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Revolutionizing Farming through Robotics, Artificial Intelligence and Satellites

What if you could use a smart handheld device to help you determine when your sheep are pregnant and predict their litter size? Or, what if you could use machine learning to analyze the health of soil conditions so that you could manage inputs like fertilizers, herbicides and fungicides.

And, what if you could make farm machinery safer to operate by using remote control? These are just three of ten projects funded by Alberta Innovates’ Smart Agriculture and Food Digitalization and Automation Challenge (SAFDAC) program.

These research projects, combined with seven more, have been approved in the latest round of SAFDAC funding. Ten research programs will share $3.19 million, looking at the frontiers of farming, agriculture and food production. Other successful projects range from using sensors on bison herds to manage their health and welfare, to examining ways to use artificial intelligence to manage your autonomous machinery.

SAFDAC supports projects to develop or advance smart technologies to increase productivity, reduce production costs or increase the value of Alberta’s agri-food commodities. It does this by looking for innovations that:

  • Develop new applications for digitalization of the agri-food sector,
  • Create autonomous systems and prototype development to reduce costs and improve quality and safety of agriculture and food products,
  • Develop and validate new smart technologies and solutions to reduce the impact of stress to plants and animals – and increase farm productivity and food-supply chain functionality.
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Michigan Cover Crop Decision Tool Update 2026

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This material is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under agreement number 2023-38640-39573 through the North Central Region SARE program under project number ENC23-226. USDA is an equal opportunity employer and service provider. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and should not be construed to represent any official USDA or U.S. Government determination or policy.