Artificial intelligence in plant breeding
Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore...
| Autores principales: | , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/162565 |
| _version_ | 1855531823017230336 |
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| author | Farooq, Muhammad Amjad Shang Gao Hassan, Muhammad Adeel Zhangping Huang Awais Rasheed Hearne, Sarah Prasanna, Boddupalli M. Xinhai Li Huihui Li |
| author_browse | Awais Rasheed Farooq, Muhammad Amjad Hassan, Muhammad Adeel Hearne, Sarah Huihui Li Prasanna, Boddupalli M. Shang Gao Xinhai Li Zhangping Huang |
| author_facet | Farooq, Muhammad Amjad Shang Gao Hassan, Muhammad Adeel Zhangping Huang Awais Rasheed Hearne, Sarah Prasanna, Boddupalli M. Xinhai Li Huihui Li |
| author_sort | Farooq, Muhammad Amjad |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype–phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field. |
| format | Journal Article |
| id | CGSpace162565 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1625652025-05-04T09:21:59Z Artificial intelligence in plant breeding Farooq, Muhammad Amjad Shang Gao Hassan, Muhammad Adeel Zhangping Huang Awais Rasheed Hearne, Sarah Prasanna, Boddupalli M. Xinhai Li Huihui Li artificial intelligence big data genetic gain plant breeding Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype–phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field. 2024-10 2024-11-21T20:22:13Z 2024-11-21T20:22:13Z Journal Article https://hdl.handle.net/10568/162565 en Open Access application/pdf Elsevier Farooq, M. A., Gao, S., Hassan, M. A., Huang, Z., Rasheed, A., Hearne, S., Prasanna, B., Li, X., & Li, H. (2024). Artificial intelligence in plant breeding. Trends In Genetics, 40(10), 891-908. https://doi.org/10.1016/j.tig.2024.07.001 |
| spellingShingle | artificial intelligence big data genetic gain plant breeding Farooq, Muhammad Amjad Shang Gao Hassan, Muhammad Adeel Zhangping Huang Awais Rasheed Hearne, Sarah Prasanna, Boddupalli M. Xinhai Li Huihui Li Artificial intelligence in plant breeding |
| title | Artificial intelligence in plant breeding |
| title_full | Artificial intelligence in plant breeding |
| title_fullStr | Artificial intelligence in plant breeding |
| title_full_unstemmed | Artificial intelligence in plant breeding |
| title_short | Artificial intelligence in plant breeding |
| title_sort | artificial intelligence in plant breeding |
| topic | artificial intelligence big data genetic gain plant breeding |
| url | https://hdl.handle.net/10568/162565 |
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