Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence

Antoine L. Harfouche; Daniel A. Jacobson; David Kainer; Jonathon C. Romero; Antoine H. Harfouche; Giuseppe Scarascia Mugnozza; Menachem Moshelion; Gerald A. Tuskan; Joost J.B. Keurentjes; Arie Altman

doi:10.1016/j.tibtech.2019.05.007

Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence

Antoine L. Harfouche, Daniel A. Jacobson, David Kainer, Jonathon C. Romero, Antoine H. Harfouche, Giuseppe Scarascia Mugnozza, Menachem Moshelion, Gerald A. Tuskan, Joost J.B. Keurentjes, Arie Altman

Research output: Contribution to journal › Review article › peer-review

162 Scopus citations

Abstract

Breeding crops for high yield and superior adaptability to new and variable climates is imperative to ensure continued food security, biomass production, and ecosystem services. Advances in genomics and phenomics are delivering insights into the complex biological mechanisms that underlie plant functions in response to environmental perturbations. However, linking genotype to phenotype remains a huge challenge and is hampering the optimal application of high-throughput genomics and phenomics to advanced breeding. Critical to success is the need to assimilate large amounts of data into biologically meaningful interpretations. Here, we present the current state of genomics and field phenomics, explore emerging approaches and challenges for multiomics big data integration by means of next-generation (Next-Gen) artificial intelligence (AI), and propose a workable path to improvement.

Original language	English
Pages (from-to)	1217-1235
Number of pages	19
Journal	Trends in Biotechnology
Volume	37
Issue number	11
DOIs	https://doi.org/10.1016/j.tibtech.2019.05.007
State	Published - Nov 2019

Funding

Funding was provided by the EU 7th Framework Programme – WATBIO, grant no 311929 (A.L.H. and J.J.B.K.), the Italian Ministry of Education, the University & Research Brain Gain Professorship to A.L.H. and the Center for Bioenergy Innovation, a US Department of Energy Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. Funding was also provided by the DOE, Laboratory Directed Research and Development funding (ORNL AI Initiative ProjectID 9613) at the Oak Ridge National Laboratory. The iRF-driven GS work referred to in this review used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. The authors would like to acknowledge Ashley Cliff for manuscript review and editing. The manuscript was coauthored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Funding was provided by the EU 7th Framework Programme – WATBIO , grant no 311929 (A.L.H. and J.J.B.K.), the Italian Ministry of Education , the University & Research Brain Gain Professorship to A.L.H., and the Center for Bioenergy Innovation, a US Department of Energy Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. Funding was also provided by the DOE, Laboratory Directed Research and Development funding (ORNL AI Initiative ProjectID 9613 ) at the Oak Ridge National Laboratory . The iRF-driven GS work referred to in this review used resources of the Oak Ridge Leadership Computing Facility , which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. The authors would like to acknowledge Ashley Cliff for manuscript review and editing. The manuscript was coauthored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

Funders	Funder number
DOE Public Access Plan
WATBIO
U.S. Department of Energy
Office of Science
Biological and Environmental Research
Oak Ridge National Laboratory	DE-AC05-00OR22725
Laboratory Directed Research and Development
Seventh Framework Programme	311929
Center for Bioenergy Innovation
Government of South Australia
UT-Battelle
Ministero dell’Istruzione, dell’Università e della Ricerca

Keywords

augmented breeding
explainable AI
field phenomics
genomics
next-generation artificial intelligence
smart farming

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.tibtech.2019.05.007

Cite this

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title = "Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence",

abstract = "Breeding crops for high yield and superior adaptability to new and variable climates is imperative to ensure continued food security, biomass production, and ecosystem services. Advances in genomics and phenomics are delivering insights into the complex biological mechanisms that underlie plant functions in response to environmental perturbations. However, linking genotype to phenotype remains a huge challenge and is hampering the optimal application of high-throughput genomics and phenomics to advanced breeding. Critical to success is the need to assimilate large amounts of data into biologically meaningful interpretations. Here, we present the current state of genomics and field phenomics, explore emerging approaches and challenges for multiomics big data integration by means of next-generation (Next-Gen) artificial intelligence (AI), and propose a workable path to improvement.",

keywords = "augmented breeding, explainable AI, field phenomics, genomics, next-generation artificial intelligence, smart farming",

author = "Harfouche, {Antoine L.} and Jacobson, {Daniel A.} and David Kainer and Romero, {Jonathon C.} and Harfouche, {Antoine H.} and {Scarascia Mugnozza}, Giuseppe and Menachem Moshelion and Tuskan, {Gerald A.} and Keurentjes, {Joost J.B.} and Arie Altman",

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AU - Harfouche, Antoine L.

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AU - Romero, Jonathon C.

AU - Harfouche, Antoine H.

AU - Scarascia Mugnozza, Giuseppe

AU - Moshelion, Menachem

AU - Tuskan, Gerald A.

AU - Keurentjes, Joost J.B.

AU - Altman, Arie

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AB - Breeding crops for high yield and superior adaptability to new and variable climates is imperative to ensure continued food security, biomass production, and ecosystem services. Advances in genomics and phenomics are delivering insights into the complex biological mechanisms that underlie plant functions in response to environmental perturbations. However, linking genotype to phenotype remains a huge challenge and is hampering the optimal application of high-throughput genomics and phenomics to advanced breeding. Critical to success is the need to assimilate large amounts of data into biologically meaningful interpretations. Here, we present the current state of genomics and field phenomics, explore emerging approaches and challenges for multiomics big data integration by means of next-generation (Next-Gen) artificial intelligence (AI), and propose a workable path to improvement.

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KW - explainable AI

KW - field phenomics

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Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence

Abstract

Funding

Keywords

UN SDGs

Access to Document

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