A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population
High throughput phenotyping (HTP) for wheat (<i>Triticum aestivum</i> 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 wide...
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MDPI AG
2021-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/24/5173 |
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| author | Xiaofeng Cao Yulin Liu Rui Yu Dejun Han Baofeng Su |
| author_facet | Xiaofeng Cao Yulin Liu Rui Yu Dejun Han Baofeng Su |
| author_sort | Xiaofeng Cao |
| collection | DOAJ |
| description | High throughput phenotyping (HTP) for wheat (<i>Triticum aestivum</i> 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’ 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’ SGR and wheat yield were assessed and the dynamics of some indices’ SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stage detection, classifiers best average accuracy reached 93.20–98.60% and 93.80–98.80% 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’ 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. |
| first_indexed | 2024-03-10T03:10:28Z |
| format | Article |
| id | doaj.art-38ffe07ee3a34a9996a5cfc9e33277aa |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:10:28Z |
| publishDate | 2021-12-01 |
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| spelling | doaj.art-38ffe07ee3a34a9996a5cfc9e33277aa2023-11-23T10:25:48ZengMDPI AGRemote Sensing2072-42922021-12-011324517310.3390/rs13245173A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat PopulationXiaofeng Cao0Yulin Liu1Rui Yu2Dejun Han3Baofeng Su4College of Mechanical and Electronic Engineering, Northwest A & F University, Yangling 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A & F University, Yangling 712100, ChinaCollege of Agronomy, Northwest A & F University, Yangling 712100, ChinaCollege of Agronomy, Northwest A & F University, Yangling 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A & F University, Yangling 712100, ChinaHigh throughput phenotyping (HTP) for wheat (<i>Triticum aestivum</i> 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’ 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’ SGR and wheat yield were assessed and the dynamics of some indices’ SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stage detection, classifiers best average accuracy reached 93.20–98.60% and 93.80–98.80% 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’ 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.https://www.mdpi.com/2072-4292/13/24/5173high throughput phenotypingstay greenwheatRGBmultispectral imagingUAV remote sensing |
| spellingShingle | Xiaofeng Cao Yulin Liu Rui Yu Dejun Han Baofeng Su A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population Remote Sensing high throughput phenotyping stay green wheat RGB multispectral imaging UAV remote sensing |
| title | A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population |
| title_full | A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population |
| title_fullStr | A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population |
| title_full_unstemmed | A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population |
| title_short | A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population |
| title_sort | comparison of uav rgb and multispectral imaging in phenotyping for stay green of wheat population |
| topic | high throughput phenotyping stay green wheat RGB multispectral imaging UAV remote sensing |
| url | https://www.mdpi.com/2072-4292/13/24/5173 |
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