Artificial intelligence (AI) is steadily transforming how food is grown and produced. What was once the stuff of science fiction – self-driving tractors, sensor-studded fields, predictive algorithms guiding farm decisions – is now becoming reality on Canadian farms. From the vast prairies of Saskatchewan to high-tech greenhouses in Ontario, farmers are embracing data and automation as the next agricultural revolution. This article explores how AI is being used in modern agriculture, highlights Canadian innovations and examples, discusses the benefits and challenges of AI on the farm, and looks at how everyday Canadians can get involved in this high-tech farming future.
How AI is Transforming Modern Agriculture
AI brings a suite of advanced technologies to agriculture that make farming more precise, efficient, and insight-driven. Some of the key applications include:
- Precision Agriculture & Predictive Analytics: Farmers no longer have to treat a whole field uniformly. Precision agriculture uses GPS, satellite imagery, and soil sensors to gather detailed data on variations in soil, moisture, and crop conditions. AI algorithms analyze this data to guide decisions on how much water or fertilizer to apply in each area, optimizing inputs and boosting yields. Predictive models can forecast crop yields or pest outbreaks, helping farmers plan ahead. For example, AI-driven analysis can operate at sub-field levels and potentially increase yields by up to 20% through pinpointed adjustments. In Canada, 50.4% of farms are already using some form of technology on the farm, such as GPS soil sampling (32% of farms), auto-steer tractors (27%), and variable-rate input application (16%) – all components of precision farming that lay the groundwork for AI-driven optimization.
- Computer Vision and Crop Health Monitoring: AI-powered computer vision allows farmers to see their crops in greater detail than the human eye. High-resolution cameras on drones or tractors scan fields to detect weeds, pests, or signs of disease stress on plants. The AI can identify issues earlier than a farmer might spot them walking the field. For instance, drones equipped with AI are being used to survey Canadian farms and flag areas of weed pressure before weeds even emerge, enabling timely intervention. An industry expert noted that AI “can determine if there’s a stressor happening earlier than the human eye can pick up” so that management measures can happen more quickly. In practice, this could mean identifying a patch of fungal disease or nutrient deficiency in a canola field and alerting the farmer to treat it before it spreads. AI vision is also improving crop quality grading – for example, assessing the condition of grain or produce. Darrell Petras of the Canadian Agri-Food Automation and Intelligence Network (CAAIN) points out that AI can assist in grading grain right in the field, helping farmers decide the best time to harvest and what quality to expect when selling.
- Smart Irrigation Systems: Water is a precious resource, and AI is helping use it wisely. Smart irrigation systems use soil moisture sensors and weather data, combined with AI, to automatically adjust watering schedules. Instead of watering on a fixed timer, the system learns when and how much different parts of a field (or greenhouse) actually need water. This precision can dramatically reduce water waste – studies in Ontario have shown that using precision agriculture techniques, including smart irrigation, can cut water and fertilizer use by up to 40% while increasing crop yields by around 20%. In a country like Canada where some regions face periodic droughts and others have limited growing seasons, efficient water use is vital. AI not only ensures crops get the right amount of water at the right time, but also helps monitor for issues like irrigation equipment failures or drainage problems by analyzing sensor feedback in real time.
- Automation and Robotics on the Farm: AI is the “brain” behind a new generation of farming machines. Autonomous tractors and robotic field equipment can navigate fields and perform tasks like planting, weeding, or harvesting with minimal human input. Advanced farm robots use AI to interpret sensor data and make decisions on the go – for example, distinguishing a crop plant from a weed and precisely spraying herbicide only where needed. A notable case is the DOT autonomous farm platform (originally developed in Canada by a Saskatchewan engineer, Norbert Beaujot), which uses AI for driverless operation and has been integrated into commercial driverless equipment after being acquired by an ag tech company. In horticulture, Canadian fruit growers are testing robotic harvesters that use AI to identify ripe fruit and pick it gently. AI-driven automation is also well-established in dairy farming – many Canadian dairy farms use robotic milking systems and automated feeders that adapt to each cow’s schedule and needs. In fact, an Edmonton-based startup is launching the world’s first AI-driven system to visually classify dairy cow traits, aiming to improve herd health and longevity. These examples show how AI and robotics are reducing the need for manual labour in farming tasks while operating with precision around the clock.
