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 5312

Special Issue Editors


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 breeding; automatic milking system; animal genetics; milk production; animal reproduction; statistical analysis

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

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

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 9 | Viewed by 4566
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)
Show Figures

Graphical abstract

Back to TopTop