Joint research consortium AI-ANIMAL WELFARE

Work Packages 6: Technologies for animal welfare detection

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Further information:

April 2025 to Murch 2029

Prof. Dr.-Ing. Uwe Freiherr von Lukas

Dr.-Ing. Purbaditya Bhattacharya, 
M.Sc. Goutham Ravinaidu

Fraunhofer IGD (Dr.-Ing. Gerald Bieber)

European Regional Development Fund  (ERDF),
funding program “Förderung von anwendungsorientierten Exzellenzforschungsprojekten des Landes Mecklenburg-Vorpommern”

https://ki-tierwohl.de (german)

Abstract


The primary objective of the project is to establish the technological infrastructure required for non-invasive monitoring of animal welfare. To achieve this, the work will integrate and refine existing tools while introducing novel methodologies to ensure objective data collection. A key focus is the implementation of AI-driven automation, designed to streamline operational workflows for staff while simultaneously elevating standards of animal care.

As a partner in this joint research consortium  consisting of multiple work packages, our contribution to AP6 focuses on establishing a robust technological infrastructure for the non-invasive monitoring of animal well-being. The core objective is to engineer intelligent systems capable of assessing the physiological and psychological states of animals without the stress of physical handling and the state of their environment. This initiative specifically targets a diverse range of species, including cattle, pigs, poultry, and laboratory mice. To achieve this, we will collaborate with our project partners: 

  • Universitätsmedizin Rostock (UMR), 

  • Forschungsinstitut für Nutztierbiologie (FBN),

  •  Fakultät für Agrar, Bau, und Umwelt, Universität Rostock (UR-AUF), 

  • Friedrich-Loeffler-Institut (FLI), 

  • Hochschule Neubrandenburg  (HS-NB), 

  • Fraunhofer-Institut für Graphische Datenverarbeitung (IGD), 

  • Rechts- und Staatswissenschaftliche Fakultät, 

  • Universität Greifswald (UG-RSF)

  • Helmholtz Institut für One Health (HIOH) 

  • BioCon Valley®GmbH

on multiple aspects of this project. We will integrate and refine existing sensor technologies while introducing novel, contactless methodologies to ensure objective data collection. By generalizing and combining these approaches, we aim to create a versatile monitoring framework suitable for both agricultural and laboratory settings. A major priority is the use of AI to create data driven robust models as a key part of a streamlined automation pipeline; this is designed not only to streamline operational workflows but also to elevate standards of care by ensuring continuous, undisturbed monitoring of the animals.