Canada Funds AI Camera Upgrade to Catch Vineyard Disease Before It Spreads
28 September 2026, Ontario: The Canadian government is putting up to CA$1.7 million (about US$1.25 million) into adapting an orchard monitoring
Read More
Computer Vision
Get the latest Computer Vision news, research and developments in agriculture, covering image-based crop monitoring, disease detection, weed identification, plant phenotyping, fruit grading and precision farming. Coverage includes artificial intelligence, machine learning, deep learning, cameras, drones, robots and imaging systems used to analyse agricultural environments.
Agricultural computer vision combines cameras and imaging technologies with algorithms such as convolutional neural networks, object detection, image segmentation and vision transformers. Applications range from identifying crop diseases and weeds to estimating crop health, detecting fruits, assessing maturity and automating field operations. Recent research is also exploring multispectral and hyperspectral imaging for detecting plant stress and diseases before symptoms become clearly visible.
Find updates on new vision models, datasets, field trials, autonomous robots, UAV-based monitoring and commercial applications. Recent work includes AI systems designed for real-world crop disease detection, where researchers are addressing challenges such as changing light, complex backgrounds, limited datasets and differences between laboratory and field conditions. These developments are important for making visual analysis more reliable and practical for precision agriculture.