High-throughput phenotyping for plant growth and biomass yield of switchgrass under a controlled environment

作     者：Yiwei Jiang Yang Yang 

作者机构：Department of AgronomyPurdue UniversityWest LafayetteIN 47907USA Institute for Plant SciencesCollege of AgriculturePurdue UniversityWest LafayetteIN 47907USA 

出 版 物：《Grass Research》 (草学研究（英文）)

年 卷 期：2022年第2卷第1期

页      面：26-32页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：image crown switch 

摘      要：Switchgrass(Panicum virgatum L.)is a native and prominent perennial grass species used for ***-throughput phenotyping of biomass component traits is desirable for switchgrass improvement and *** objective of this study was to establish correlations between the manually measured traits and image-extracted measurements in switchgrass grown in a controlled ***-green-blue(RGB)images from side-and top-views were automatically collected from the plants varying in growth stages for assessing their relationships with manually measured *** height,tiller number,crown diameter,and shoot dry weight were all significantly correlated with RGB image-based measurements including side-view height(SHT),side convex hull(SCH),side projected area(SPA),top convex hull(TCH),and top projected area(TPA).For a particular plant trait,a good prediction was observed based on an image-based measurement,including plant height and SHT(R2=0.992),tiller number and SPA(R2=0.86),crown diameter and SCH(R2=0.72),and shoot dry weight and SPA(R2=0.88).Plant height was also well predicted by SCH(R2=0.94)and SPA(R2=0.88).Overall,SHT,SCH,and SPA extracted from RGB images well predicted plant height,tiller number and shoot dry *** results demonstrated that the image-based parameters could be leveraged in quantifying the growth and development of switchgrass.