RESEARCH PROJECT: KUHTRACKING (COW TRACKING)
Health Monitoring in the Livestock Industry
In agricultural livestock farming, especially in dairy farming, there is a particularly strong trend both internationally and nationally towards the use of technology to monitor animal health and to cope with herd management more broadly. Funded by the FFG, we are conducting research together with Mechatronik Austria on an artificial intelligence-supported camera-based solution for monitoring cows.
Do not Lose Track of Your Stable
More than 34% of all Austrian cow farms were operated on a part-time basis in 2020. Although these small farms have a long tradition in Austria, they are now facing completely new challenges - especially due to demographic changes in rural areas. Tasks that used to be shared by a large family are now taken on by a few. This often results in the workload exceeding the benefits of part-time livestock farming. In the long run, this can endanger this traditional form of agriculture in Austria. As a consequence, the loss of this special bond between the locals and their rural communities may increase the rural exodus. In general, while there are now mechanized aids for many tasks in livestock management, one of the most time-consuming tasks still cannot be automated (at least for small part-time farmers): keeping an eye on the animals. Many essential aspects of cow management rely on farmer’s observation; from determining that a cow is in heat and should have insemination arranged by the veterinarian, to calving.
Monitoring of All Relevant Parameters
The systems currently available are limited to parameters that can be recorded using sensors. The necessary hardware for these systems can quickly become cost-intensive, especially for part-time farmers. With our automated, camera-based solution, classic behavior such as oestrus, calving, ruminating or the movement activity of the cows can be recorded. Other informative information such as feed intake, lying and standing time, staging areas, as well as the general behavior of the animals can be systematically recorded without gaps throughout the entire life of the cattle. The results of the video analysis form a scoring system for cows, where physio- and pathological processes can be identified. The goal of such a system is improved profitability, better husbandry conditions and improved animal health. In the future, these data will be used to feed a comprehensive herd management system that will allow animal welfare to be quantified.
What are cognify & Mechatronik Austria Researching in this Project?
How Our Customers Benefit
Advantages of “KuhTracking” for farmers:
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Automated herd monitoring and alerting in case of need
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Comprehensive dashboard and convenient alerting via smartphone
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Detection of critical behavior such as oestrus or calving
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Early detection of diseases and associated reduction of veterinary costs
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Cost-effective and easy-to-use solution without hardware on the animal
More About KuhTracking
You are interested or you are farmer who wants to take part in the pilot project? Come this way!
About Our Partner: Mechatronik Austria
MECHATRONIK AUSTRIA is an independent engineering and software development company that has made a name for itself through its technical expertise and sophisticated digital solutions in robotics. The company has established itself as a valued partner within the industry through its expertise and service quality. The goal: to be able to offer and implement everything related to digitization - from a single source for the whole of Austria. The company is now active throughout Austria and Germany and employs 25 people.
About Our Funding Provider: FFG
The Austrian Research Promotion Agency (FFG) is the national funding institution for business-related research and development in Austria. FFG grants make a significant contribution to generating new knowledge, developing new products and services and thus being more competitive on the global market. They facilitate or enable the financing of research and innovation projects and thus help to cushion research risk.
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