Advanced, trustworthy AI and data solutions for individualised automated milking & feeding of dairy cows

Informazioni relative al progetto

dAIry 4.0

ID dell’accordo di sovvenzione: 101119714

DOI

10.3030/101119714

Data della firma CE 26 Giugno 2023

Data di avvio 1 Ottobre 2023

Data di completamento 31 Marzo 2027

Finanziato da

• Digital, Industry and Space

Costo totale

€ 3 533 843,75

Contributo UE

€ 2 957 356,25

Coordinato da

CY.R.I.C CYPRUS RESEARCH AND INNOVATION CENTER LTD
Cyprus

Periodic Reporting for period 2 - dAIry 4.0 (Advanced, trustworthy AI and data solutions for individualised automated milking & feeding of dairy cows)

Periodo di rendicontazione: 2024-10-01 al 2025-09-30

Sintesi del contesto e degli obiettivi generali del progetto

Producing more with fewer resources and less impact on society, animal welfare, climate and biodiversity is essential for modern dairy farming. Automated Milking Systems (AMS) have greatly enhanced farm profitability by reducing manual labour and costs while enhancing animal welfare, milk quality and farm management. The dAIry 4.0 project advances AMS by integrating AI-driven tools that allow individualised and optimised milking, feeding, and gating control to improve system performance. New sensing systems in the AMS enable general health inspection of the animal even at the single teat level and support milk quality evaluation, partitioning milk into separate tanks. Additionally, a novel Decision Support System (DSS) leverages multimodal learning techniques to generate recommendations for managing each cow, allowing farmers to adopt more precise and proactive management practices.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

During this second reporting period work focused on progress in each individual module. In the vision module, the new cameras for rumen motility and eye temperature have been calibrated and tested. The requirements for these cameras along with the cameras already existed in the LELY's milking robot Astronaut A5 have been identified; setting up the scene for the 1st integration into the milking robot enabling real-time monitoring. The milking control module also progressed adjusting the pulsation settings and the vacuum settings for each cow. The feeding control module can predict the amount of the concentrate each cow needs, visualising the feed efficiency. The sample preparation module, with the new purchased homogeniser and the upgraded multi-stage filtration system have been integrated into the unified milk quality analyser. The data from all these modules are feeding the overall Animal Status Classifier, providing initial classification results for each cow (Healty, At risk, Requiring immediate attention). The DSS progressed also, identifying risks and thresholds, enabling the farmers to be notified with recommendations for the overall farm management. The simulator extracting data from the six pilot farms, as well as historical data from these farms, can provide a 5-year projection simulation for the farm. All these data-based modules, as well as the results (data) from the vision module, the milk analyser and the actuation modules will be integrated into the new dAIry 4.0 data platform, which during this second reporting period, progressed well with the early development of the User Interface, as well as with some preliminary data from the farm (e.g the milk yield).

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

dAIry 4.0 project aims to go beyond the current state of the art by focusing on the individualised, AI-driven optimization of milking and feeding processes at the single animal and single teat level. By leveraging advanced AI and data analytics, the system will implement adaptive milking settings for improved udder health and reduced CO2 emissions. Additionally, AI-powered individualised feeding strategies will optimize nutritional balance and cost efficiency, enhancing both animal welfare and farm sustainability. Early progress will also include the development of robust animal health and welfare monitoring systems that provide reliable, actionable insights, integrating novel data techniques and AI models for real-time analysis.

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