Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV
Tao Duan A B , Bangyou Zheng A , Wei Guo C , Seishi Ninomiya C , Yan Guo B and Scott C. Chapman A D E
+ Author Affiliations
- Author Affiliations
A CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Road, St Lucia, Qld 4067, Australia.
B College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100 193, China.
C Institute for Sustainable Agro-ecosystem Services, The University of Tokyo, Tokyo 188-0002, Japan.
D Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Qld 4072, Australia.
E Corresponding author. Email: scott.chapman@csiro.au
Functional Plant Biology 44(1) 169-183 https://doi.org/10.1071/FP16123
Submitted: 31 March 2016 Accepted: 6 October 2016 Published: 24 November 2016
Abstract
Ground cover is an important physiological trait affecting crop radiation capture, water-use efficiency and grain yield. It is challenging to efficiently measure ground cover with reasonable precision for large numbers of plots, especially in tall crop species. Here we combined two image-based methods to estimate plot-level ground cover for three species, from either an ortho-mosaic or undistorted (i.e. corrected for lens and camera effects) images captured by cameras using a low-altitude unmanned aerial vehicle (UAV). Reconstructed point clouds and ortho-mosaics for the whole field were created and a customised image processing workflow was developed to (1) segment the ‘whole-field’ datasets into individual plots, and (2) ‘reverse-calculate’ each plot from each undistorted image. Ground cover for individual plots was calculated by an efficient vegetation segmentation algorithm. For 79% of plots, estimated ground cover was greater from the ortho-mosaic than from images, particularly when plants were small, or when older/taller in large plots. While there was a good agreement between the ground cover estimates from ortho-mosaic and images when the target plot was positioned at a near-nadir view near the centre of image (cotton: R2 = 0.97, sorghum: R2 = 0.98, sugarcane: R2 = 0.84), ortho-mosaic estimates were 5% greater than estimates from these near-nadir images. Because each plot appeared in multiple images, there were multiple estimates of the ground cover, some of which should be excluded, e.g. when the plot is near edge within an image. Considering only the images with near-nadir view, the reverse calculation provides a more precise estimate of ground cover compared with the ortho-mosaic. The methodology is suitable for high throughput phenotyping for applications in agronomy, physiology and breeding for different crop species and can be extended to provide pixel-level data from other types of cameras including thermal and multi-spectral models.
Additional keywords: image processing, plot segmentation, remotely piloted aircraft, RPA, unmanned aerial vehicle.
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