Robotics is reshaping greenhouse and orchard production in Canada
Canada is becoming a test bed for robotic systems in greenhouses and orchards as growers look for solutions to labour shortages, higher precision needs and lower chemical use.
Canada is increasingly serving as a test bed for robotic systems in greenhouse and orchard production. Digital Journal argues that the shift is no longer about isolated demonstrations, but about a broader move from conventional mechanisation to technologies that can perceive, classify and act in biologically complex environments. That distinction matters in horticulture, where harvesting and crop care often require delicate, crop-specific decisions that standard field machinery cannot easily replicate.
One example is the Guelph Intelligent Greenhouse Automation System, or GIGAS. The platform uses machine vision and deep learning to identify ripe tomatoes and guide picking mechanisms capable of handling fragile produce without damage. Similar progress is being made in orchards, where robotic systems combine cameras, lidar and spectral sensors to assess ripeness, detect disease and optimise harvest timing. As more field data is collected, the underlying models improve, turning each harvest cycle into another round of machine learning refinement.
The economic case for adoption is closely linked to labour. The article says Canada's farm sector could face more than 100,000 vacancies by 2030 as a large share of the workforce retires. Fruit picking and greenhouse harvesting are among the most exposed segments because they are repetitive, time-sensitive and labour-intensive. Robots can operate continuously, without fatigue and without depending on seasonal labour availability. That could help growers extend harvest windows for perishable crops and reduce losses caused by delayed picking.
The technology also carries agronomic and environmental benefits. Machine vision can identify early signs of plant stress or disease, making it possible to replace blanket agrochemical use with targeted intervention. The article highlights robotic weeders that use artificial intelligence to distinguish crops from weeds and then either remove weeds mechanically or apply micro-doses of herbicide only where needed. In greenhouse systems, the same digital logic can be applied to irrigation, nutrient delivery and lighting, with sensors tracking plant-level microclimates to improve water and energy efficiency while supporting yield per square metre.
Adoption is still constrained by cost, complexity and infrastructure. Upfront investment remains high for small and mid-sized farms, and reliable operation depends on connectivity, data management and technical support. Another challenge is that many current systems are designed for specific crops or controlled environments, which limits broader scale-up. That means the next step for the sector is not only better robots, but more standardised and financially accessible platforms that can be integrated across a wider range of horticultural operations.