Jaco Fourie
Machine Vision Manager, Lincoln AgritechJaco has a Ph.D in computer vision from the University of Canterbury and a Masters in applied mathematics from the University of Stellenbosch. He joined Lincoln Agritech in July 2013. Prior to that, he worked at CropLogic as the modelling technology leader and as a researcher at Geospatial Research Centre (NZ). His background is in computer vision, image processing, software, and heuristic optimisation techniques. In 2016 he led the team in upgrading their skillset to include deep learning which has now become a major focus of the group’s research.
Presentation Promo
Humans rely on their vision system to collect information about their surroundings and then in turn base their decisions and actions on the processing of this information. Similarly, the field of computer vision aims to use camera technology and image processing algorithms to replicate this feat using computers, and with the help of machine learning hopes to make similarly informed and sensible decisions.
In the last 20 years precision agriculture has made extensive use of these technologies to automate and improve the accuracy of several common tasks in agriculture like yield estimation, disease detection, growth modelling, harvesting, weeding, etc. However, the pervasive use of this technology also highlighted the limitations that researchers must overcome to get practical, economically viable outputs to farmers.
This presentation will focus on identifying and addressing these limitations and to suggest some future research directions to move the industry to a point where the use of computer vision will be as impactful to the industry today as mechanisation was 100 years ago.
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Jaco Fourie
Lincoln Agritech
Jaco Fourie
Precision agriculture has made extensive use of new technology to automate and improve common tasks. However, the pervasive use of this technology also highlighted the limitations that researchers must overcome to get practical, economically viable outputs to farmers.
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