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It seems all economic sectors are now talking about how artificial intelligence (AI) and machine learning will change the way business is done. The term “AI” describes a wide spectrum of research and innovation applications for which machines carry out tasks that once only humans could do. Machine learning is one facet of AI in which machines “learn” to interpret data and make predictions based on what they discover.

AI can be used in so many aspects of agriculture. Below is a list of current commercial activities in this sphere.

Autonomous vehicles and robots: In Western Canada, we have the SeedMaster DOT Platform (see page 30), an autonomous vehicle that can be integrated with other functional units such as sprayers or seeders. Internationally, companies are working with robots to harvest delicate produce such as strawberries and grapes. Progress is being made in both greenhouse and field applications.

Crop and soil monitoring: Many companies are combining image capture and visual analysis with machine learning to assess crop nutrient and water status in combination with smart systems to
identify insect and disease issues.

Predictive analytics and precision farming: Research is being done to track and predict crop yield and profitability based on multiple environmental factors including weather, planting and harvest dates, market opportunities, quality optimization and other crop variables.

Automation in food processing systems: Equipment identifies and removes foreign objects and poor-quality produce within factory systems. Such automated technology is just the beginning as high-efficiency food manufacturing systems develop.

Traceability and consumer trust initiatives: Citizens want as much information as possible from all parts of the agri-food value chain. AI tools can deliver such confidence in the food system through the power of data harnessed by applications such as blockchain (see page 12) and other shared ledger tools.

New research tools: As the amount of data increases in activities such as plant breeding programs, there is opportunity to mine information from genomics, proteomics and other processes to identify plants and animals with specific traits.

The Faculty of Agriculture, Life and Environmental Sciences at the University of Alberta has engaged in identifying AI opportunities that can provide value to farmers, processors, retailers and others in the agri-food value chain. In January, I was asked to participate in a Microsoft workshop in Redmond, WA, where the company unveiled its new FarmBeats platform, which aims to make farmers more productive by improving their access to data. The initiative will enable data-driven farming by creating new means to move data from the field in the absence of internet availability.

I have also talked to employees of IBM Watson who are creating the Watson Decision Platform for Agriculture, which addresses yield forecasts, disease and pest indicators and crop health monitoring. This division of IBM is also studying preferred consumer taste profiles and developing unique recipes based on data harvested from social media and other sources.

Numerous small companies are also exploring agri-food opportunities presented by AI applications. An advantage for Western Canada is the presence of the Alberta Machine Intelligence Institute (Amii), which is affiliated with the University of Alberta. It is a remarkable research and consulting group that has helped make the university the fourth-ranked educational institution in the world in research productivity within the areas of AI and machine learning. This is according to the University of Massachusetts, Amherst’s Computer Science Rankings.

The world is taking notice. Citing the presence of Amii, Google’s Deep Mind chose Edmonton as the location of its first laboratory outside of the United Kingdom. There is a tremendous opportunity to connect these talented and motivated researchers with innovators across the agri-food sector to identify big problems where big data can provide the solutions our industry needs.

But we cannot wait too long. Universities and research groups across the world are hiring researchers in digital agriculture and predictive analytics—the practice of extracting information from existing data sets to determine patterns and predict future trends and outcomes. The global agri-food sector will be defined by the companies that find their competitive edge in new technologies including AI. We need to ensure they see value in working with us in Western Canada.  

Stan Blade, PhD, is dean of the Faculty of Agriculture, Life and Environmental Sciences at the University of Alberta.


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