Canadian Innovations and Applications in AI Farming
Canada is emerging as a hotbed for agricultural AI innovation, with homegrown startups, research institutions, and even government programs pushing the technology forward. Farms across the country – big and small – are beginning to implement AI solutions tailored to Canada’s unique agricultural landscape.
One vivid example comes from the greenhouse sector in Ontario. At Nature Fresh Farms in Leamington, Ontario (a hub of Canada’s greenhouse vegetable production), thousands of sensors blanket the rows of tomatoes, cucumbers, peppers, and strawberries in its smart greenhouses. These sensors feed data into an AI system that finely controls lighting, temperature, and irrigation. “We wanted to use technology to help us grow more, have a better-tasting vegetable, and just do more in general,” said Keith Bradley, Vice-President of IT and Security at Nature Fresh Farms. Using an AI platform developed with tech partners Intel and Dell, the greenhouse can be proactive instead of reactive, adjusting conditions in real time to optimize growth. The results have been impressive – Bradley notes that the AI has increased crop yields while reducing power and water usage for the facility. It’s even had side benefits like improving work-life balance for employees by automating tedious monitoring tasks.
In the vast grain farms of Western Canada, AI is helping manage sprawling acres more efficiently. Many Prairie farmers now use drone imagery and AI analytics to monitor crop conditions over thousands of hectares. Jacqueline Keena, managing director at Winnipeg-based EMILI (Enterprise Machine Intelligence & Learning Initiative), observes that farmers have already adopted tools like drones to scout for weed or pest issues. The next phase is using AI models to interpret that data and make inferences and decisions – enabling agriculture to become “hyper-optimized” down to very specific locations in a field. This means an AI might analyze drone images and sensor feeds and then automatically recommend adding extra nitrogen to one corner of a wheat field, or identify an early insect infestation in another, with precision that wasn’t possible before.
Canada’s commitment to agricultural AI is also supported by strong research and government initiatives. A network of “smart farms” has sprung up across the country – experimental farms equipped with the latest technology to test and demonstrate AI innovations in real farming conditions. Olds College in Alberta operates one such Smart Farm, and EMILI’s Innovation Farms site near Winnipeg is another; these sites let farmers and ag-tech companies see AI tools in action at scale “We really show how they work in a commercial setting… acting as a bit of a risk mitigator as we try out these technologies and then share with others how they actually work,” says EMILI’s Jacqueline Keena, explaining that these demonstration farms help accelerate adoption of new technologies by proving their value. On the academic front, universities are deeply involved – for example, the University of Guelph’s “Artificial Intelligence for Food” initiative and University of Calgary’s research into AI for precision agriculture are generating new AI applications for farming. Canadian researchers like U. Calgary’s Dr. Farhad Maleki point out that agriculture contributes around 7% of Canada’s GDP, so innovating in this sector is critical for maintaining competitiveness. “To stay competitive, we need to invest in AI and integrate it with every industry,” Maleki notes, emphasizing that Canada must be a leader in applying AI to agriculture.
Several Canadian agri-tech startups and companies are making waves with AI solutions. Winnipeg-based Croptimistic Technology (known for its SWAT Maps platform) uses field sensors and AI to map soil variability and guide precise applications of seed and fertilizer. CAAIN’s Darrell Petras mentions that Croptimistic’s system can detect pests or crop color changes from field data, warning farmers of stress factors early. On the west coast, Vancouver’s Ecoation developed an AI platform with robotics to detect pests and diseases in greenhouse vegetables before outbreaks spread, protecting crops in places like British Columbia’s greenhouse operations. In the Prairies, Regina-based startup Precision AI is developing drone technology that uses AI to identify and spray weeds selectively, promising to cut herbicide use and costs for Canadian grain growers. Established firms are in the game as well: Winnipeg-founded Farmers Edge offers a digital platform using machine learning to give yield forecasts and agronomic advice based on data from hundreds of Canadian farms. Even the Canadian government is directly supporting this innovation boom – the Canadian Agri-Food Automation and Intelligence Network (CAAIN), launched in 2019, is a federal initiative to fund and commercialize AI and robotic solutions in agriculture. By providing grants for projects that apply AI, drones, and data analytics to farming, CAAIN helps new technologies make it out of the lab and onto the farm.
