Daniel McCann Is Teaching Farm Machinery to See Every Plant

The first plant in Daniel McCann's agricultural technology story was not growing beneath the long sky of Saskatchewan. It sat in an office, close enough to become a convenient test subject. McCann's team was working on artificial intelligence and computer vision. They pointed the system at the houseplant again and again. The software became unusually good at finding it. What looked like a small technical success soon suggested a much larger business: if a machine could see this plant, perhaps it could learn to tell a crop from a weed while moving above an entire field.

For McCann, the leap from desk to field had personal logic. He was born and raised in Regina in a farming family and spent about two decades in Saskatchewan before, as he later put it to a Canadian parliamentary committee, running off to become a technology entrepreneur. His career passed through software development, security, mobile payments, hardware, and computer vision. Agriculture waited in the background until the technology caught up with the problem he already understood.

Precision AI, founded in 2017, joined those two halves of his life. The company set out to move farm decisions down from the field level to the individual plant. Its systems use cameras and artificial intelligence to distinguish what is growing below, while aerial machinery gathers information or applies an input where it is needed. The principle is simple enough to explain at a kitchen table: do not treat every square foot alike when a camera and a model can tell the difference.

A career comes back to its starting point

McCann did not arrive in agriculture as a first-time founder. He led NetSecure Technologies from 2006 to 2018, served as chief technology officer at payments company AnywhereCommerce, and became chairman and president of Fivethru. A Future of Agriculture profile describes him as a three-time founder with more than 25 years across artificial intelligence, fintech, security, fast food, and agriculture. It also credits him with six patents. Long before Precision AI, he was learning to build products where software meets transactions, hardware, and the untidy behavior of the real world.

The office plant story is appealing because it makes invention look accidental. McCann is careful about what came afterward. In early interviews he talked about the distance between a successful test and a tool ready for a farm. Weather changes. Rural connectivity fails. Supply chains stretch. A growing season gives a team a narrow window to test and an even narrower tolerance for mistakes. Farmers may get only about 50 seasons in a working life. A failed deployment is not a lost afternoon. It can take a meaningful bite from a year's income and from the trust required to try again.

“It takes way more time, money and patience to be successful in Ag-Tech.”Daniel McCann, 2021

That patience became part of Precision AI's operating method. The company ran field trials outside Regina and Saskatoon. At Canada's Farm Progress Show in 2019, its drone system received a Sterling Innovation Award. In 2021, Precision AI announced $20 million in seed equity and grant funding. By 2023, it had won the Cooperative Ventures Innovation Challenge at the World Agri-Tech Innovation Summit. These milestones did not remove the long seasonal clock. They showed the team continuing to work inside it.

The field is too blunt an instrument

Agriculture often manages a field as a single unit even though the field contains millions of different plants and conditions. A broad sprayer crosses the acreage and applies treatment across the boom. McCann's argument is that this uniformity is expensive. The crop does not need the same decision as the weed beside it. A dry patch does not need the same treatment as a thriving row. Once a system can see at plant level, the field stops being one average and becomes a map of specific choices.

Precision AI first focused that idea on targeted spraying. Its computer vision distinguishes a crop from a weed in real time, allowing an aerial system to direct spray toward the weed. McCann has contrasted the approach with outfitting a 160-foot ground sprayer with cameras and computers along the entire boom. A drone carries its sensing system across the field and can reach places heavy ground equipment cannot, without driving over the crop or compacting soil.

In October 2025, McCann gave Canada's House of Commons agriculture committee a case study from Precision AI's field trials: pesticide use down 83 percent, fertilizer use down 60 percent, and yield up 2 percent. He translated the combination into farm economics of more than $100 an acre. Those figures were presented as the company's own results, not as a universal promise. Their importance is in how McCann frames the product. Environmental restraint and farm profit are not separate pitches. A more precise decision can reduce waste because waste is costly.

The longer ambition reaches beyond identifying weeds. McCann has discussed applying the same core perception to insecticides, fungicides, and fertilizer. The intelligence produced during a flight can also become a record of the field, giving farmers and agronomists data for later decisions. Precision AI calls the goal plant-level decision-making at broad-acre scale. The phrase carries the central engineering contradiction: see something tiny, then repeat the judgment across an enormous landscape.

A machine can be ready before the rules are

By 2025, the technical problem had collided with a regulatory one. Precision AI had built drones that could scan fields and apply crop protection products, but McCann told Parliament that the company could not sell that system in Canada under the existing pathway. The policies had been written before this type of artificial intelligence machinery existed. Testing, registration, and marketing had moved to the United States even though the company and its founder had deep Canadian roots.

His proposal was procedural rather than theatrical. Create a regulatory sandbox where companies can test on tightly limited fields and collect safety and efficacy data. Set predictable timelines for approvals so investors and operators know how long commercialization could take. Modernize the rules without discarding the evidence those rules are meant to demand. It was an engineer-founder asking government for a test environment.

The same monthslong rhythm appears in Precision AI's financing and partnerships. In May 2025, Cooperative Ventures, the investment fund formed by farmer-owned cooperatives CHS and GROWMARK, announced that it had joined Precision AI's Series A. New investors included Farm Credit Canada, The 51 Food and AgTech Fund, and Green Spark Ventures, alongside returning investors Fulcrum Global Capital, BDC Capital, and At One Ventures. The connection to cooperatives mattered because it gave the company a path toward farmers who could evaluate the technology in working operations.

McCann's public persona is built less around futurist spectacle than around useful comparisons. He talks about an expensive number a farmer already pays and the appeal of making it smaller. He describes AI's potential by comparing it with the move from horse to tractor. He calls himself a proud Saskatchewanite and talks openly about returning to the farm after a career in technology. Even his most ambitious claims come back to the acre, the input bill, and the next season.

The drone is visible. The deeper product is a smaller decision, made millions of times.

The farm as a collection of decisions

There is a temptation to tell the story of agricultural AI as a contest between old machinery and new autonomy. McCann's version is more continuous. Farmers have always looked for patterns, managed uncertainty, and tried to put scarce resources where they will do the most good. Artificial intelligence adds a different resolution. It can inspect more plants than a person could count and preserve those observations as data. The machinery changes, but the economic question remains familiar: what does this part of the field need now?

That perspective helps explain why the office plant mattered. It did not prove that a drone business would work. It revealed a capability. McCann then matched that capability to a problem he knew from family history and tested it through the constraints of agriculture. The result has taken years, several generations of machinery, patient capital, field partners, and a continuing argument with regulation.

The aspiration is large, but its unit is deliberately small. Precision AI wants to help a machine decide whether this plant is crop or weed, whether this spot needs treatment, whether this pass can be avoided. A field becomes a mosaic of judgments. An input becomes a targeted action rather than a blanket habit. The farmer gets a record rather than an average.

McCann left Saskatchewan to build technology and returned to agriculture when technology learned to see the thing growing on his desk. The circle is neat. The work is not. It takes place in dust and wind, inside short seasons and long approval cycles, with customers who cannot casually surrender a harvest to experimentation. Precision AI's bet is that computer vision can respect those constraints and still change the scale of the decision. Not a field at a time. A plant at a time, across the whole field.