Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Zoom in on the precision livestock farming
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.ORCID iD: 0000-0003-2817-5331
2021 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Inzoomning på precisionsdjurhållning (Swedish)
Abstract [en]

Global attention to the welfare of zoo animals and livestock results in stronger legislation and higher pressure for achieving higher standards of animal welfare. Monitoring and understanding animal behaviour can assist in optimising the welfare of zoo and livestock animals. Precision livestock farming solutions open the door to increase automation of behaviour monitoring and welfare management. The overall purpose of the thesis was to investigate the possibilities of using computer vision and sensor technology for studying animal behaviour in zoo and production environments. To fulfil this overall purpose, two main research questions were addressed: How can we identify and track individual animals using computer vision and sensor technology? Combining the identity and position information, how well animal behaviour can be monitored and analysed?

First, we developed and justified methods for identifying and tracking individual animals in different livestock environments: zoo outdoor environment, sheep barn and free-stall dairy cattle barn's indoor production environment. Three methods were developed to identify and track individual animals: a combination of radio frequency identification and camera sensor, a deep learning method based on visual biometrics and behaviour features and an ultra-wideband based real-time location system method. The data quality, in terms of missing data, in one commercially available ultra-wideband system was examined. The choice of method was justified according to different species' natural appearance, breeding strategy and housing conditions. We found that the computer vision system can perform as good as an expert in identifying individual bears based on images. The real-time location system can provide the position of individual animals inside barns with a mean error under 0.4 m. No major obstacles were found to interfere with the ultra-wideband based real-time location system. The between-cow variation was statistically significant.

Second, two animal behaviour monitoring systems that assist activity registration and analysing social interactions were proposed. To detect sheep's standing and lying behaviour in sheep barn environments, infrared radiation cameras, and three-dimensional computer vision technology were used. Dairy cows' negative and positive social interactions were analysed using a Long-term Recurrent Convolution Networks model. Both systems integrated the real-time location system and computer vision system to perform identification, tracking and analysing animal behaviour tasks. Working with real systems in a real-world application setting made the study more credible and valuable for the related research. The result showed that the system was able to understand animal standing lying activity and social behaviour.

The developed technologies and the results of the experiments added value for the animal behaviour monitoring by focusing on individual or sub-group in a herd and analysing individual activity and social behaviour continuously. By understanding animal behaviour, it can push the continuous surveillance system towards a welfare decision support system.

Place, publisher, year, edition, pages
Umeå: Umeå University , 2021. , p. 57
Keywords [en]
precision livestock farming, computer vision, sensor fusion, object tracking, real-time location system, animal behaviour, machine learning
National Category
Embedded Systems Signal Processing Genetics and Breeding in Agricultural Sciences
Identifiers
URN: urn:nbn:se:umu:diva-187085ISBN: 978-91-7855-607-6 (print)ISBN: 978-91-7855-608-3 (electronic)OAI: oai:DiVA.org:umu-187085DiVA, id: diva2:1589834
Public defence
2021-09-30, Triple Helix, Samverksanshuset, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2021-09-09 Created: 2021-09-01 Last updated: 2021-09-01Bibliographically approved
List of papers
1. Tracking and identification of animals for a digital zoo
Open this publication in new window or tab >>Tracking and identification of animals for a digital zoo
2010 (English)In: Proceedings of the 1st IEEE/ACM Internet of Things Symposium, 18-20 December 2010, Hangzhou, China., 2010Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present our approach to use a combination of radio frequency identification (RFID) and a wireless camera sensor network to identify and track animals at a zoo. We have developed and installed 25 cameras covering the whole zoo. The cameras are totally autonomous and they are configuring themselves in a wireless ad-hoc network. At strategic locations RFID readers are deployed to identify animals in close proximity. The camera network deployed in the zoo is continuous tracking animals in its field of view. By using data fusion from the camera system and the RFID readers we can get semi-continuous tracking of individual animals. The camera network has been running in the zoo for more than one year and about 5 000 hours of video has been captured and recorded. This will give us a very large dataset for offline development and testing of computer vision algorithms for animal detection and tracking.

Identifiers
urn:nbn:se:umu:diva-38026 (URN)
Conference
the 1st IEEE/ACM Internet of Things Symposium, 18-20 December 2010, Hangzhou, China.
Projects
Digital Djurpark
Available from: 2010-11-22 Created: 2010-11-22 Last updated: 2021-09-01Bibliographically approved
2. Identifying Individual Bears in a Zoo Setting Using Visual Biometrics and Behaviour Information
Open this publication in new window or tab >>Identifying Individual Bears in a Zoo Setting Using Visual Biometrics and Behaviour Information
(English)Manuscript (preprint) (Other academic)
National Category
Signal Processing
Identifiers
urn:nbn:se:umu:diva-186633 (URN)
Available from: 2021-08-16 Created: 2021-08-16 Last updated: 2021-11-01
3. A sensor-fusion-system for tracking sheep location and behaviour
Open this publication in new window or tab >>A sensor-fusion-system for tracking sheep location and behaviour
Show others...
2020 (English)In: International Journal of Distributed Sensor Networks, ISSN 1550-1329, E-ISSN 1550-1477, Vol. 16, no 5Article in journal (Refereed) Published
Abstract [en]

The growing interest in precision livestock farming is prompted by a desire to understand the basic behavioural needs of the animals and optimize the contribution of each animal. The aim of this study was to develop a system that automatically generated individual animal behaviour and localization data in sheep. A sensor-fusion-system tracking individual sheep position and detecting sheep standing/lying behaviour was proposed. The mean error and standard deviation of sheep position performed by the ultra-wideband location system was 0.357 +/- 0.254 m, and the sensitivity of the sheep standing and lying detection performed by infrared radiation cameras and three-dimenional computer vision technology were 98.16% and 100%, respectively. The proposed system was able to generate individual animal activity reports and the real-time detection was achieved. The system can increase the convenience for animal behaviour studies and monitoring of animal welfare in the production environment.

