Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding
Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides...
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MDPI AG
2024-09-01
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| author | Charleston dos Santos Lima Darci Francisco Uhry Junior Ivan Ricardo Carvalho Christian Bredemeier |
| author_facet | Charleston dos Santos Lima Darci Francisco Uhry Junior Ivan Ricardo Carvalho Christian Bredemeier |
| author_sort | Charleston dos Santos Lima |
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| description | Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (>80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model. |
| format | Article |
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| language | English |
| publishDate | 2024-09-01 |
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| series | AgriEngineering |
| spelling | doaj-art-48a5bb0d5f3d4fa4bf0ca2efe2e370af2025-08-20T01:56:09ZengMDPI AGAgriEngineering2624-74022024-09-01633261327810.3390/agriengineering6030186Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to FloodingCharleston dos Santos Lima0Darci Francisco Uhry Junior1Ivan Ricardo Carvalho2Christian Bredemeier3Department of Crop Science, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 91540-000, RS, BrazilDepartment of Crop Science, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 91540-000, RS, BrazilDepartment of Agriculture, Northwestern Regional University of the State of Rio Grande do Sul (UNIJUI), Ijuí 98700-000, RS, BrazilDepartment of Crop Science, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 91540-000, RS, BrazilFlooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (>80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model.https://www.mdpi.com/2624-7402/6/3/186phenotypingbreedingfloodspectral |
| spellingShingle | Charleston dos Santos Lima Darci Francisco Uhry Junior Ivan Ricardo Carvalho Christian Bredemeier Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding AgriEngineering phenotyping breeding flood spectral |
| title | Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding |
| title_full | Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding |
| title_fullStr | Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding |
| title_full_unstemmed | Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding |
| title_short | Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding |
| title_sort | use of phenomics in the selection of uav based vegetation indices and prediction of agronomic traits in soybean subjected to flooding |
| topic | phenotyping breeding flood spectral |
| url | https://www.mdpi.com/2624-7402/6/3/186 |
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