{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T15:22:27Z","timestamp":1784992947947,"version":"3.55.0"},"reference-count":62,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T00:00:00Z","timestamp":1639958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31961143019"],"award-info":[{"award-number":["31961143019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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-purpose HTP platforms for crops in the field. The purpose of this study was to compare the potential of UAV RGB and multispectral images (MSI) in SG phenotyping of diversified wheat germplasm. The multi-temporal images of 450 samples (406 wheat genotypes) were obtained and the color indices (CIs) from RGB and MSI and spectral indices (SIs) from MSI were extracted, respectively. The four indices (CIs in RGB, CIs in MSI, SIs in MSI, and CIs + SIs in MSI) were used to detect four SG stages, respectively, by machine learning classifiers. Then, all indices\u2019 dynamics were analyzed and the indices that varied monotonously and significantly were chosen to calculate wheat temporal stay green rates (SGR) to quantify the SG in diverse genotypes. The correlations between indices\u2019 SGR and wheat yield were assessed and the dynamics of some indices\u2019 SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stage detection, classifiers best average accuracy reached 93.20\u201398.60% and 93.80\u201398.80% in train and test set, respectively, and the SIs containing red edge or near-infrared band were more effective than the CIs calculated only by visible bands. Indices\u2019 temporal SGR could quantify SG changes on a population level, but showed some differences in the correlation with yield and in tracking visual SG grades samples. In SIs, the SGR of Normalized Difference Red-edge Index (NDRE), Red-edge Chlorophyll Index (CIRE), and Normalized Difference Vegetation Index (NDVI) in MSI showed high correlations with yield and could track visual SG grades at an earlier stage of grain filling. In CIs, the SGR of Normalized Green Red Difference Index (NGRDI), the Green Leaf Index (GLI) in RGB and MSI showed low correlations with yield and could only track visual SG grades at late grain filling stage and that of Norm Red (NormR) in RGB images failed to track visual SG grades. This study preliminarily confirms the MSI is more available and reliable than RGB in phenotyping for wheat SG. The index-based SGR in this study could act as HTP reference solutions for SG in diversified wheat genotypes.<\/jats:p>","DOI":"10.3390\/rs13245173","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T08:43:32Z","timestamp":1639989812000},"page":"5173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population"],"prefix":"10.3390","volume":"13","author":[{"given":"Xiaofeng","family":"Cao","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A & F University, Yangling 712100, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Northwest A & F University, Yangling 712100, China"},{"name":"Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Northwest A & F University, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A & F University, Yangling 712100, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Northwest A & F University, Yangling 712100, China"},{"name":"Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Northwest A & F University, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Agronomy, Northwest A & F University, Yangling 712100, China"},{"name":"State Key Laboratory of Crop Stress Biology for Arid Areas, Northwest A & F University, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dejun","family":"Han","sequence":"additional","affiliation":[{"name":"College of Agronomy, Northwest A & F University, Yangling 712100, China"},{"name":"State Key Laboratory of Crop Stress Biology for Arid Areas, Northwest A & F University, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baofeng","family":"Su","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A & F University, Yangling 712100, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Northwest A & F University, Yangling 712100, China"},{"name":"Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Northwest A & F University, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.eng.2018.06.005","article-title":"Developing Wheat for improved yield and adaptation under a changing climate: Optimization of a few key genes","volume":"4","author":"Dowla","year":"2018","journal-title":"Engineering"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1038\/s41437-020-0320-1","article-title":"Phenology and related traits for wheat adaptation","volume":"125","author":"Hyles","year":"2020","journal-title":"Heredity"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"638738","DOI":"10.3389\/fpls.2021.638738","article-title":"Capturing and selecting senescence variation in wheat","volume":"12","author":"Chapman","year":"2021","journal-title":"Front. 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