This white paper explores how machine vision automates and optimises processes in agriculture. From industrial-scale agricultural enterprises to small farms, everyone is relying more on digital technologies in line with smart farming.
Resources
11 December 2020
Chromasens’ Dr Maximilian Klammer and Daniel Dürhammer describe a 3D imaging system to inspect ball grid arrays
03 December 2020
The panellists discussed the latest advances in neural network-based approaches to vision applications, and gave advice on what engineers should focus on to develop a successful AI vision project
30 November 2020
Our panel of experts discussed how vision can help advance robot automation and improve uptake of robotics in manufacturing
18 November 2020
The white paper describes how one of Pleora's customers has benefited from the firm's AI Gateway product, which simplifies the deployment of machine learning to improve quality inspection. Working with existing hardware and software, the embedded platform integrates plug-in AI skills, a user-friendly approach to algorithm training, and powerful processing to support more advanced machine learning and computer vision algorithms.
Pages
Latest issue
Outfield analyses aerial images of orchards using machine learning to predict crop yield
Farmers are starting to reap the rewards of robotics and machine vision, as Keely Portway finds out
Open source software has advanced to a point where it’s now a credible option for industrial imaging, Matthew Dale finds
Active Silicon’s 4xCXP-12 FireBird Coaxpress frame grabber (PCIe Gen3 x8)
Chris Beynon, Active Silicon’s CTO and technical chair of the Coaxpress committee, updates on the Coaxpress standard
Neil Trevett and Chris Yates
The Khronos Group and the EMVA are to explore software standards for embedded vision. Khronos’ Neil Trevett and EMVA’s Chris Yates explain the work
Greg Blackman reports from the Embedded World show, where industry experts gave insights into vision processing at the edge
The first colour image, captured by a CMV20000 sensor, to be sent back by the hazard cameras on the Perseverance rover after its landing on Mars on 18 February. Credit: NASA/JPL-Caltech
Greg Blackman speaks to Guy Meynants, formerly of Cmosis, and Paul Jerram, of Teledyne e2v, about the history of the image sensors onboard the Mars rover
