Canada Invests Up to C$1,693,412 to Adapt Apple Orchard AI for Vineyards

The Canadian government is investing up to C$1,693,412 in Vivid Machines Inc. to develop computer vision and machine learning tools aimed at helping vineyard operators detect disease earlier, monitor fruit quality, and estimate yields with greater precision.

Agriculture and Agri-Food Canada said the funding will be provided through the AgriScience Program’s Projects Component under the Sustainable Canadian Agricultural Partnership. The support is intended to help the Toronto-based agricultural technology company adapt its existing Vivid XV system, now used in apple orchards, for use in grape vineyards.

According to the federal department, the project will expand the company’s imaging technology so it can scan grapevines in real time and generate data at the level of individual vines, rows, and entire vineyard blocks. The upgraded system is expected to use camera-based sensing and machine learning models to identify early signs of crop disease, follow fruit development, assess quality, and predict production levels before harvest.

The announcement reflects a broader push by Canada to support agricultural innovation that can improve farm management and strengthen long-term production. In this case, the government said the technology could give growers faster and more detailed information about crop conditions, allowing them to act sooner when disease risks emerge and to manage inputs more precisely.

Vivid Machines has built its business around automated imaging tools for fruit growers. Its Vivid XV system is mounted on farm equipment such as tractors and sprayers, where it captures images while moving through the field. Those images are then processed by artificial intelligence models designed to interpret plant health, fruit growth, and signs of stress or disease. In vineyards, that could mean more consistent monitoring than visual checks alone, especially across large acreages where conditions can change quickly from one block to another.

The government said the planned vineyard version of the system is intended not only to improve disease detection but also to help reduce chemical use. Earlier identification of problems can allow growers to target treatments more carefully instead of applying them across broader areas as a precaution. That approach, if it proves effective in commercial use, could lower input costs and reduce unnecessary applications while helping protect crop quality.

That matters beyond farm operations. Grapes are the raw material for wine, and tools that improve disease surveillance and yield forecasting could help wineries and grape suppliers plan harvest timing, manage fruit quality, and stabilize production. Over time, more precise vineyard monitoring could also support efficiency across the beverage sector by improving supply visibility and potentially reducing some crop protection use, though those outcomes will depend on adoption and field performance.

Jenny Lemieux, co-founder and chief executive of Vivid Machines, said in the announcement that the federal funding will allow the company to apply technology originally built for apple growers to major vineyard challenges, including crop load estimation and early disease detection. She also said the project will support hiring in Toronto and expand high-skilled agricultural technology work in Ontario.

The federal government framed the investment as support for both farm productivity and economic development. Agriculture and Agri-Food Canada said the project is meant to strengthen the resilience of Canada’s grape and wine sector at a time when growers face pressure from disease risks, variable weather, and the need to improve efficiency.

The underlying technology combines two forms of artificial intelligence that are becoming more common in farm equipment. Computer vision allows a system to capture and interpret visual information from plants and fruit. Machine learning uses large sets of data to find patterns and improve predictions over time. In practical terms, that means the system can be trained to recognize disease symptoms, fruit clusters, and crop development stages from images collected in the field.

For vineyard operators, one of the central promises of the project is speed. Traditional scouting often depends on crews walking through vineyards and recording observations manually. A camera system mounted on equipment that is already moving through the vineyard could collect more data more often, making it easier to detect changes as the season develops. Yield estimates at the vine, row, and block levels could also help growers make decisions on labor, harvest scheduling, and winery intake planning.

The funding was announced in a news release from Agriculture and Agri-Food Canada dated Sept. 10 and later reported by The Grower. The department said the project is part of Canada’s effort to back homegrown agricultural technology that can be applied directly in commercial production and help growers respond to operational and environmental pressures.