Innovative 3D and 2D machine vision methods for analysis of plants and crops in the field

Smith, Lyndon; Zhang, Wenhao; Hansen, Mark F; Hales, Ian; Smith, Melvyn

Abstract

© 2018 Elsevier B.V. Machine vision systems offer great potential for automating crop control, harvesting, fruit picking, and a range of other agricultural tasks. However, most of the reported research on machine vision in agriculture involves a 2D approach, where the utility of the resulting data is often limited by effects such as parallax, perspective, occlusion and changes in background light – particularly when operating in the field. The 3D approach to plant and crop analysis described in this paper offers potential to obviate many of these difficulties by utilising the richer information that 3D data can generate. The methodologies presented, such as four-light photometric stereo, also provide advanced functionalities, such as an ability to robustly recover 3D surface texture from plants at very high resolution. This offers potential for enabling, for example, reliable detection of the meristem (the part of the plant where growth can take place), to within a few mm, for directed weeding (with all the associated cost and ecological benefits) as well as offering new capabilities for plant phenotyping. The considerable challenges associated with robust and reliable utilisation of machine vision in the field are also considered and practical solutions are described. Two projects are used to illustrate the proposed approaches: a four-light photometric stereo apparatus able to recover plant textures at high-resolution (even in direct sunlight), and a 3D system able to measure potato sizes in-the-field to an accuracy of within 10%, for extended periods and in a range of environmental conditions. The potential benefits of the proposed 3D methods are discussed, both in terms of the advanced capabilities attainable and the widespread potential uptake facilitated by their low cost.

• Journal Article Type: Article
• Acceptance Date: Feb 22, 2018
• Online Publication Date: Mar 10, 2018
• Publication Date: May 1, 2018
• Deposit Date: Mar 12, 2018
• Publicly Available Date: Apr 24, 2018
• Journal: Computers in Industry
• Print ISSN: 0166-3615
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 97
• Pages: 122-131
• DOI: https://doi.org/10.1016/j.compind.2018.02.002
• Keywords: 3D vision, plant analysis, photometric stereo
• Public URL: https://uwe-repository.worktribe.com/output/868767
• Publisher URL: https://doi.org/10.1016/j.compind.2018.02.002
• Contract Date: Mar 13, 2018