A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 24; p. 5173
• Main Authors: Cao, Xiaofeng, Liu, Yulin, Yu, Rui, Han, Dejun, Su, Baofeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021
• Subjects: Adaptability; Agricultural production; Chlorophyll; Classifiers; color; Color imagery; Correlation; Crop yield; Crops; Genotype & phenotype; Genotypes; Geography; Germplasm; Grain; high throughput phenotyping; Learning algorithms; leaves; Machine learning; Mathematical analysis; multispectral imaging; normalized difference vegetation index; Normalized difference vegetative index; Phenology; phenotype; Phenotypes; Phenotyping; Population; Rain; Remote sensing; RGB; Senescence; Software; Sorghum; stay green; Triticum aestivum; UAV remote sensing; Unmanned aerial vehicles; Vegetation; Visual observation; Wheat; China

High throughput phenotyping (HTP) for wheat (Triticum aestivum L.) stay green (SG) is expected in field breeding as SG is a beneficial phenotype for wheat high yield and environment adaptability. The RGB and multispectral imaging based on the unmanned aerial vehicle (UAV) are widely popular multi-pu...