Field Capture and Automatic Image Analysis for Crop Breeding and Disease Monitoring
A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Agricultural Biosystem and Biological Engineering".
Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 277
Special Issue Editor
Special Issue Information
Dear Colleagues,
The measurement of field crops has been tackled using satellite, drone, and ground-based imagery. Each approach has a different resolution, capture speed and method of analysis. Recent hardware has made it possible to produce millimeter resolution images of field-grown crops with two- and three-dimensional capture systems. Critical to the success of any imaging hardware are the algorithms for converting images or point-cloud representations into crop yield, dimensional, or plant health.
In this Special Issue, we would like to focus on the latest development in field applicable image capture and corresponding analysis algorithms. Hardware platforms can include 2/3D imaging, Lidar, hyperspectral, and IR with accompanying analysis algorithms using conventional or machine learning. We would like to include specific references to the metrology and quality of measurement at the forefront of these approaches to provide confidence in measurement and analysis to potential adopters of the technology.
Dr. Richard Dudley
Guest Editor
Manuscript Submission Information
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Keywords
- 3D imaging
- machine learning
- trait analysis
- field trials
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