Abstract
Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining.
Highlights
Given the rapidly growing world population and pressure placed on availability and productivity of agricultural lands by climate change, it is imperative to explore new technologies and approaches to help increase the rate of crop improvement
The overall aim of this study is to investigate the application of unmanned aerial vehicle (UAV)-based high-resolution remote sensing to phenotyping ecotypes of the bioenergy crop Arundo donax under contrasting water stress levels to determine the associations between genomic and phenotypic data that will allow rapid crop improvement [1,5]
Recent efforts in A. donax crop improvement are focused on the measurement of above-ground biomass (AGB) and crop height as determinants of greater biomass yield
Summary
Given the rapidly growing world population and pressure placed on availability and productivity of agricultural lands by climate change, it is imperative to explore new technologies and approaches to help increase the rate of crop improvement These improvements aim to increase biomass of food and feed crops and that of bioenergy crops which reduce the need for fossil fuels. There remains a bottleneck in acquiring the information on the detailed morphological and physiological characteristics of large number of crop genotypes, an activity known as phenotyping In part, this requirement has been fulfilled by the development of phenomics platforms for fast, accurate, high-resolution, and non-destructive high-throughput phenotyping [1], which have several advantages, including flexibility, convenient operation [2,3] and the ability to provide controlled experimental treatments. Key physical parameters that are targets for A. donax high-throughput phenotypic evaluation under the real conditions experienced by the plant include crop height and above-ground biomass (AGB) production
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