AIMC Topic: Dairying

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Deep learning pose estimation for multi-cattle lameness detection.

Scientific reports
The objective of this study was to develop a fully automated multiple-cow real-time lameness detection system using a deep learning approach for cattle detection and pose estimation that could be deployed across dairy farms. Utilising computer vision...

Monitoring the respiratory behavior of multiple cows based on computer vision and deep learning.

Journal of dairy science
Automatic respiration monitoring of dairy cows in modern farming not only helps to reduce manual labor but also increases the automation of health assessment. It is common for cows to congregate on farms, which poses a challenge for manual observatio...

Comparing State-of-the-Art Deep Learning Algorithms for the Automated Detection and Tracking of Black Cattle.

Sensors (Basel, Switzerland)
Effective livestock management is critical for cattle farms in today's competitive era of smart modern farming. To ensure farm management solutions are efficient, affordable, and scalable, the manual identification and detection of cattle are not fea...

Livestock Identification Using Deep Learning for Traceability.

Sensors (Basel, Switzerland)
Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy f...

Lightweight individual cow identification based on Ghost combined with attention mechanism.

PloS one
Individual cow identification is a prerequisite for intelligent dairy farming management, and is important for achieving accurate and informative dairy farming. Computer vision-based approaches are widely considered because of their non-contact and p...

Accurate detection of dairy cow mastitis with deep learning technology: a new and comprehensive detection method based on infrared thermal images.

Animal : an international journal of animal bioscience
Mastitis is one of the most common diseases in dairy cows and has a negative impact on their welfare and life, causing significant economic losses to the dairy industry. Many attempts have been made to develop a detection method for mastitis using th...

Validation of a deep learning-based image analysis system to diagnose subclinical endometritis in dairy cows.

PloS one
The assessment of polymorphonuclear leukocyte (PMN) proportions (%) of endometrial samples is the hallmark for subclinical endometritis (SCE) diagnosis. Yet, a non-biased, automated diagnostic method for assessing PMN% in endometrial cytology slides ...

Individual dairy cow identification based on lightweight convolutional neural network.

PloS one
In actual farms, individual livestock identification technology relies on large models with slow recognition speeds, which seriously restricts its practical application. In this study, we use deep learning to recognize the features of individual cows...

Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows.

Scientific reports
Bovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms...

Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning.

Scientific reports
Streptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate th...