Stream 4 – Automation and Digital Technologies in the Agriculture and Agri-food Sector

From: Innovation, Science and Economic Development Canada

Winning Applicant: Canadian Agri-Food Automation and Intelligence Network (CAAIN)

Project Name: Canadian Agri-Food Automation and Intelligence Network (CAAIN)

The project proposes to accelerate the automation and digitization of Canada’s agricultural sector. CAAIN will bring together a network of partners – spearheaded by Alberta Innovates and Vineland Research and Innovation Centre – to increase the competitiveness of our agricultural sector and reduce financial risk for Canadian farmers. The network will establish a smart farm platform to develop and validate technologies that will enable automation of agricultural tasks. Canada’s strengths in artificial intelligence (AI) and precision agriculture will be leveraged and merged to develop scalable, exportable farming solutions.

News Release

Key Outcomes:

Establish an ecosystem for innovation

This project establishes an ecosystem for strong collaboration between technology providers, research institutes, and the agricultural sector to accelerate automation and digitization of Canada’s agriculture sector. The network will help connect partners across Canada to drive new growth in the agriculture sector and increase the competitiveness of Canadian farmers.

Invest in innovation to boost competitiveness

The project activity aligns with Canada’s Agri-Food Economic Strategy Table, to invest in innovation and boost competitiveness through increased automation and digitization. Industry will work with academia to develop automated technologies, processes, modules and machines to advance the use of automation and robotics in agriculture and food.

Integrate data streams

The project will merge state-of-the-art sensor data, such as computer vision and internet of things, with traditional agriculture data, such as soil sampling data, to form a comprehensive data stream for analysis. With this integrated data stream, AI deep learning can be applied to identify patterns, optimize decisions, identify problems and propose solutions to those problems. The data, captured in constructs such as blockchain, will be used to report on food provenance as well as environmental and social sustainability.

The development of a repository of data will allow researchers and technology partners to develop tools that will benefit and strengthen Canada’s agriculture and agri-food sector.

Participants

A starting composition of 8-members across 5 provinces including:

  • Alberta Innovates
  • Vineland Research and Innovation Centre
  • Olds College
  • Lakeland College
  • Linamar Corp.
  • MDA
  • DOT Technology Corp
  • TrustBIX
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