Impact Factor 4.106 | CiteScore 4.47
More on impact ›

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Plant Sci. | doi: 10.3389/fpls.2019.01381

Using Sensors and Unmanned Aircraft Systems for High-Throughput Phenotyping of Biomass in Perennial Ryegrass Breeding Trials

 Junping Wang1, Pieter Badenhorst2, Andrew Phelan2,  Luke Pembleton2, Fan Shi2, German Spangenberg1,  Kevin Smith3* and  Noel Cogan4
  • 1Agriculture Victoria, Australia
  • 2Agriculture Victoria, Australia
  • 3The University of Melbourne, Australia
  • 4AgriBio, La Trobe University, Australia

Increasing herbage biomass is the predominant objective for pasture plant breeding programs. Three types of field trials are commonly involved during forage plant breeding, i.e. individually spaced plants, row plot, and sward trials. Assessments of biomass production at individual plant, row plot and sward plot levels are through visual scoring and/or cutting of biomass manually or mechanically. Both visual scoring and cutting of plants are laborious, time consuming and costly. The development of sensor technology such as multispectral sensors and unmanned aircraft systems (UAS) provide the opportunity to accelerate the process of biomass evaluation and to increase throughput, improve resolution, reduce time and cost. We tested either the handheld Trimble GreenSeeker® or Parrot Sequoia multispectral sensors attached to a 3DR Solo Quadcopter to assess biomass in perennial ryegrass field trials sown as spaced individual plants, row plots, and simulated sward plots. Significant correlations were observed between visual score and normalized difference vegetation index (NDVI) in a spaced plant field trial and between biomass yield and NDVI in row plot and sward trials. NDVI obtained from multispectral sensors and UAS can replace visual scoring in spaced plant trials. It was also a valuable proxy for yield estimation in row plot and sward trials. The ranking of cultivars by NDVI was correlated with the ranking by biomass, although the ranking order was not in complete agreement. Multispectral sensors and UAS also allow tracking productivity over time for perennial pasture, in particular, the regrowth after grazing or clipping and to explore seasonal changes and responses to environmental factors such as precipitation. These technologies will assist in transition for the forage grass breeding from pen and notepad to digital and data era.

Keywords: NDVI, Ryegrass (Lolium perenne L.), UAV (Unmanned aerial vehicle, Sensor, biomass

Received: 23 Apr 2019; Accepted: 07 Oct 2019.

Copyright: © 2019 Wang, Badenhorst, Phelan, Pembleton, Shi, Spangenberg, Smith and Cogan. 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(s) 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: Prof. Kevin Smith, The University of Melbourne, Melbourne, Australia,