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

Computers in Industry
Lyndon SmithMelvyn Smith

Abstract

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 sol...Continue Reading

Citations

Jun 2, 2020·World Journal of Gastrointestinal Endoscopy·Benjamin M ShandroMelvyn L Smith
Oct 18, 2020·Scientific Reports·Gary A AtkinsonAlison H Kingston-Smith

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Software Mentioned

Kinect
CMV
OpenUPS2
TensorFlow

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