AI for Agri-food Innovation

Enabling Responsible AI-Driven Agri-Food Innovation in Ontario: Challenges and Opportunities

The adoption of artificial intelligence (AI) can enhance competitiveness and create new market opportunities in Ontairon’s agri-food sector. However, AI-driven AI should cater to responsible innovation that minimizes the challenges of job displacement, increased inequality, ethical concerns, and indifference to adoption. The adoption of AI varies across different sectors and value chains, and skills needed to thrive in the age of AI will vary depending on the specific AI technologies. Therefore, we should envision a future that complements and enhances existing knowledge, skills, and institutional support mechanisms. This research will use a mixed-methods approach to investigate the key factors influencing AI adoption in Ontario’s horticultural and livestock sectors, the associated challenges and opportunities, and the essential skills and knowledge for agri-food workers to thrive. The overall goal is to understand how AI can be used to improve the competitiveness and growth of Ontario’s agri-food sector within a responsible innovation framework.

Legal aspects of dealing with robot and AI tools in climate smart agriculture.

16 December, 2024,

This part was organized as part of the digital development, information integrity and inclusive innovation webinar series of agri-food, climate change and rural misinformation research platform.

Speakers and Panelists include,

Dr. Mahatab Uddin, who holds a Ph.D. in public international law, an adjunct professor and postdoctoral researcher at the University of Guelph. He specializes in climate change law, intellectual property, technology transfer, and sustainable development

   

 

Publications

Assessment of AI Technology Adoption in Ontario: Content Layer Classification
This brief examines AI adoption in Ontario’s horticulture and livestock sectors using descriptive, diagnostic, predictive, and prescriptive analytics. It identifies barriers like connectivity, cost, and data issues, and recommends policies on broadband expansion, financial support, AI training, and data governance to promote integrated, efficient, and sustainable AI use in agriculture.
Dara, Hazrati Fard, and Kaur (2022); Hall, Vinodrai, and Huneke (2024); Leeuwis and Aarts (2011); Lemay and Boggs (2024); Njuguna, Daum, Birner, and Mburu (2025); Püschel (2016); and van Hilten, Ryan, Blok, and de Roo (2025)
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Framework for Understanding AI Adoption in Ontario Agriculture
This brief explores AI adoption in Ontario agriculture, highlighting benefits for productivity and sustainability. It identifies barriers—like costs, skills, and regulations—and uses a multi-level framework to guide policy recommendations for strategic, responsible, and inclusive AI integration.
Ataharul Chowdhury, Associate Professor, and Uduak Ita Edet, PhD Candidate, both from the School of Environmental Design and Rural Development at the University of Guelph.
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    Livestock Research and Innovation Corporation

      

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