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PLFDoc: Precision Livestock Farming

Maciej Oczak (ORCID: 0000-0003-1045-7568)
  • Grant DOI 10.55776/DFH34
  • Funding program doc.funds.connect
  • Status Ongoing
  • Start October 1, 2023
  • End September 30, 2027
  • Funding amount € 1,029,586

Disciplines

Computer Sciences (50%); Animal Breeding, Animal Production (50%)

Keywords

  • Livestock,
  • Computer Vision,
  • Sensors,
  • Animal Health,
  • Animal Welfare,
  • Artificial Intelligence
Abstract

The doctoral program PLFDoc - Precision Livestock Farming (PLFDoc) is a collaborative project involving the TU Wien, the University of Applied Sciences Upper Austria (Campus Hagenberg), and the University of Veterinary Medicine Vienna. This interdisciplinary training program offers five PhD candidates the opportunity to work as a team on contributing to a more sustainable agriculture with improved livestock farming. The research focus consists of applied basic research, particularly in the application of methods of Explainable Artificial Intelligence (XAI) as well as image and video analysis (Computer Vision, CV), to be used for monitoring livestock. Recent advancements in sensor technologies, computer vision, and data science provide the potential for continuous, real-time monitoring of the health status and well- being of animals, both at the herd and individual level. The application of these tools thus holds the potential to detect behavioral and/or physiological deviations in animals early and to initiate appropriate countermeasures to maintain or enhance health and well-being. This is not only relevant for farmers and veterinarians but also of societal significance. PLFDoc aims to develop new CV- and XAI-based tools, including prediction of parturition, animal identification, and estimation of individual water and feed intake in pigs and cattle. The development of these tools requires the combination of expertise from fields such as veterinary medicine, agricultural sciences, behavioral biology, data science, and engineering. The collaborative research by experts from these disciplines is therefore of utmost importance and forms the research framework for the doctoral program. As part of a team, the PhD candidates pursue common goals and learn from each other. The faculty members of the doctoral program possess the respective expertise needed for optimal personal support of the candidates and for conducting the described research activities. Due to the interdisciplinary approach, the doctoral candidates receive training in a variety of areas. The theoretical, scientific, experimental, networking, and communication skills acquired in PLFDoc are essential foundations for a successful future career in industry or academia.

Consortium
  • Allan Hanbury, Technische Universität Wien
    consortium member (01.03.2026 -)
  • Maciej Oczak, Veterinärmedizinische Universität Wien
    consortium member (01.07.2026 -)
  • Margrit Gelautz, Technische Universität Wien
    consortium member (01.10.2023 -)
  • Stephan M. Winkler, FH Oberösterreich
    consortium member (01.10.2023 -)
  • Viktoria Dorfer, FH Oberösterreich
    consortium member (01.10.2023 -)
Research institution(s)
  • Veterinärmedizinische Universität Wien
International project participants
  • Tomas Norton, Katholieke Universiteit Leuven - Belgium
  • Michael Iwersen, Ludwig Maximilians-Universität München - Germany

