AIMC Topic: Dairying

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Artificial intelligence-based dairy cattle behavior recognition for estrus detection via ensemble fusion of two camera views.

PloS one
Monitoring cattle behavior plays an important role in improving farm productivity, maintaining animal welfare, and supporting efficient management practices. This study presents a multi-view behavior recognition system that uses synchronized top-view...

Predicting performance traits in Murrah buffaloes using machine learning: a comparative approach.

Tropical animal health and production
This study evaluated the comparative performance of nine machine learning (ML) algorithms for predicting 305-day first lactation milk yield (305FLMY) and total milk yield (TMY) in Murrah buffaloes. Data from 657 animals recorded over 24 years (2000-2...

End-to-end deep SAE-DNN model for predicting Egyptian buffalo calf sex, weight, and daily milk yield.

Tropical animal health and production
In the present study, a novel stacked Sparse Autoencoder-Deep Neural Network (SAE-DNN) learning prediction model was applied to predict calf sex, weight, and daily milk yield for dairy buffalo. First, SAE stage extracts the unique statistical feature...

Classification of individual dairy cow behaviors using accelerometer, gyroscope, and integrated sensor models.

BMC veterinary research
BACKGROUND: Automated behavior monitoring is increasingly important in precision dairy farming, supporting early disease detection, welfare assessment, and productivity optimization. Although accelerometers effectively detect postural changes, they h...

Optimizing selective breeding of livestock and forage crops to reduce the environmental impacts of grass-based dairy production by combining life cycle assessment and machine learning.

The Science of the total environment
Global demand for ruminant milk-based products is increasing, contributing to increases in associated environmental impacts. Yet, most efforts to reduce the total environmental impact of dairy production are based on livestock breeding and manipulati...

Improving bovine disease detection through multilabel classification.

Scientific reports
R1.C1: The dairy industry is a cornerstone of global food production and economic development; yet, its productivity is frequently hindered by common bovine health issues, including lameness, mastitis, metritis, and foot-and-mouth disease. These cond...

Determination of milk yield in water buffaloes using multi-class logistic regression and machine learning methods.

Tropical animal health and production
In this study, Random Forest, Gradient Boosting Machines (GBM), and Support Vector Machines (SVM), Multi-Class Logistic Regression (MCLR) models were comparatively evaluated for the prediction of milk yield in water buffaloes. The study's main purpos...

Monitoring of milking routines for dairy cows using a computer vision system: A diagnostic accuracy study.

Journal of dairy science
The primary objective was to assess the performance of a computer vision system for the detection of reattachment and manual removal of the milking unit, as well as the assessment of the preparation lag time of the milking routine. The secondary obje...

Leveraging unsupervised machine learning techniques for detecting outliers in the daily milk yield data of dairy cows.

Journal of dairy science
The lactation curve is essential for developing effective feeding plans, optimizing breeding, and strategizing milk production for dairy farms. However, health disorders, as well as external factors such as heat stress, dietary changes, and certain m...

Association of artificial intelligence-predicted milk yield residuals to behavioral patterns and transition success in multiparous dairy cows.

Journal of dairy science
Data-driven health monitoring based on milk yield has shown potential to identify health-perturbing events during the transition period. As a proof of principle, we explored the association between the cow's residual milk yield, that is, the differen...