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Front. Plant Sci. | doi: 10.3389/fpls.2018.00237

High throughput determination of plant height, ground cover and above-ground biomass in wheat with LiDAR

  • 1High Resolution Plant Phenomics Centre (HRPPC), Australian Plant Phenomics Facility (APPF), Australia
  • 2Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
  • 3ARC Centre of Excellence for Translational Photosynthesis, Australia
  • 4Agronomía, Instituto de Agricultura Sostenible (CSIC), Spain
  • 5National Computational Infrastructure (NCI), Australia

Crop improvement efforts are targeting increased above-ground biomass and radiation-use efficiency as drivers for greater yield. Early ground cover and canopy height contribute to biomass production, but manual measurements of these traits, and in particular above-ground biomass, are slow and labour-intensive, more so when made at multiple developmental stages. These constraints limit the ability to capture these data in a temporal fashion, hampering insights that could be gained from multi-dimensional data. Here we demonstrate the capacity of Light Detection And Ranging (LiDAR), mounted on a lightweight, mobile, ground-based platform, for rapid multi-temporal and non-destructive estimation of canopy height, ground cover and above-ground biomass.

Field validation of LiDAR measurements is presented. For canopy height, strong relationships with LiDAR (r² of 0.99 and root mean square error of 0.017 m) were obtained. Ground cover was estimated from LiDAR using two methodologies: red reflectance image and canopy height. In contrast to NDVI, LiDAR was not affected by saturation at high ground cover, and the comparison of both LiDAR methodologies showed strong association (r²=0.92 and slope=1.02) at ground cover above 0.8. For above-ground biomass, a dedicated field experiment was performed with destructive biomass sampled eight times across different developmental stages. Two methodologies are presented for the estimation of biomass from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). The parameters involved in the calculation of 3DVI and 3DPI were optimised for each sample event from tillering to maturity, as well as generalized for any developmental stage. Individual sample point predictions were strong while predictions across all eight sample events, provided the strongest association with biomass (r²=0.93 and r²=0.92) for 3DPI and 3DVI, respectively. Given these results, we believe that application of this system will provide new opportunities to deliver improved genotypes and agronomic interventions via more efficient and reliable phenotyping of these important traits in large experiments.

Keywords: lidar, plant phenotyping, aboveground biomass, NDVI, field experiments

Received: 01 Dec 2017; Accepted: 09 Feb 2018.

Edited by:

Yann Guédon, Centre de coopération internationale en recherche agronomique pour le développement (CIRAD), France

Reviewed by:

Nicolas Virlet, Rothamsted Research (BBSRC), United Kingdom
Helge Aasen, ETH Zurich, Switzerland  

Copyright: © 2018 Jimenez-Berni, Deery, Rozas-Larraondo, Condon, Rebetzke, James, Bovill, Furbank and Sirault. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jose A. Jimenez-Berni, Australian Plant Phenomics Facility (APPF), High Resolution Plant Phenomics Centre (HRPPC), Clunies Ross St, BLD 5, Canberra, 2601, ACT, Australia,