Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery

作     者：Yan Zhao Bangyou Zheng Scott CChapman Kenneth Laws Barbara George-Jaeggli Graeme LHammer David RJordan Andries B.Potgieter 

作者机构：The University of QueenslandQueensland Alliance for Agriculture and Food InnovationCentre for Crop ScienceGattonQueensland 4343Australia CSIRO Agriculture and FoodSt.LuciaQueensland 4072Australia The University of QueenslandSchool of Agriculture and Food SciencesSt.LuciaQueensland 4072Australia Department of Agriculture and FisheriesAgri-Science QueenslandWarwickQueensland 4370Australia 

出 版 物：《Plant Phenomics》 (植物表型组学（英文）)

年 卷 期：2021年第3卷第1期

页      面：337-350页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 080203[工学-机械设计及理论] 082503[工学-航空宇航制造工程] 0816[工学-测绘科学与技术] 0802[工学-机械工程] 0901[农学-作物学] 0825[工学-航空宇航科学与技术] 

基　　金：the Australian Govern-ment through the Australian Research Council Centre of Excellence for Translational Photosynthesis(grant CE140100015)and a Major Equipment and Infrastructure Grant“Phenotype Sensing Platform to Enhance Plant Breeding”by the University of Queensland. 

主　　题：breeding backward Plant 

摘      要：In plant breeding,unmanned aerial vehicles(UAVs)carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping(HTP)to aid the interpretation of genotype and environment effects on morphological,biochemical,and physiological traits.A key constraint remains the reduced resolution and quality extracted from“stitchedmosaics generated from UAV missions across large areas.This can be addressed by generating high-quality reflectance data from a single nadir image per plot.In this study,a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications.Sequential steps involved(i)imagery calibration,(ii)spectral band alignment,(iii)backward calculation,(iv)plot segmentation,and(v)application.Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot.Using a derived nadir image of each plot,the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads,respectively.Furthermore,the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours(i.e.,red and white).Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.