Research Output

  • 3 Citations
  • 14 Publications
  • 8 Datasets & models
  • 1 Disseminations
  • 1 Scientific Awards
  • 2 Fundings
Publications
  • 2026
    Title Using posture classification for detecting calving related behaviour in dairy cows
    Type Conference Proceeding Abstract
    Author Gosch
    Conference 59th Annual Conference Physiology and Pathology of Reproduction and 51st Joint Conference on Veterinary and Human Reproductive Medicine, Munich, Germany.
    Link Publication
  • 2026
    Title Vision-based detection of pain and nest-building behaviors in sows within commercial farrowing pens
    Type Conference Proceeding Abstract
    Author Helf
    Conference Artificial Intelligence 4 Animal Science 2026, Ghent, Belgium
  • 2026
    Title Visions-based detection of pain and nest-building behaviors in sows within commercial farrowing pens
    Type Conference Proceeding Abstract
    Author Helf
    Conference Austrian Symposium on AI, Robotics, and Vision
    Pages 267-272
    Link Publication
  • 2025
    Title How toMeasure Explainability andInterpretability ofMachine Learning Results; In: Genetic Programming Theory and Practice XXI
    DOI 10.1007/978-981-96-0077-9_18
    Type Book Chapter
    Publisher Springer Nature Singapore
  • 2026
    Title Skeleton integrity: A method for the efficient fine-tuning of pose estimation models for pigs
    DOI 10.1016/j.biosystemseng.2025.104380
    Type Journal Article
    Author Brunner D
    Journal Biosystems Engineering
  • 2026
    Title Improved and interpretable accelerometer-based farrowing prediction
    DOI 10.1016/j.biosystemseng.2025.104381
    Type Journal Article
    Author Mayrhuber E
    Journal Biosystems Engineering
  • 2025
    Title Towards Automated and Interpretable Decision Support Systems for Precision Livestock Farming Using Evolutionary Computing
    DOI 10.1145/3712255.3734314
    Type Conference Proceeding Abstract
    Author Mayrhuber E
    Pages 2600-2603
    Link Publication
  • 2025
    Title 14. Leveraging Vision Mamba for cow pose estimation
    DOI 10.1016/j.anscip.2025.08.169
    Type Journal Article
    Author Daadouch S
    Journal Animal - Science proceedings
    Pages 515-517
  • 2025
    Title 89. Evaluation of YOLO-Pose for cattle pose estimation from videos
    DOI 10.1016/j.anscip.2025.08.244
    Type Journal Article
    Author Zhao K
    Journal Animal - Science proceedings
    Pages 658-660
  • 2025
    Title 136. Pose classification for detection of calving related behavior in dairy cows
    DOI 10.1016/j.anscip.2025.08.291
    Type Journal Article
    Author Gosch M
    Journal Animal - Science proceedings
    Pages 745-747
  • 2025
    Title Explainable Artificial Intelligence and Computer Vision as Powerful Tools for Precision Livestock Farming.
    Type Conference Proceeding Abstract
    Author Brunner
    Conference 18. Forschungsforum der österreichischen Fachhochschulen
    Pages 552-553
    Link Publication
  • 2025
    Title Automated Body Measurement of Sows in Feeding Stations Using Multiple Cameras
    Type Conference Proceeding Abstract
    Author Helf
    Conference 1st EAAP Conference on Artificial Intelligence 4 Animal Science, 2025, Zurich, Switzerland
    Pages 63
    Link Publication
  • 2024
    Title Exploring the Potential of Symbolic Regression in Precision Livestock Farming.
    Type Conference Proceeding Abstract
    Author Mayrhuber
    Conference 11th European Conference on Precision Livestock Farming, Bologna, Italy 9 - 12 September 2024
    Pages 1324-1331
  • 2024
    Title Skeleton-based computer vision algorithms for detecting behaviour of dairy cows prior to calving
    Type Conference Proceeding Abstract
    Author Gosch
    Conference 11th European Conference on Precision Livestock Farming. Bologna, Italy.
    Pages 274-283
    Link Publication
Datasets & models
  • 0 Link
    Title Pre-farrowing activity dataset
    Type Database/Collection of data
    Public Access
    Link Link
  • 0
    Title YOLO Pose models trained for cattle pose estimation
    DOI 10.1016/j.anscip.2025.08.244
    Type Computer model/algorithm
    Public Access
  • 0 Link
    Title Pre-farrowing behavior dataset
    DOI 10.34749/3061-1466.2026.36
    Type Database/Collection of data
    Public Access
    Link Link
  • 0 Link
    Title Cow calving postures dataset
    DOI 10.1016/j.anscip.2025.08.291
    Type Database/Collection of data
    Public Access
    Link Link
  • 2026 Link
    Title Pig pose estimation dataset
    DOI 10.17632/sn49zt6jpw.1
    Type Database/Collection of data
    Public Access
    Link Link
  • 2025 Link
    Title Vision Mamba-based Keypoint R-CNN model for cow pose estimation
    DOI 10.1016/j.anscip.2025.08.169
    Type Computer model/algorithm
    Public Access
    Link Link
  • 2025 Link
    Title MLP classifyer for classifying calving-related body-, head- and tail postures of cows approaching parturition
    DOI 10.1016/j.anscip.2025.08.291
    Type Computer model/algorithm
    Public Access
    Link Link
  • 2025 Link
    Title K-nearest neighbors (KNN) classifyer for classifying calving-related body-, head- and tail postures of cows approaching parturition
    DOI 10.1016/j.anscip.2025.08.291
    Type Computer model/algorithm
    Public Access
    Link Link
Disseminations
  • 2025
    Title Presentation to the industry partner - memeber of the industry advisory board of PLFDoc
    Type A talk or presentation
Scientific Awards
  • 2025
    Title Frontiers in Precision Livestock Farming Award
    Type Poster/abstract prize
    Level of Recognition Continental/International
Fundings
  • 2026
    Title Computer vision assisted phenotyping, early detection and genetic analysis of abnormal behavior in weaned piglets
    Type Research grant (including intramural programme)
    Start of Funding 2026
    Funder BMLUK - Federal Ministry of Agriculture and Forestry, Climate and Environmental Protection, Regions and Water Management
  • 2025
    Title VetIdeas challenge - 3rd place for PhD student of PLFDoc - Mathias Gosch
    Type Travel/small personal
    Start of Funding 2025
    Funder University of Veterinary Medicine Vienna

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