Place, publisher, year, edition, pages
Sage Publications, 2020
Keywords
Precision farming, behaviour detection, UWB positioning and tracking, depth camera, infrared camera, computer vision
National Category
Computer Engineering
Identifiers
urn:nbn:se:umu:diva-173842 (URN)10.1177/1550147720921776 (DOI)000536464700001 ()2-s2.0-85084635348 (Scopus ID)
Available from: 2020-08-03 Created: 2020-08-03 Last updated: 2023-03-24Bibliographically approved
4. Tracking and analysing social interactions in dairy cattle with real-time locating system and machine learning
Open this publication in new window or tab >>Tracking and analysing social interactions in dairy cattle with real-time locating system and machine learning
2021 (English)In: Journal of systems architecture, ISSN 1383-7621, E-ISSN 1873-6165, Vol. 116, article id 102139Article in journal (Refereed) Published
Abstract [en]

There is a need for reliable and efficient methods for monitoring the activity and social behaviour in cows, in order to optimise management in modern dairy farms. This research presents an embedded system that could track individual cows using Ultra-wideband technology. At the same time, social interactions between individuals around the feeding area were analysed with a computer vision module. Detections of the dairy cows' negative and positive interactions were performed on foreground video stream using a Long-term Recurrent Convolution Networks model. The sensor fusion system was implemented and tested on seven dairy cows during 45 days in an experimental dairy farm. The system performance was evaluated at the feeding area. The real-time locating system based on Ultra-wideband technology reached an accuracy with mean error 0.39 m and standard deviation 0.62 m. The accuracy of detecting the affiliative and agonistic social interactions reached 93.2%. This study demonstrates a potential system for monitoring social interactions between dairy cows.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Computer vision, Dairy cows, Machine learning, Social interactions, Ultra-wideband
National Category
Animal and Dairy Science Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:umu:diva-182927 (URN)10.1016/j.sysarc.2021.102139 (DOI)000663315100023 ()2-s2.0-85104583225 (Scopus ID)
Available from: 2021-05-20 Created: 2021-05-20 Last updated: 2023-09-05Bibliographically approved
5. Where do we find missing data in a commercial real-time location system?: Evidence from 2 dairy farms
Open this publication in new window or tab >>Where do we find missing data in a commercial real-time location system?: Evidence from 2 dairy farms
2021 (English)In: JDS Communications, ISSN 2666-9102, Vol. 2, no 6, p. 345-350Article in journal (Refereed) Published
Abstract [en]

Real-time indoor positioning using ultra-wideband devices provides an opportunity for modern dairy farms to monitor the behavior of individual cows; however, missing data from these devices hinders reliable continuous monitoring and analysis of animal movement and social behavior. The objective of this study was to examine the data quality, in terms of missing data, in one commercially available ultra-wideband–based real-time location system for dairy cows. The focus was on detecting major obstacles, or sections, inside open freestall barns that resulted in increased levels of missing data. The study was conducted on 2 dairy farms with an existing commercial real-time location system. Position data were recorded for 6 full days from 69 cows on farm 1 and from 59 cows on farm 2. These data were used in subsequent analyses to determine the locations within the dairy barns where position data were missing for individual cows. The proportions of missing data were found to be evenly distributed within the 2 barns after fitting a linear mixed model with spatial smoothing to logit-transformed proportions (mean = 18% vs. 4% missing data for farm 1 and farm 2, respectively), with the exception of larger proportions of missing data along one of the walls on both farms. On farm 1, the variation between individual tags was large (range: 9–49%) compared with farm 2 (range: 12–38%). This greater individual variation of proportions of missing data indicates a potential problem with the individual tag, such as a battery malfunction or tag placement issue. Further research is needed to guide researchers in identifying problems relating to data capture problems in real-time monitoring systems on dairy farms. This is especially important when undertaking detailed analyses of animal movement and social interactions between animals.

Place, publisher, year, edition, pages
Elsevier, 2021
National Category
Computer Sciences Embedded Systems
Identifiers
urn:nbn:se:umu:diva-186646 (URN)10.3168/jdsc.2020-0064 (DOI)
Funder
Swedish Research Council Formas, 2019-02276Swedish Research Council Formas, 2019-02111
Available from: 2021-08-16 Created: 2021-08-16 Last updated: 2022-03-10Bibliographically approved

Open Access in DiVA

fulltext(1593 kB)482 downloads
File information
File name FULLTEXT01.pdfFile size 1593 kBChecksum SHA-512
553d132e32341b48460e6a3b601b9b3d0b4ca7ec060f84e31b6d5e0432a59e3304c9f4d16d258a2e1993c7a7264a03c807ac6c84d2f9a59cb27656fbecbfa963
Type fulltextMimetype application/pdf
spikblad(138 kB)49 downloads
File information
File name SPIKBLAD01.pdfFile size 138 kBChecksum SHA-512
132149803a1b86d3b180ef0cfb4c1883827407cebf869547e9b1ca3f4df9f0662fa889869fc50af7d6646d2b383358d271b45411bd48545df900f3527aa57b55
Type spikbladMimetype application/pdf

Authority records

Ren, Keni

Search in DiVA

By author/editor
Ren, Keni
By organisation
Department of Applied Physics and Electronics
Embedded SystemsSignal ProcessingGenetics and Breeding in Agricultural Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 488 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 1434 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf