Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability
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| Publié dans: | Journal of experimental botany. - 1985. - 73(2022), 15 vom: 03. Sept., Seite 5336-5354 |
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| Auteur principal: | |
| Autres auteurs: | , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2022
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| Accès à la collection: | Journal of experimental botany |
| Sujets: | Journal Article Research Support, Non-U.S. Gov't Climate data science Genomes to Fields (G2F) deep neural network (DNN) genotype by environment (G×E) model maize yield predictability train–test schemes |
| Accès en ligne |
Volltext |