Canopy Roughness:A New Phenotypic Trait to Estimate Aboveground Biomass from Unmanned Aerial System
作者机构：Department of AgronomyPurdue UniversityWest LafayetteINUSA Department of Cartographic and Land EngineeringHigher Polytechnic School of AvilaUniversity of SalamancaAvilaSpain Institute for Plant SciencesCollege of AgriculturePurdue UniversityWest LafayetteINUSA Department of Plant BiologyUniversity of GeorgiaAthensGAUSA Warnell School of Forestry and Natural ResourcesUniversity of GeorgiaAthensGAUSA Institute of BioinformaticsUniversity of GeorgiaAthensGAUSA Finnish Geospatial Research InstituteNational Land Survey of FinlandMasalaFinland
出 版 物：《Plant Phenomics》 (植物表型组学（英文）)
年 卷 期：2020年第2卷第1期
页 面：419-428页
核心收录：
学科分类：0710[理学-生物学] 07[理学] 09[农学] 071007[理学-遗传学] 0901[农学-作物学] 090102[农学-作物遗传育种]
基 金：M.H.is funded by the project“Development of Analytical Tools for Drone-Based Canopy Phenotyping in Crop Breeding”from the American Institute of Food and Agriculture(grant number:17000419 WBSE:F.00068834.02.005).E.P.is funded by the project“Upscaling of Carbon Intake and Water Balance Models of Individual Trees to Wider Areas with Short Interval Laser Scanning Time Series”from the Academy of Finland(no.316096).A.B.was in part supported by the NSF CAREER Award No.1845760
摘 要：Cost-effective phenotyping methods are urgently needed to advance crop genetics in order to meet the food,fuel,and fiber demands of the coming ***,characterizing plot level traits in fields is of particular *** developments in highresolution imaging sensors for UAS(unmanned aerial systems)focused on collecting detailed phenotypic measurements are a potential *** introduce canopy roughness as a new plant plot-level *** tested its usability with soybean by optical data collected from UAS to estimate *** validate canopy roughness on a panel of 108 soybean[Glycine max(L.)Merr.]recombinant inbred lines in a multienvironment trial during the R^(2) growth stage.A senseFly eBee UAS platform obtained aerial images with a senseFly *** digital *** a structure from motion(SfM)technique,we reconstructed 3D point clouds of the soybean experiment.A novel pipeline for feature extraction was developed to compute canopy roughness from point *** used regression analysis to correlate canopy roughness with field-measured aboveground biomass(AGB)with a leave-one-out ***,our models achieved a coefficient of determination(R^(2))greater than 0.5 in all ***,we found that canopy roughness has the ability to discern AGB variations among different *** test trials demonstrate the potential of canopy roughness as a reliable trait for high-throughput phenotyping to estimate *** such,canopy roughness provides practical information to breeders in order to select phenotypes on the basis of UAS data.
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