AIMC Topic: Breeding

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Artificial intelligence and porcine breeding.

Animal reproduction science
Livestock management is evolving into a new era, characterized by the analysis of vast quantities of data (Big Data) collected from both traditional breeding methods and new technologies such as sensors, automated monitoring system, and advanced anal...

Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens.

Genes
Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increa...

TSML: A New Pig Behavior Recognition Method Based on Two-Stream Mutual Learning Network.

Sensors (Basel, Switzerland)
Changes in pig behavior are crucial information in the livestock breeding process, and automatic pig behavior recognition is a vital method for improving pig welfare. However, most methods for pig behavior recognition rely on human observation and de...

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore c...

A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds.

Animal : an international journal of animal bioscience
Single nucleotide polymorphisms (SNPs) able to describe population differences can be used for important applications in livestock, including breed assignment of individual animals, authentication of mono-breed products and parentage verification amo...

A comparison of machine learning and logistic regression in modelling the association of body condition score and submission rate.

Preventive veterinary medicine
The effect of body condition score (BCS) on reproductive outcomes is complex, dynamic and non-linear with interaction and confounding. The flexibility inherent in machine learning algorithms makes them attractive for analysing complex data. This stud...

Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle.

Journal of dairy science
In this study, we compared multiple logistic regression, a linear method, to naive Bayes and random forest, 2 nonlinear machine-learning methods. We used all 3 methods to predict individual survival to second lactation in dairy heifers. The data set ...

Machine-learning algorithms to identify key biosecurity practices and factors associated with breeding herds reporting PRRS outbreak.

Preventive veterinary medicine
Investments in biosecurity practices are made by producers to reduce the likelihood of introducing pathogens such as porcine reproductive and respiratory syndrome virus (PRRSv). The assessment of biosecurity practices in breeding herds is usually don...

Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.

Plant physiology
Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in m...

Predicting Growth and Carcass Traits in Swine Using Microbiome Data and Machine Learning Algorithms.

Scientific reports
In this paper, we evaluated the power of microbiome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured predicti...