These examples barely scratch the surface, but they illustrate a clear trend: across Canada’s agricultural sectors – from crop farming to greenhouses to dairy – AI-driven technology is taking hold. Importantly, it’s not just large industrial farms leading the charge. Smaller farms and family operations are also finding value in these tools (often through agtech services or co-ops), and many startups are focusing on affordable, user-friendly AI products for farms of all sizes. As AI becomes more accessible, we can expect its use in Canadian agriculture to continue rising. In fact, RBC’s Farmer 4.0 report predicted that by 2030, the integration of digital and autonomous technologies could be so widespread that farms will be staffed more by tech specialists than traditional labor, and Canada could face a shortage of 123,000 agriculture workers with the advanced skills needed for this high-tech farming future. The same report noted that if Canada succeeds in fully embracing such innovations, the agriculture sector could add another $11 billion to the national GDP, surpassing the output of auto manufacturing and aerospace combined. It’s no wonder, then, that there is intense interest in AI for agriculture – the stakes for productivity, sustainability, and food security are enormous.
Benefits of AI-Powered Farming: A Greener, Smarter Future
Why are farmers and researchers so excited about AI in agriculture? Implementing AI technologies on the farm offers a range of compelling benefits:
- Higher Yields and Greater Efficiency: Perhaps the most immediate draw is the potential for increased crop yields and more efficient use of inputs. By making farming decisions hyper-specific to each plant or animal’s needs, AI minimizes waste and maximizes output. For example, AI systems that analyze sensor data can ensure fertilizer and water are applied at the optimal rate, avoiding overuse and ensuring crops aren’t undernourished. One analysis suggests that AI-driven precision agriculture could boost yields by around 20% in some cases. At the Nature Fresh greenhouse in Ontario, adopting AI controls has already led to yield improvements in vegetable crops while using less energy and water. On dairy farms, AI-driven health monitoring can lead to longer-living, more productive cows by catching illnesses early and improving breeding decisions, thereby increasing milk output. All of these gains contribute to a more productive farm sector that can produce more food on the same amount of land.
- Sustainability and Environmental Benefits: AI is helping farms become more sustainable by reducing the environmental footprint of agriculture. Precision application of inputs means less fertilizer runoff into waterways and lower greenhouse gas emissions from over-fertilization. TELUS Agriculture (a branch of the Canadian telecom company focusing on ag tech) estimates that broad use of precision agriculture techniques in Canada could significantly cut emissions – potentially abating 2% of Canada’s total greenhouse gases – by optimizing fertilizer and fuel use. They noted that implementing precision fertilization (the “4Rs” – right source, right rate, right time, right place) in corn farming increased yields by ~20% and reduced GHG emissions by ~75% in an Ontario study. Furthermore, AI-powered irrigation saves water during droughts, and smarter pest management means farmers can often reduce chemical pesticide use by targeting only the problem areas. In sum, AI allows producing more with less, aligning with Canada’s goals for sustainable agriculture and climate change mitigation. It’s a win-win where farming becomes both more productive and eco-friendlier.
- Reduced Labor Burden and Improved Safety: Farming has long been a labor-intensive industry, and Canadian farms often face worker shortages – a situation expected to grow, with a projected shortfall of tens of thousands of farm workers by 2030. AI and automation offer a partial solution by taking over repetitive or strenuous tasks. Autonomous machinery can handle long hours of fieldwork (like plowing or spraying) without fatigue, while robotic systems automate milking cows or picking produce in greenhouses. This not only helps farms compensate for labor shortages but also makes farm work safer. Dangerous tasks (such as handling hazardous chemicals or heavy equipment) can be delegated to machines. For instance, spraying pesticides via drone keeps human operators at a safe distance from chemicals. In addition, AI can improve on-farm safety by monitoring equipment and environment conditions to prevent accidents – imagine an AI that detects that a tractor is overheating or that a grain silo has a gas buildup and then alerts the farmer. By reducing physical strain and risk, technology can improve quality of life for farmers and farm workers. In the words of one agri-tech leader, integrating IT and data science into agriculture is creating new high-skilled roles on farms, meaning farmers of the future may spend more time with tablets than on tractors.
