Precision Livestock Farming: Monitoring Pig Behavioral Characteristics by Using Sensors and Data

A special issue of Veterinary Sciences (ISSN 2306-7381). This special issue belongs to the section "Veterinary Education, Veterinary Communication and Animal Behavior".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 2177

Special Issue Editor


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Guest Editor
Department of Applied Biology, HAS University of Applied Sciences, PO Box 90108, 5200 MA ‘s Hertogenbosch, The Netherlands
Interests: precision technology; data and sensor applications in livestock and companion animals

Special Issue Information

Dear Colleagues,

I hereby invite you to send in contributions for a Special Issue of Veterinary Sciences titled ‘Precision Livestock Farming: Monitoring Pig Behavioral Characteristics Using Sensors and Data’.

Animal welfare is receiving increasing attention, and this especially applies to the welfare of intensively kept livestock such as pigs. Monitoring behavior forms a basis for measuring and improving pig welfare. Many research groups are working on automating the monitoring of pig behavior, and numerous sensors and data applications are being developed. The focus of this Special Issue is precisely this field of research: monitoring pig behavioral characteristics using sensors and data. The scope of the Special Issue includes measuring behavior using sensors and data in sows, piglets, weaned piglets, and fattening pigs. Studies can focus on data applications using (combined) on-farm data from water, feed, weighing, or climate systems, and group or individual data can be analyzed. It may be useful to focus on a specific moment, process, or event in the pigs’ life, such as farrowing, piglet crushing, or weaning. Specific behaviors can be studied, such as tail biting or play behavior, providing insight into the risk factors or conditions on the farm. Behavioral patterns or time budgets can be studied, such as activity budgets, eating time or rest/sleep, to detect anomalies in behavior as a warning signal for stress or disease. Studies can be on applications of well-known sensors in pig farming, such as cameras, microphones, and accelerometers, or new and innovative sensors such as sound cameras or thermographic cameras. Contributions detailing new and innovative data science applications for pig farm data are also very welcome.

The purpose of this Special Issue is to bring together the state-of-the-art advancements in automated measurement of pig behavior and to gather and publish the latest knowledge and experience in this field. This will give our field of research an extra boost and help the sector move forward. I look forward to receiving your contributions.

Dr. Lenny Van Erp-van der Kooij
Guest Editor

Manuscript Submission Information

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Keywords

  • pig behavior
  • welfare
  • activity
  • time budgets
  • behavioral patterns
  • early detection of disease
  • precision livestock farming
  • sensors
  • data

Published Papers (1 paper)

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Research

17 pages, 3206 KiB  
Article
Implementation of Computer-Vision-Based Farrowing Prediction in Pens with Temporary Sow Confinement
by Maciej Oczak, Kristina Maschat and Johannes Baumgartner
Vet. Sci. 2023, 10(2), 109; https://doi.org/10.3390/vetsci10020109 - 2 Feb 2023
Cited by 1 | Viewed by 1800
Abstract
The adoption of temporary sow confinement could improve animal welfare during farrowing for both the sow and the piglets. An important challenge related to the implementation of temporary sow confinement is the optimal timing of confinement in crates, considering sow welfare and piglet [...] Read more.
The adoption of temporary sow confinement could improve animal welfare during farrowing for both the sow and the piglets. An important challenge related to the implementation of temporary sow confinement is the optimal timing of confinement in crates, considering sow welfare and piglet survival. The objective of this study was to predict farrowing with computer vision techniques to optimize the timing of sow confinement. In total, 71 Austrian Large White and Landrace × Large White crossbred sows and four types of farrowing pens were included in the observational study. We applied computer vision model You Only Look Once X to detect sow locations, the calculated activity level of sows based on detected locations and detected changes in sow activity trends with Kalman filtering and the fixed interval smoothing algorithm. The results indicated the beginning of nest-building behavior with a median of 12 h 51 min and ending with a median of 2 h 38 min before the beginning of farrowing with the YOLOX-large object detection model. It was possible to predict farrowing for 29 out of 44 sows. The developed method could reduce labor costs otherwise required for the regular control of sows in farrowing compartments. Full article
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