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Integrating speed breeding with artificial intelligence for developing climate-smart crops
INTRODUCTION: In climate change, breeding crop plants with improved productivity, sustainability, and adaptability has become a daunting challenge to ensure global food security for the ever-growing global population. Correspondingly, climate-smart crops are also the need to regulate biomass product...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Netherlands
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9360691/ https://www.ncbi.nlm.nih.gov/pubmed/35941420 http://dx.doi.org/10.1007/s11033-022-07769-4 |
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author | Rai, Krishna Kumar |
author_facet | Rai, Krishna Kumar |
author_sort | Rai, Krishna Kumar |
collection | PubMed |
description | INTRODUCTION: In climate change, breeding crop plants with improved productivity, sustainability, and adaptability has become a daunting challenge to ensure global food security for the ever-growing global population. Correspondingly, climate-smart crops are also the need to regulate biomass production, which is imperative for the maintenance of ecosystem services worldwide. Since conventional breeding technologies for crop improvement are limited, time-consuming, and involve laborious selection processes to foster new and improved crop varieties. An urgent need is to accelerate the plant breeding cycle using artificial intelligence (AI) to depict plant responses to environmental perturbations in real-time. MATERIALS AND METHODS: The review is a collection of authorized information from various sources such as journals, books, book chapters, technical bulletins, conference papers, and verified online contents. CONCLUSIONS: Speed breeding has emerged as an essential strategy for accelerating the breeding cycles of crop plants by growing them under artificial light and temperature conditions. Furthermore, speed breeding can also integrate marker-assisted selection and cutting-edged gene-editing tools for early selection and manipulation of essential crops with superior agronomic traits. Scientists have recently applied next-generation AI to delve deeper into the complex biological and molecular mechanisms that govern plant functions under environmental cues. In addition, AIs can integrate, assimilate, and analyze complex OMICS data sets, an essential prerequisite for successful speed breeding protocol implementation to breed crop plants with superior yield and adaptability. |
format | Online Article Text |
id | pubmed-9360691 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-93606912022-08-09 Integrating speed breeding with artificial intelligence for developing climate-smart crops Rai, Krishna Kumar Mol Biol Rep Original Article INTRODUCTION: In climate change, breeding crop plants with improved productivity, sustainability, and adaptability has become a daunting challenge to ensure global food security for the ever-growing global population. Correspondingly, climate-smart crops are also the need to regulate biomass production, which is imperative for the maintenance of ecosystem services worldwide. Since conventional breeding technologies for crop improvement are limited, time-consuming, and involve laborious selection processes to foster new and improved crop varieties. An urgent need is to accelerate the plant breeding cycle using artificial intelligence (AI) to depict plant responses to environmental perturbations in real-time. MATERIALS AND METHODS: The review is a collection of authorized information from various sources such as journals, books, book chapters, technical bulletins, conference papers, and verified online contents. CONCLUSIONS: Speed breeding has emerged as an essential strategy for accelerating the breeding cycles of crop plants by growing them under artificial light and temperature conditions. Furthermore, speed breeding can also integrate marker-assisted selection and cutting-edged gene-editing tools for early selection and manipulation of essential crops with superior agronomic traits. Scientists have recently applied next-generation AI to delve deeper into the complex biological and molecular mechanisms that govern plant functions under environmental cues. In addition, AIs can integrate, assimilate, and analyze complex OMICS data sets, an essential prerequisite for successful speed breeding protocol implementation to breed crop plants with superior yield and adaptability. Springer Netherlands 2022-08-08 2022 /pmc/articles/PMC9360691/ /pubmed/35941420 http://dx.doi.org/10.1007/s11033-022-07769-4 Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Rai, Krishna Kumar Integrating speed breeding with artificial intelligence for developing climate-smart crops |
title | Integrating speed breeding with artificial intelligence for developing climate-smart crops |
title_full | Integrating speed breeding with artificial intelligence for developing climate-smart crops |
title_fullStr | Integrating speed breeding with artificial intelligence for developing climate-smart crops |
title_full_unstemmed | Integrating speed breeding with artificial intelligence for developing climate-smart crops |
title_short | Integrating speed breeding with artificial intelligence for developing climate-smart crops |
title_sort | integrating speed breeding with artificial intelligence for developing climate-smart crops |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9360691/ https://www.ncbi.nlm.nih.gov/pubmed/35941420 http://dx.doi.org/10.1007/s11033-022-07769-4 |
work_keys_str_mv | AT raikrishnakumar integratingspeedbreedingwithartificialintelligencefordevelopingclimatesmartcrops |