Project description
Tech-driven farming for a sustainable future
The agricultural sector is grappling with the challenge of increasing production while minimising the adverse effects of this on society, climate and biodiversity. There is a growing need for climate-friendly sustainable agriculture. Automated milking systems (AMS) have proven beneficial by reducing manual labour, cutting costs and enhancing milk quality. The EU-funded dAIry 4.0 will revolutionise AMS by integrating AI, data and robotics solutions. By leveraging multimodal learning techniques, self-supervised data augmentation and novel explainable AI as well as actively involving farmers in the process, the project seeks to optimise AMS production while minimising its impact on the environment and animal welfare. It promises to be a game changer for both the farming sector and the food industry.
Objective
The agricultural sector has a big challenge: producing more with fewer raw materials and less adverse effects on society, production animals, climate and biodiversity. Optimal use of resource is even more important now, due to the imminent food crisis. Climate-friendly sustainable agriculture, with care for natural resources, is essential for our food production and quality of life, today and for future generations.
Automated Milking Systems (AMS) were developed in the late 20th century under the perspective of reducing manual labour & costs and improving quality of life for the farmers. Not only have these machines improved in harvesting milk efficiently, but they also have the added ability to collect a greater amount of data about production, milk composition, cows health and behaviour. This could allow producers to make more informed management decisions, while in parallel reducing emissions and increasing animal welfare.
Nevertheless, currently available AMS have important limitations in terms of optimising their operation.
dAIry 4.0 addresses these challenges, integrating and optimising AI, data and robotics solutions to demonstrate how this combination will optimise AMS production aspects and minimise adverse effects on society, climate and biodiversity. The approach will be demonstrated through real-world use cases of interest both for the farming sector and the food industry. In terms of AI tools to be used, the project will focus on the following novelties:
- Developing multimodal learning techniques to efficiently utilize multiple types of information for animal health & overall animal status monitoring
- Developing self-supervised and novel data augmentation techniques to reduce the amount of labelled training data needed
- Exploring novel explainable AI techniques to increase transparency of the system and eventually facilitate acceptance by the users
- Including the farmer in the loop to build the cognitive abilities for the system
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- agricultural sciences animal and dairy science dairy
- natural sciences biological sciences ecology ecosystems
- agricultural sciences agriculture, forestry, and fisheries agriculture
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering robotics
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback. You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
HORIZON.2.4 - Digital, Industry and Space
MAIN PROGRAMME
See all projects funded under this programme -
HORIZON.2.4.5 - Artificial Intelligence and Robotics
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-IA - HORIZON Innovation Actions
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-CL4-2022-DIGITAL-EMERGING-02
See all projects funded under this callCoordinator
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
1060 Lefkosia
Cyprus
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.