Details of the tender:
Project Contract No: 2020-1.1.2-PIACI-KFI-2020-00174
Project title: Full digitalisation of the rabbit meat vertical
Consortium leader: TETRABBIT Food Industry and Trade Limited Liability Company
Aid amount: HUF 673 529 764
Project implementation start date: 01.01.2021.
Project expected completion date: 31.12.2023.
Project leader at the university: Dr. Áron Szenes PhD, MBA, research fellow
The aim of this call for proposals, which is funded by the National Research, Development and Innovation Fund of the National Research, Development and Innovation Office, is to improve the competitiveness of enterprises by supporting their market-oriented research, development and innovation projects.
Our University is participating in the project as a consortium partner alongside Tetrabbit Ltd. The grant of HUF 673,529,764 will be used to fund HUF 222,918,164 for the professional work of the Digital Food Chain Education, Research, Development and Innovation Institute (DÉOKFII), the Department of Pharmacy and Toxicology and the Department of Pathology.
Content of the project:
Significant improvements in productivity and product quality in the agri-food industry can be achieved through the introduction and further development of digitalisation technologies in production. The greatest potential is seen in the area of production management and quality assurance. This is based on the acquisition and central storage of information and the analysis of large-scale databases (big data). The three main stages of production are breeding (genetic improvement and reproduction), meat production (fattening) and food production (slaughter, processing and supply chain).
In the rabbit meat production sector to be developed, production data at group level of the cooperating breeding and fattening partners will be available digitally from the 2000s onwards. An online production information system is now used to record and collect this data. The breeding animals are individually marked with RFID devices and the workers record the data using a smartphone application. As a result of previous successful R&D projects, data from genomic and molecular biological markers are already available.
The second data set is the result of the feed tests. Batch analysis data are also available going back almost two decades. In the project, we plan to carry out detailed analytical testing of each batch delivered. Feed consumption will also be monitored at some sites using automatic measuring cells.
The third data set is the result of veterinary diagnostic tests. Data from more than 5500 surface faecal examinations are available for the monitoring of subclinical coccidiosis.
The fourth data set is meteorological and microclimate data. The internal microclimate of the colonies is strictly controlled and regulated. State-of-the-art computer equipment is used to collect the data. These data can also be easily transferred to a central database.
In the slaughterhouse and in the distribution chain, meat products are also monitored by electronic systems. Batches can be individually identified, and a QR code can be encoded on the packaging to provide the consumer with detailed information on the whole chain.
Data-driven management has been part of our lives for years. Today, we collect a lot of seemingly unrelated data, but data collection systems in all segments are non-existent or independent of each other. To date, no one has investigated interactions such as complex correlations between internal temperature and humidity, feed composition, or changes in genetic stock within critical regulatory limits. Our vision is to integrate the systems that currently exist independently on a common data collection platform, organizing the information into a normalized data warehouse over time. We then cluster and search for co-occurrences using big data analytics and supervised, unsupervised machine learning methods.
We envision a centralized system that would use the measured data to predict the expected material requirements and yields, and to propose optimized microclimate and nutrient requirements for specific livestock, their life cycles, and even for rabbit farms, thus increasing yields and reducing environmental impact. The planned system would act as an advisory system, which could extrapolate the data collected to other breeding operations, thus creating a new service that could be sold abroad as expert knowledge with high added value.
