A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches
Rice fields are a major producer of methane, a strong greenhouse gas. However, identifying genetic variation in methane emissions among rice varieties remains challenging. This study applied association rule mining to detect key rice root morphological and anatomical traits influencing methane emiss...
| Autores principales: | , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Oxford University Press
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179411 |
| _version_ | 1855522070446735360 |
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| author | Roy, Ripon Kumar Hosseiniyan Khatibi, Seyed Mahdi Kim, Sung-Ryul Trijatmiko, Kurniawan Rudi Diaz, Maria Genaleen Q. Ocampo, Eureka Teresa M. Hernandez, Jose E. Pirmoradi, Saeed Henry, Amelia Kohli, Ajay |
| author_browse | Diaz, Maria Genaleen Q. Henry, Amelia Hernandez, Jose E. Hosseiniyan Khatibi, Seyed Mahdi Kim, Sung-Ryul Kohli, Ajay Ocampo, Eureka Teresa M. Pirmoradi, Saeed Roy, Ripon Kumar Trijatmiko, Kurniawan Rudi |
| author_facet | Roy, Ripon Kumar Hosseiniyan Khatibi, Seyed Mahdi Kim, Sung-Ryul Trijatmiko, Kurniawan Rudi Diaz, Maria Genaleen Q. Ocampo, Eureka Teresa M. Hernandez, Jose E. Pirmoradi, Saeed Henry, Amelia Kohli, Ajay |
| author_sort | Roy, Ripon Kumar |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Rice fields are a major producer of methane, a strong greenhouse gas. However, identifying genetic variation in methane emissions among rice varieties remains challenging. This study applied association rule mining to detect key rice root morphological and anatomical traits influencing methane emissions, validated using a support vector machine. We report models which accurately classified high and low methane-emitting varieties with 98% (morphological) and 94% (anatomical) accuracy. These models effectively distinguished methane emission categories based on intrinsic trait patterns. Machine learning analysis highlighted the top 10 morphological and anatomical traits associated with methane emission levels. High methane-emitting varieties were characterized by lower middle root porosity, base root porosity, average root porosity, root diameter (RDia), and higher S-type lateral root length. Conversely, low methane-emitting varieties exhibited lower root number, tiller number, root dry weight, leaf number, and higher RDia. Anatomically, high methane-emitting varieties showed reduced lacunae number, total stele area, mean metaxylem size, metaxylem number, and metaxylem vessel area. Low methane-emitting varieties, in contrast, had higher percent aerenchyma, total stele area, ratio of total cortical area to root cross-section area, ratio of stele to root cross-section area, and aerenchyma area. The results suggest that the rhizosphere oxygenation role of root porosity and aerenchyma might predominate over the methane transport role. |
| format | Journal Article |
| id | CGSpace179411 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1794112026-01-07T02:05:29Z A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches Roy, Ripon Kumar Hosseiniyan Khatibi, Seyed Mahdi Kim, Sung-Ryul Trijatmiko, Kurniawan Rudi Diaz, Maria Genaleen Q. Ocampo, Eureka Teresa M. Hernandez, Jose E. Pirmoradi, Saeed Henry, Amelia Kohli, Ajay methane emission rice varieties roots morphology anatomical structures genetic variation machine learning models rice fields Rice fields are a major producer of methane, a strong greenhouse gas. However, identifying genetic variation in methane emissions among rice varieties remains challenging. This study applied association rule mining to detect key rice root morphological and anatomical traits influencing methane emissions, validated using a support vector machine. We report models which accurately classified high and low methane-emitting varieties with 98% (morphological) and 94% (anatomical) accuracy. These models effectively distinguished methane emission categories based on intrinsic trait patterns. Machine learning analysis highlighted the top 10 morphological and anatomical traits associated with methane emission levels. High methane-emitting varieties were characterized by lower middle root porosity, base root porosity, average root porosity, root diameter (RDia), and higher S-type lateral root length. Conversely, low methane-emitting varieties exhibited lower root number, tiller number, root dry weight, leaf number, and higher RDia. Anatomically, high methane-emitting varieties showed reduced lacunae number, total stele area, mean metaxylem size, metaxylem number, and metaxylem vessel area. Low methane-emitting varieties, in contrast, had higher percent aerenchyma, total stele area, ratio of total cortical area to root cross-section area, ratio of stele to root cross-section area, and aerenchyma area. The results suggest that the rhizosphere oxygenation role of root porosity and aerenchyma might predominate over the methane transport role. 2025-10-07 2026-01-06T02:54:33Z 2026-01-06T02:54:33Z Journal Article https://hdl.handle.net/10568/179411 en Open Access application/pdf Oxford University Press Roy, Ripon Kumar, Seyed Mahdi Hosseiniyan Khatibi, Sung-Ryul Kim, Kurniawan Rudi Trijatmiko, Maria Genaleen Q. Diaz, Eureka Teresa M. Ocampo, Jose E. Hernandez, Saeed Pirmoradi, Amelia Henry, and Ajay Kohli. "A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches." in silico Plants 7, no. 2 (2025): diaf017. |
| spellingShingle | methane emission rice varieties roots morphology anatomical structures genetic variation machine learning models rice fields Roy, Ripon Kumar Hosseiniyan Khatibi, Seyed Mahdi Kim, Sung-Ryul Trijatmiko, Kurniawan Rudi Diaz, Maria Genaleen Q. Ocampo, Eureka Teresa M. Hernandez, Jose E. Pirmoradi, Saeed Henry, Amelia Kohli, Ajay A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches |
| title | A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches |
| title_full | A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches |
| title_fullStr | A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches |
| title_full_unstemmed | A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches |
| title_short | A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches |
| title_sort | robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches |
| topic | methane emission rice varieties roots morphology anatomical structures genetic variation machine learning models rice fields |
| url | https://hdl.handle.net/10568/179411 |
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