- Proactive Decision Making and Precision: One of the less tangible but very powerful benefits of AI is a shift from reactive to proactive farm management. Traditional farming often reacted to problems after they became visible – irrigating when crops look thirsty, spraying when pests are obviously present, adjusting fertilizer after noticing yellowing leaves. AI changes this by analyzing data continuously to predict issues before they fully manifest. As Darrell Petras explains, AI can catch subtle signs of crop stress or disease early, enabling interventions “much more quickly” than waiting for human observation. Likewise, AI-driven models can incorporate weather forecasts to, say, recommend adjusting planting dates or harvesting earlier if a drought or early frost is predicted. Farmers gain a decision support tool that crunches vast datasets (soil data, weather, market prices, etc.) and offers tailored advice. Keith Bradley of Nature Fresh Farms described how their AI lets them be proactive, adjusting greenhouse conditions ahead of time rather than reacting after plants show stress. This kind of data-driven foresight leads to healthier crops and more stable outputs. It can also reduce costs – for example, if an AI predicts a disease risk in one corner of a field, a farmer can target treatment there rather than spraying an entire field “just in case.” Overall, farming becomes more of a high-precision science, with AI ensuring no drop of water or ounce of fertilizer is wasted and no potential problem is ignored.
- Enhanced Food Security and Quality: For Canada as a whole, embracing AI in agriculture strengthens food security. Higher yields and efficiency mean Canadian agriculture can produce more food domestically, reducing reliance on imports and ensuring a stable food supply even as population grows. AI tools also help farmers adapt to climate variability – by analyzing climate data and guiding climate-smart practices – which is crucial as weather patterns become more unpredictable. Additionally, AI can improve the quality and traceability of food. Computer vision systems can sort crops by quality, detect contaminants, or identify the optimal harvest time to ensure peak nutrition and taste. There are experimental AI models that can predict crop storage life or optimize supply chain logistics so that Canadian-grown produce gets to market fresher and with less waste. All these improvements contribute to a more resilient food system for Canada, able to feed its population sustainably and possibly even expand exports of high-quality Canadian agricultural products.
Challenges and Considerations: Why Adoption Isn’t Automatic
While the promise of AI in agriculture is great, it’s not without challenges. Canadian farmers must navigate several hurdles and concerns as they integrate AI into their operations. Some of the notable challenges include:
- High Initial Costs and Access to Technology: Cutting-edge AI equipment and systems can be expensive to acquire and implement. Many small and medium-sized farms operate on thin profit margins, making them cautious about investing in unproven technology. Purchasing drones, sensors for every field, or autonomous robots is a big upfront expense, not to mention the cost of software subscriptions or AI advisory services. “Small-scale farmers and those in remote areas may still face significant barriers, including high upfront costs and limited access to technical training,” notes Dr. Farhad Maleki of UCalgary. The technology also often requires supporting infrastructure – for example, to use a suite of IoT sensors and cloud-based AI analytics, a farm needs reliable internet (which, as discussed below, is not always available everywhere in Canada). There is a risk that wealthier or larger farms leap ahead with AI, while smaller family farms get left behind, widening the gap in productivity. However, costs are gradually coming down, and various government grants and programs (such as CAAIN funding competitions) are emerging to help farmers adopt these technologies. The Telus Agriculture report advocates for subsidies or incentives to ensure farmers can justify the investment in technologies that bring environmental benefits. Over time, as equipment like drones and smart sensors become more common, their price is expected to decrease, making AI more accessible across the industry.
- Digital Skills and Training: Introducing AI means farmers suddenly need to deal with software, data dashboards, and perhaps complex machinery. This represents a new skill set that not all farmers have had the opportunity to develop. The average Canadian farmer is getting older (many are in their 50s and 60s), and there’s a learning curve to adopt digital tools. Even for tech-savvy younger farmers, time is a major constraint – “Farmers don’t have the time to spend an entire day learning how to navigate complex software or hardware. The technology must be intuitive, seamless and clearly worth the investment,” Maleki emphasizes. During planting or harvest season, farmers are working from dawn to dusk; they can’t afford systems that are user-unfriendly or that frequently break down requiring troubleshooting. Therefore, ease-of-use is critical. This challenge is being addressed as ag-tech providers focus on user-centric design (making interfaces simpler) and as outreach programs help train farmers. For example, many provinces hold workshops, field days, and demo events (often at those smart farms) to teach producers how to use new AI tools. Additionally, the next generation of farmers – many of whom are more digitally native – will likely accelerate adoption as they take over operations. The Canadian agricultural education system is already adapting, with colleges integrating precision ag and data science into their curricula to ensure future farmers are prepared for AI-driven agriculture.
