AIMC Topic: Dairying

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Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle.

Genetics, selection, evolution : GSE
BACKGROUND: Recently, artificial neural networks (ANN) have been proposed as promising machines for marker-based genomic predictions of complex traits in animal and plant breeding. ANN are universal approximators of complex functions, that can captur...

Machine learning approaches for the prediction of retained placenta in dairy cows.

Theriogenology
Retained placenta (RP) is a reproductive disorder that causes significant financial losses to the dairy industry. Predicting RP risk in cows post-calving is a challenging task. This study aimed to evaluate the predictive capabilities of five machine ...

Artificial intelligence meets dairy cow research: Large language model's application in extracting daily time-activity budget data for a meta-analytical study.

Journal of dairy science
This study investigates the application of ChatGPT-4 in extracting and classifying behavioral data from scientific literature, focusing on the daily time-activity budget of dairy cows. Accurate analysis of time-activity budgets is crucial for underst...

Time series data analysis to predict the status of mastitis in dairy cows by applying machine learning models to automated milking systems data.

Preventive veterinary medicine
Mastitis in dairy cows is one of the most important issues that not only pose risk to animal health and welfare but also cause huge direct and indirect economic losses to the dairy sector. In recent times, automated milking systems (AMS) have gained ...

Computer vision systems for monitoring hutch-housed dairy calves.

Journal of dairy science
Computer vision systems (CVS) have emerged as a powerful technology for animal monitoring. However, there is limited research on CVS for behavior monitoring of hutch-housed dairy calves, which account for >50% of all calf housing in the United States...

Approaches for Measuring and Predicting Fouling During Thermal Processing of Dairy Solutions.

Comprehensive reviews in food science and food safety
Fouling during the thermal processing of dairy products remains a significant challenge, reducing operational efficiency, increasing energy consumption, and complicating cleaning cycles. This review critically assesses current methods for measuring a...

Modeling enteric methane emission from dairy cows using deep learning approach.

The Science of the total environment
This study explores the application of deep learning (DL) models to predict methane (CH) emissions from enteric fermentation in dairy cows using performance, feeding, behavioral and weather data from automated milking and feeding systems, behavioral ...

Farmers who implemented this, also implemented that: Use of association-rule-learning to improve biosecurity on dairies.

Preventive veterinary medicine
Biosecurity practices are the cornerstone of disease prevention and control programs. In Canada, their implementation is evaluated with a Risk Assessment Questionnaire (RAQ). Association Rule Learning (ARL) - a non-supervised machine learning algorit...

Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning.

Journal of animal science
Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers th...

Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees.

Animal science journal = Nihon chikusan Gakkaiho
The purpose of this paper was to utilize the decision trees technique to determine the factors responsible for high monthly milk yield in Polish Holstein-Friesian cows from 27 herds equipped with milking robots. The applied statistical method-the dec...