Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data
Recent technological advances in high‐throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyperspectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 3...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/164016 |
| _version_ | 1855522185388490752 |
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| author | Yassue, Rafael Massahiro Galli, Giovanni Fritsche-Neto, Roberto Morota, Gota |
| author_browse | Fritsche-Neto, Roberto Galli, Giovanni Morota, Gota Yassue, Rafael Massahiro |
| author_facet | Yassue, Rafael Massahiro Galli, Giovanni Fritsche-Neto, Roberto Morota, Gota |
| author_sort | Yassue, Rafael Massahiro |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Recent technological advances in high‐throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyperspectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize (Zea mays L.) lines with or without plant growth‐promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1,021 nm and 13,826 single‐nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth‐related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyperspectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral‐based phenotyping for management and phenomic prediction studies. |
| format | Journal Article |
| id | CGSpace164016 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1640162025-08-21T15:39:31Z Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data Yassue, Rafael Massahiro Galli, Giovanni Fritsche-Neto, Roberto Morota, Gota high-throughput phenotyping plant height genomics Recent technological advances in high‐throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyperspectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize (Zea mays L.) lines with or without plant growth‐promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1,021 nm and 13,826 single‐nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth‐related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyperspectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral‐based phenotyping for management and phenomic prediction studies. 2023-01 2024-12-19T12:53:20Z 2024-12-19T12:53:20Z Journal Article https://hdl.handle.net/10568/164016 en Open Access Wiley Yassue, Rafael Massahiro; Galli, Giovanni; Fritsche‐Neto, Roberto and Morota, Gota. 2022. Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data. Crop Science, Volume 63 no. 1 p. 88-100 |
| spellingShingle | high-throughput phenotyping plant height genomics Yassue, Rafael Massahiro Galli, Giovanni Fritsche-Neto, Roberto Morota, Gota Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data |
| title | Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data |
| title_full | Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data |
| title_fullStr | Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data |
| title_full_unstemmed | Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data |
| title_short | Classification of plant growth‐promoting bacteria inoculation status and prediction of growth‐related traits in tropical maize using hyperspectral image and genomic data |
| title_sort | classification of plant growth promoting bacteria inoculation status and prediction of growth related traits in tropical maize using hyperspectral image and genomic data |
| topic | high-throughput phenotyping plant height genomics |
| url | https://hdl.handle.net/10568/164016 |
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