Project identification number: 2024-1.1.1-KKV_FÓKUSZ-2024-00032
The project will be implemented in cooperation with a consortium, with the help of the following partners:
- Mezőfalvai Tejhasznú Korlátolt Felelősségű Társaság
- Mezőfalvai Húshasznú Korlátolt Felelősségű Társaság
- University of Veterinary Medicine, Budapest
Amount of fund (HUF): 737 423 060 HUF
Total project cost (HUF): 1 043 840 320 HUF
Start of implementation of the project: 01.02.2025.
Planned deadline for the physical completion of the project: 31.01.2027.
DETAILED CONTENT OF THE PROJECT
The goal of the consortium of Mezőfalvai Tejhasznú Kft., Mezőfalvai Húshasznú Kft. and the University of Veterinary Medicine is to create the PractiCow Beef beef cattle monitoring system within the framework of the project. In recent years, the consortium leader Mezőfalvai Tejhasznú Kft. has successfully introduced a monitoring system for the herd of barn-raised dairy cattle partly by purchasing market solutions and partly by further developing and expanding them. Together with the functionality identified as the PractiCow system, the company gained very positive experiences and the sister company’s request was to introduce a similar system for the extensive cattle herd, however, based on the market survey, it turned out that some of the solutions that can be called immature, provide a fraction of the functionality related to dairy cattle there is no such advanced technology on the market. Building on the experience gained during the introduction and development of the PractiCow function group, the consortium undertakes no less than the application of the PractiCow functions to the beef area and the expansion, adaptation and development of the necessary missing functions in the beef cattle sector, a technology that is already widely used in the dairy sector. create.
Within the framework of the project, we use partially existing sensor systems available on the market as a basis (CowScout), which, together with the functional extensions applicable to dairy cattle carried out in close connection with this (Image processing-based condition detection), forms the basis of the development at TRL4 level. Similar to other animal monitoring systems, the goal is to create a three-layer solution, the lower level of which is the sensors and the raw data generated in them, the middle level is the data processing structures suitable for providing targeted and defined sub-functions, and the third level is based on the data content of the entire system, revealed during the project there are business intelligence functions mapping animal health, ethological and animal welfare professional relationships. In terms of the operation of the system, we have defined several data sources, such as the CowScout sensor system that has already been successfully used on dairy cattle, the image processing systems adapted to beef cattle within the project and further developed according to the expectations associated with extensive keeping, the individual sensors based on proximity measurement sensor technology that will be developed within the framework of the project individual interaction sensors, as well as weather stations measuring local weather parameters and sensor systems suitable for assessing the condition of pastures. The raw data content of the sensor solutions is processed separately for each data source, which offers users useful services by function group (e.g.: detection of signs of estrous ability, disease, automatic condition classification, automatic lameness detection, detection of anomalies related to cow-calf interactions, automatic detection of pasture depletion detection, bloodline survey support, etc.). According to one of our hypotheses, which is the scientific basis of the project and data mining, based on the simultaneous and combined examination and use of the raw data and derived data of each functional group level service, it is possible to develop business intelligence system functions that can be used to increase the operational reliability of the sub-functions and which together are suitable for additional information to generate based on the data that goes beyond the framework of sub-functions.
