AIMC Topic: Milk

Clear Filters Showing 61 to 70 of 126 articles

Machine Learning Based Prediction of Insufficient Herbage Allowance with Automated Feeding Behaviour and Activity Data.

Sensors (Basel, Switzerland)
Sensor technologies that measure grazing and ruminating behaviour as well as physical activities of individual cows are intended to be included in precision pasture management. One of the advantages of sensor data is they can be analysed to support f...

Metabolomics meets machine learning: Longitudinal metabolite profiling in serum of normal versus overconditioned cows and pathway analysis.

Journal of dairy science
This study aimed to investigate the differences in the metabolic profiles in serum of dairy cows that were normal or overconditioned when dried off for elucidating the pathophysiological reasons for the increased health disturbances commonly associat...

Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models.

Computers in biology and medicine
Sub-clinical bovine mastitis decreases milk quality and production. Moreover, sub-clinical mastitis leads to the use of antibiotics with consequent increased risk of the emergence of antibiotic-resistant bacteria. Therefore, early detection of infect...

Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms.

Journal of dairy science
Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like growth factor 1 (IGF-1). These plasma metabolites and metabolic ...

Using machine learning to estimate herbage production and nutrient uptake on Irish dairy farms.

Journal of dairy science
Nutrient management on grazed grasslands is of critical importance to maintain productivity levels, as grass is the cheapest feed for ruminants and underpins these meat and milk production systems. Many attempts have been made to model the relationsh...

Invited review: Hygienic quality, composition, and technological performance of raw milk obtained by robotic milking of cows.

Journal of dairy science
Automatic milking systems (AMS), first introduced on dairy farms in the 1990s, rapidly spread across many countries. This technology is based on the voluntary milking of dairy cattle in a completely automated process, which relies on computer managem...

Classification of cow milk using artificial neural network developed from the spectral data of single- and three-detector spectrophotometers.

Food chemistry
Spectra data from two instruments (UV-Vis/NIR and FT-NIR) consisting of three and one detectors, respectively, were employed in order to discriminate the geographical origin of milk as a way to detect adulteration. Initially, principal component anal...

The effect of pasture quantity temporal variation on milking robot utilization.

Journal of dairy science
In pasture-based automatic milking systems (AMS), a decrease in robot utilization (RU) often occurs in the early morning hours. Novel feeding strategies that encourage voluntary cow traffic throughout 24 h could help mitigate this problem. We determi...

Disposition of ceftizoxime in Staphylococcal mastitis in Indian crossbred cows.

Veterinary journal (London, England : 1997)
Disposition of ceftizoxime was studied in Indian crossbred cows following a single IV dosing in field conditions. Six healthy lactating and six mastitic crossbred cows were assigned to two groups (Group 1 and Group 2). A single IV administration of c...

Estimation of somatic cell count levels of hard cheeses using physicochemical composition and artificial neural networks.

Journal of dairy science
This study addresses the prediction of the somatic cell counts of the milk used in the production of sheep cheese using artificial neural networks. To achieve this objective, the neural network was designed using 33 parameters of the physicochemical ...