- Connectivity and Rural Broadband Gaps: A very practical barrier in Canada is internet connectivity. Modern AI systems often rely on cloud computing, real-time data upload, or remote monitoring – all of which assume you have a solid internet connection on the farm. Yet, 40% of rural Canadians do not have reliable broadband at the CRTC’s target speeds (50 Mbps download/10 Mbps upload, according to a 2023 report. This digital divide means some farmers literally can’t use certain high-tech solutions that require constant connectivity. If your field sensors can’t upload their data or your autonomous tractor can’t receive a GPS correction signal due to poor internet, the AI advantages are lost. Improving rural internet access is a national priority if AI is to benefit all regions from the Maritimes to the North. Telecommunications companies and governments have been investing in rural broadband and LTE networks for precisely this reason. In the meantime, some farmers use offline-first solutions – for instance, recording data on local devices and uploading when back at the farmhouse Wi-Fi – but this is not ideal. As Canada rolls out 5G and other infrastructure in rural areas, connectivity should gradually improve, enabling even remote farms in Saskatchewan or the Yukon to fully participate in the AI revolution. Until then, lack of internet remains a significant challenge for tech adoption on the farm.
- Data Privacy and Ownership: The advent of AI in farming means an explosion of data – yield maps, soil metrics, drone images, livestock health records – much of which is stored digitally, often on platforms run by private tech companies. This raises questions about who owns or controls this data. Farmers are understandably protective of information about their land and operations. Handing it over to an AI platform can feel risky if it’s unclear how the data might be used beyond the farmer’s purposes. There’s concern that large agribusinesses or tech providers could exploit farm data for their gain, or that sensitive data could be exposed in breaches. “Farmers can be resistant to sharing their own data,” notes Felippe Karp, a researcher at Olds College, highlighting a challenge for developing robust AI models. The trust factor is crucial – farmers need to trust that an AI’s recommendations are sound (even if the decision process is a “black box”) and trust that their data isn’t being misused. Canadian institutions are aware of this issue: efforts are underway to ensure farmers retain control of their data through secure storage solutions and more transparent data policies. Some initiatives, like data trusts or co-ops, are exploring ways for farmers to collectively manage data sharing on their terms. In any case, the industry recognizes that without addressing privacy and ownership, many farmers will hesitate to fully embrace AI. Clear agreements, opt-in data sharing, and demonstrating the value returned to the farmer in exchange for their data are all strategies to build confidence. As Rozita Dara of University of Guelph points out, data is the lifeblood of AI models, so farmers need to be incentivized to share data and see benefit from it. Over time, as trust grows and successful use-cases spread, this barrier may lessen, but it remains a consideration.
- Uncertainty and Transition Hurdles: Farming is already a risky business – weather, market prices, and other factors can make each year unpredictable. Adding new technology into the mix can feel like adding another uncertainty. What if the AI recommendation is wrong? What if the system goes down at a critical time? These concerns make some farmers prefer a cautious approach. As one observer noted, farmers get just “one shot” at a crop each year, so they can’t afford big risks on unproven methods. It might take a season or more to see the payoff of an AI tool, which can slow adoption. There’s also the broader concern of over-reliance on automation – if future farms depend heavily on AI, a cyberattack or software glitch could be disruptive. Farmers and tech providers need to incorporate fail-safes and allow human override so that technology remains a tool, not a potential point of failure. Additionally, some farm communities worry about technology replacing jobs or changing rural life; although AI is more about augmenting scarce labor than eliminating plentiful labor, these social considerations are part of the conversation. Transitioning to “Farm 4.0” will take time and trust. This is why demonstration projects and peer learning are so important – farmers are more convinced by seeing their neighbor successfully use a technology than by any sales pitch. As Darrell Petras notes, engagement through field demos, conferences, and workshops is “absolutely critical” – when farmers see AI tools working effectively “in their backyard” at local test farms, they grow more comfortable adopting them. The good news is that such engagement is happening more and more, and early adopters in Canada are sharing their stories of both successes and lessons learned, helping the whole sector move forward carefully but steadily.
