With AI, it’s still early days, but adoption will be critical if businesses are to remain competitive and responsive to changing markets and consumer needs. If Canada can’t compete on economies of scale, it can employ AI to increase operational efficiencies, create better harvests, reduce reliance on manual work, improve water usage, and reduce waste, among other uses.
TELUS Agriculture is already using AI through its digital kits, which equip farmers with Internet of Things (IoT) sensors, farm management software, and predictive analytics tools. These kits have been found to enhance sustainability and improve food system resilience by helping farmers achieve a 20% increase in water use efficiency and a 15% reduction in fertilizer applications, saving 500 million liters of water annually.
Yet, AI and technological adoption are generally not a given in this industry. John Jansen, Global Head of TELUS Agriculture, says that while there have been “amazing programs with phenomenal investments, they fall flat because there isn’t the time available on the farm or the margins that enable things to work. Getting crops planted becomes more important.”
Marc Low, Director of Innovation, Growth and Emerging Tech, KPMG in Canada, says there are several barriers to AI implementation, including:
- Financial barriers: high upfront investment costs and limited capital for smaller producers.
- Technological infrastructure: lack of digital and data-collecting systems.
- Technical expertise: a shortage of skilled personnel who can implement complex AI systems makes it challenging to meet ongoing training and education needs.
- Return uncertainty: benefits aren’t immediately clear, and users have concerns about how spending on AI will translate into gains.
Due to some of the challenges when implementing AI, Sadek recommends farmers and others to start with one area of focus within the supply chain. “Chunk it into smaller pieces,” he says. “Where are your pain points within the value chain? That’s where you need to start.”
One single technology isn’t the answer, however. To be effective, companies must layer multiple AI technologies on one another. For instance, a farm might have a robot that travels up and down rows of indoor greenhouses. With computer vision, it looks at crops to see if the pH levels of water are off or if there’s an issue with fertilizer. Then, using AI analytics models, it can determine how best to optimize yield. “That’s layering,” says Sadek. “It’s taking AI and IoT and marrying them to get this perfect solution.”
CEA vegetable grower Windset Farms, based in Delta, British Columbia hopes to implement AI in several ways. It’s eyeing tech to optimize crop production by automatically adjusting mechanical tools like venting, heating, lighting, irrigation, and other parameters based on complex data inputs. This will help Windset Farms manage more hectares of greenhouses with less manual oversight. Windset Farms is also developing its own proprietary software called StreamlineTM, which uses AI for procurement recommendations and sales insights. “AI is going to be pretty big with true machine learning, where it will steer the mechanical tools of the greenhouse more deliberately utilizing past experience,” says Steven Newell, President and CEO of Windset Farms.