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Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding
Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucid...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9570104/ https://www.ncbi.nlm.nih.gov/pubmed/36232455 http://dx.doi.org/10.3390/ijms231911156 |
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author | Khan, Muhammad Hafeez Ullah Wang, Shoudong Wang, Jun Ahmar, Sunny Saeed, Sumbul Khan, Shahid Ullah Xu, Xiaogang Chen, Hongyang Bhat, Javaid Akhter Feng, Xianzhong |
author_facet | Khan, Muhammad Hafeez Ullah Wang, Shoudong Wang, Jun Ahmar, Sunny Saeed, Sumbul Khan, Shahid Ullah Xu, Xiaogang Chen, Hongyang Bhat, Javaid Akhter Feng, Xianzhong |
author_sort | Khan, Muhammad Hafeez Ullah |
collection | PubMed |
description | Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs. |
format | Online Article Text |
id | pubmed-9570104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95701042022-10-17 Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding Khan, Muhammad Hafeez Ullah Wang, Shoudong Wang, Jun Ahmar, Sunny Saeed, Sumbul Khan, Shahid Ullah Xu, Xiaogang Chen, Hongyang Bhat, Javaid Akhter Feng, Xianzhong Int J Mol Sci Review Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs. MDPI 2022-09-22 /pmc/articles/PMC9570104/ /pubmed/36232455 http://dx.doi.org/10.3390/ijms231911156 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Khan, Muhammad Hafeez Ullah Wang, Shoudong Wang, Jun Ahmar, Sunny Saeed, Sumbul Khan, Shahid Ullah Xu, Xiaogang Chen, Hongyang Bhat, Javaid Akhter Feng, Xianzhong Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding |
title | Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding |
title_full | Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding |
title_fullStr | Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding |
title_full_unstemmed | Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding |
title_short | Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding |
title_sort | applications of artificial intelligence in climate-resilient smart-crop breeding |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9570104/ https://www.ncbi.nlm.nih.gov/pubmed/36232455 http://dx.doi.org/10.3390/ijms231911156 |
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