Automatic Milking Systems: Latest Advances and Prospects

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Livestock Farming Technology".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 6418

Special Issue Editors


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Guest Editor
Department of Biotechnology and Animal Genetics, Faculty of Animal Breeding and Biology, UTP University of Science and Technology, Bydgoszcz 85-084, Poland
Interests: cattle breeding; automatic milking system; animal genetics; milk production; animal reproduction; statistical analysis
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Biotechnology and Animal Genetics, Faculty of Animal Breeding and Biology, UTP University of Science and Technology, 85-084 Bydgoszcz, Poland
Interests: genetics; animal production; milk production; milking systems; automatic milking system
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Biotechnology and Animal Genetics, Faculty of Animal Breeding and Biology, UTP University of Science and Technology, 85-084 Bydgoszcz, Poland
Interests: cattle and sheep breeding; genetic parameters and assessment of breeding value; automatic cattle milking system; statistical modeling; data mining techniques

Special Issue Information

Dear Colleagues,

We would like to invite you to contribute to a Special Issue to the MDPI journal AgriEngineering. The title of the Issue is “Automatic Milking Systems: Application, Innovation, and Prospects”.

In the 1980s, very intensive development of precision farming occurred, mainly due to the implementation of information technology and the introduction of GPS systems. In cattle breeding, this process was initiated in the early 1970s when automation in cow identification begun. Work on the computerization of herd management also begun during that time. One of the examples of precision livestock farming used today in dairy farming is the automatic milking system (AMS).

AMS provides an opportunity to obtain access to a considerably greater data set compared to conventional milking systems (CMS). In herds that are equipped with AMS software collects data from each milking in the robot, including parameters that are not easily measured in CMS. Moreover, farmers posses a group of tools and indicators that help monitor the animals in the herd in real-time. This saves time and money, but requires the breeders to expand their knowledge and skillfully use it to improve cow performance. The information obtained can be used mainly to improve the welfare and health of animals, improve the herd's milk yield and milk quality.

Currently, there is a strong need to work on the potential of AMS as a tool to predict and optimize herd production levels, as well as to predict future health issues of milked animals.

This Special Issue focuses on the latest finding in AMS research, engineering, and management solutions in all fields of livestock farming. This Special Issue will focus on the most recent advances in the research areas that include, but are not limited to, the following:

  • AMS as a tool to predict future milking
  • Automatic milking system
  • Animal health
  • Animal welfare
  • Cow
  • Livestock farming
  • Management systems
  • Milk parameters
  • Milk production
  • Milking speed
  • Modeling variability of the lactation curves
  • Online Somatic Cell Count Estimation
  • Precision livestock farming
  • Pros and cons of AMS
  • Quality of milk
  • Robots
  • Somatic cell score
  • Voluntary milking system

Dr. Beata Sitkowska
Dr. Magdalena Kolenda
Dr. Dariusz Piwczyński
Guest Editors

Manuscript Submission Information

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Keywords

  • automatic milking system
  • animal health and welfare
  • animal milk production
  • optimization of milk yield
  • prediction

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Published Papers (1 paper)

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Research

9 pages, 1051 KiB  
Article
MasPA: A Machine Learning Application to Predict Risk of Mastitis in Cattle from AMS Sensor Data
by Naeem Abdul Ghafoor and Beata Sitkowska
AgriEngineering 2021, 3(3), 575-583; https://doi.org/10.3390/agriengineering3030037 - 4 Aug 2021
Cited by 17 | Viewed by 5411
Abstract
Mastitis is a common disease that prevails in cattle owing mainly to environmental pathogens; they are also the most expensive disease for cattle in dairy farms. Several prevention and treatment methods are available, although most of these options are quite expensive, especially for [...] Read more.
Mastitis is a common disease that prevails in cattle owing mainly to environmental pathogens; they are also the most expensive disease for cattle in dairy farms. Several prevention and treatment methods are available, although most of these options are quite expensive, especially for small farms. In this study, we utilized a dataset of 6600 cattle along with several of their sensory parameters (collected via inexpensive sensors) and their prevalence to mastitis. Supervised machine learning approaches were deployed to determine the most effective parameters that could be utilized to predict the risk of mastitis in cattle. To achieve this goal, 26 classification models were built, among which the best performing model (the highest accuracy in the shortest time) was selected. Hyper parameter tuning and K-fold cross validation were applied to further boost the top model’s performance, while at the same time avoiding bias and overfitting of the model. The model was then utilized to build a GUI application that could be used online as a web application. The application can predict the risk of mastitis in cattle from the inhale and exhale limits of their udder and their temperature with an accuracy of 98.1% and sensitivity and specificity of 99.4% and 98.8%, respectively. The full potential of this application can be utilized via the standalone version, which can be easily integrated into an automatic milking system to detect the risk of mastitis in real time. Full article
(This article belongs to the Special Issue Automatic Milking Systems: Latest Advances and Prospects)
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