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Machine Learning Speeding Up the Development of Portfolio of New Crop Varieties to Adapt to and Mitigate Climate Change

A. Bari, H. Ouabbou, A. Jilal, View ORCID ProfileH. Khazaei, F.L. Stoddard, View ORCID ProfileM.J. Sillanpää
doi: https://doi.org/10.1101/2021.10.06.463347
A. Bari
1Operational AI, Montreal, Canada
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  • For correspondence: abdallah.bari@gmail.com
H. Ouabbou
2INRA, Rabat, Morocco
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A. Jilal
2INRA, Rabat, Morocco
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H. Khazaei
3World Vegetable Centre, Tainan, Taiwan
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F.L. Stoddard
4University of Helsinki, Helsinki, Finland
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M.J. Sillanpää
5University of Oulu, Oulu, Finland
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  • ORCID record for M.J. Sillanpää
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Abstract

Climate change poses serious challenges to achieving food security in a time of a need to produce more food to keep up with the world’s increasing demand for food. There is an urgent need to speed up the development of new high yielding varieties with traits of adaptation and mitigation to climate change. Mathematical approaches, including ML approaches, have been used to search for such traits, leading to unprecedented results as some of the traits, including heat traits that have been long sought-for, have been found within a short period of time.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted October 06, 2021.
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Machine Learning Speeding Up the Development of Portfolio of New Crop Varieties to Adapt to and Mitigate Climate Change
A. Bari, H. Ouabbou, A. Jilal, H. Khazaei, F.L. Stoddard, M.J. Sillanpää
bioRxiv 2021.10.06.463347; doi: https://doi.org/10.1101/2021.10.06.463347
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Machine Learning Speeding Up the Development of Portfolio of New Crop Varieties to Adapt to and Mitigate Climate Change
A. Bari, H. Ouabbou, A. Jilal, H. Khazaei, F.L. Stoddard, M.J. Sillanpää
bioRxiv 2021.10.06.463347; doi: https://doi.org/10.1101/2021.10.06.463347

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