artificial neural network and multiple regression methods for prediction of carcass tissues composition of goat kids

The aim of this study was to predict carcass tissue composition of goat kids using the decision tree with CHAID algorithm (DT) and artificial neural network (ANN) method in comparison with classical step-wise regression (SWR) analyse. Data were obtained from 57 goat kids of Gokceada breed. Predictor variables were pre-slaughter weight, several carcass measurements and indices, weights of different carcass joints and dressing percentage. R² values ranging from 0.212 to 0.371 indicating low to moderate accuracy were obtained for predicting muscle proportion. DT and ANN yielded similar R² values for predicting bone proportion. DT was the best prediction method for estimating proportions of subcutaneous fat (R² = 0.828) and intermuscular fat (R² = 0.789). According to DT analyses, cold carcass weight was the most important factor influencing bone proportion, while kidney knob and channel fat weight was the predominant factor influencing subcutaneous, intermuscular and total fat proportions. Consequently, the use of DT method can be considered to predict carcass fat proportions.

Designing of an artificial neural network model to evaluate the association of three combined Y-specific microsatellite loci on the actual and predicted postthaw motility in crossbred bull semen

The freezing of bull semen significantly hamper the motility of sperm which reduces the conception rate in dairy cattle. The prediction of postthaw motility (PTM) before freezing will be useful to take the decision on discarding or freezing of the germplasm. The artificial neural network (ANN) methodology found to be useful in prediction and classification problems related to animal science, and hence, the present study was undertaken to compare the efficiency of ANN in prediction of PTM on the basis of the number of ejaculates, volume, and concentration of sperms. The combined effect of Y-specific microsatellite alleles on the actual and predicted PTM was also studied. The results revealed that the prediction accuracy of PTM based on the semen quality parameters was comparatively lower because of higher variability in the data set. The ANN gave better prediction accuracy (34.88%) than the multiple regression analysis models (32.04%). The root mean square error was lower for ANN (8.4353) than that in the multiple regression analysis (8.6168). The haplotype or combined effect of microsatellite alleles on actual and predicted PTM was found to be highly significant (P < 0.01). On the basis of results, it was concluded that the ANN methodology can be used for prediction of PTM in crossbred bulls.

Predicting in vitro rumen VFA production using CNCPS carbohydrate fractions with multiple linear models and artificial neural networks

The objectives of this trial were to develop multiple linear regression (MLR) models and three-layer Levenberg-Marquardt back propagation (BP3) neural network models using the Cornell Net Carbohydrate and Protein System (CNCPS) carbohydrate fractions as dietary variables for predicting in vitro rumen volatile fatty acid (VFA) production and further compare MLR and BP3 models. Two datasets were established for the trial, of which the first dataset containing 45 feed mixtures with concentrate/roughage ratios of 10∶90, 20∶80, 30∶70, 40∶60, and 50∶50 were used for establishing the models and the second dataset containing 10 feed mixtures with the same concentrate/roughage ratios with the first dataset were used for testing the models. The VFA production of feed samples was determined using an in vitro incubation technique. The CNCPS carbohydrate fractions (g), i.e. CA (sugars), CB1 (starch and pectin), CB2 (available cell wall) of feed samples were calculated based on chemical analysis. The performance of MLR models and BP3 models were compared using a paired t-test, the determination coefficient (R2) and the root mean square prediction error (RMSPE) between observed and predicted values. Statistical analysis indicated that VFA production (mmol) was significantly correlated with CNCPS carbohydrate fractions (g) CA, CB1, and CB2 in a multiple linear pattern. Compared with MLR models, BP3 models were more accurate in predicting acetate, propionate, and total VFA production while similar in predicting butyrate production. The trial indicated that both MLR and BP3 models were suitable for predicting in vitro rumen VFA production of feed mixtures using CNCPS carbohydrate fractions CA, CB1, and CB2 as input dietary variables while BP3 models showed greater accuracy for prediction.