Despite these challenges, the trajectory is clearly toward greater use of AI in agriculture. Each growing season, the technology improves and becomes a bit more user-friendly and affordable. Meanwhile, the pressure to feed a growing population sustainably and the ongoing labor shortages are pushing the industry to innovate. Canadian farmers have a well-earned reputation for adapting and overcoming hardships – and adopting AI may be the latest chapter in that story. With supportive policies, education, and innovation, the obstacles to AI integration can be managed, ensuring that the benefits are broadly shared across Canada’s agricultural community.
AiFarming: Bringing AI to Your Home Garden in Canada
So far, we have looked at AI in the context of farms and greenhouses, but what about the average Canadian who isn’t a farmer? Excitingly, the power of artificial intelligence in agriculture is not limited to large-scale operations. AiFarming is a new Canadian platform that is bringing advanced farming intelligence right to people’s homes – whether you have a rooftop garden in Toronto, a backyard vegetable patch in Vancouver, or even just a few pots on a balcony in Calgary. This AI-powered platform is designed for urban and home farmers, making sustainable growing accessible to everyday Canadians.
AiFarming acts as a personal smart gardening assistant. When a user signs up, the platform takes into account their location, local climate data, and the specific crops or plants they want to grow. It then provides real-time guidance from planting to harvest. For example, if you’re in Montreal trying to grow tomatoes on your balcony, AiFarming will recommend the optimal planting date (tailored to Quebec’s frost schedule), and send you reminders or instructions on watering, fertilizing, and pruning timed to your plants’ needs. The AI behind the platform analyzes weather forecasts, daylight hours, and even growth patterns to tell you exactly when to water or when your tomatoes might need a nutrient boost. Essentially, it removes the guesswork from gardening by giving data-driven tips in an easy-to-understand way.
One of the platform’s unique values is personalization. It’s like having a master gardener by your side, but one who knows your specific micro-climate and plant variety intimately. As conditions change – say an unexpected heat wave in July – AiFarming might alert you to shade your plants or water more frequently to prevent heat stress. If a certain pest that’s common in your region is spotted in the area, the app can warn you to take preventive measures. This level of insight can be game-changing for novice gardeners and experienced green thumbs alike. It helps novices avoid common mistakes (like overwatering or planting at the wrong time), increasing their success rate. And for experienced growers, it offers optimization and the latest techniques (for instance, suggesting a cover crop or soil amendment based on AI analysis of what your soil likely lacks).
AiFarming was founded in Canada with a mission to empower communities to grow their own fresh produce. In urban centers, many people feel disconnected from agriculture; AiFarming aims to change that by making home farming simpler and more efficient. It’s not just about individual gardens either – the platform encourages a community aspect. Users can share their successes, troubleshoot issues with others, and even take part in a marketplace to share or sell excess produce to neighbors. This community-building reflects a broader vision: turning urban farming into a collective effort toward sustainability and food security, one backyard at a time. By combining AI technology with local knowledge, AiFarming helps users get higher yields from small spaces, use resources wisely (avoiding water waste, for example), and grow healthy organic food right at home. Imagine city rooftops overflowing with fresh vegetables and herbs, guided by AI for optimal growth – that’s the future AiFarming is working toward, and it’s happening now in communities across Canada.
For Canadians passionate about sustainability, technology, and taking control of their food, AiFarming offers an ideal intersection. It takes the advanced principles of AI in agriculture that we’ve discussed throughout this article – precision, data analytics, predictive insights – and applies them on a home-friendly scale. Whether you’re an apartment dweller looking to maximize a small balcony garden or a homeowner aiming for a bountiful backyard harvest, AiFarming can help plan and manage your garden with the expertise of agronomists and the efficiency of AI. The platform’s user-friendly design means you don’t have to be a tech expert to benefit; if you can use a smartphone, you can use AiFarming to start growing smarter.
In conclusion, the rise of AI in agriculture isn’t just reshaping big farms in Canada – it’s opening up possibilities for everyone to participate in the future of farming. From boosting national crop production to helping a Toronto resident grow tomatoes on a rooftop, AI is indeed the future of farming in Canada at all scales. It promises a more efficient, sustainable, and connected agricultural system. As we embrace these technologies responsibly, guided by both innovation and respect for the land, Canada’s farming – and even gardening – future looks bright. AI in agriculture is not a distant vision; it’s here now, sowing seeds of change across the country, and platforms like AiFarming are inviting all Canadians to be part of this agricultural evolution.
