AIMC Topic: Cattle

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Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals.

Sensors (Basel, Switzerland)
Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation...

Machine Learning-Based Prediction of Cattle Activity Using Sensor-Based Data.

Sensors (Basel, Switzerland)
Livestock monitoring is a task traditionally carried out through direct observation by experienced caretakers. By analyzing its behavior, it is possible to predict to a certain degree events that require human action, such as calving. However, this c...

Accuracy of early pregnancy diagnosis and determining pregnancy loss using different biomarkers and machine learning applications in dairy cattle.

Theriogenology
This study aimed to compare the accuracy of IFN-τ stimulated gene abundance (ISGs) in peripheral blood mononuclear cells (PBMCs), CL blood perfusion by Doppler ultrasound (Doppler-US), plasma concentration of P4 on Day 21 and pregnancy-associated gly...

Metrisor: A novel diagnostic method for metritis detection in cattle based on machine learning and sensors.

Theriogenology
The Metrisor device has been developed using gas sensors for rapid, highly accurate and effective diagnosis of metritis. 513 cattle uteri were collected from abattoirs and swabs were taken for microbiological testing. The Metrisor device was used to ...

An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem.

International journal of molecular sciences
The serious drawback underlying the biological annotation of whole-genome sequence data is the p >> n problem, which means that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). We propose a wa...

DeepOCR: A multi-species deep-learning framework for accurate identification of open chromatin regions in livestock.

Computational biology and chemistry
A wealth of experimental evidence has suggested that open chromatin regions (OCRs) are involved in many critical biological activities, such as DNA replication, enhancer activity, and gene transcription. Accurately identifying OCRs in livestock speci...

DairyCoPilot-Automated data compilation and analysis tools for DairyComp data assets.

PloS one
Modern dairy farm management requires meaningful data and careful analysis to maximize profitability, cow health, and welfare. Current data platforms, such as DairyComp, lack robust integrated data analysis tools. Producers and consultants need dedic...

Automatic segmentation of lower limb muscles from MR images of post-menopausal women based on deep learning and data augmentation.

PloS one
Individual muscle segmentation is the process of partitioning medical images into regions representing each muscle. It can be used to isolate spatially structured quantitative muscle characteristics, such as volume, geometry, and the level of fat inf...

Automated dairy cattle lameness detection utilizing the power of artificial intelligence; current status quo and future research opportunities.

Veterinary journal (London, England : 1997)
Lameness represents a major welfare and health problem for the dairy industry across all farming systems. Visual mobility scoring, although very useful, is labour-intensive and physically demanding, especially in large dairies, often leading to incon...

Machine learning methods for genomic prediction of cow behavioral traits measured by automatic milking systems in North American Holstein cattle.

Journal of dairy science
Identifying genome-enabled methods that provide more accurate genomic prediction is crucial when evaluating complex traits such as dairy cow behavior. In this study, we aimed to compare the predictive performance of traditional genomic prediction met...