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

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

150 Citations (Scopus)

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
Journal	Trends in Biotechnology
Volume	37
Issue number	11
Early online date	21 Jun 2019
DOIs	https://doi.org/10.1016/j.tibtech.2019.05.007
Publication status	Published - Nov 2019

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

WATBIO: Development of improved perennial non-food biomass and bioproduct crops for water stressed environments
1/11/12 → 31/10/17
Project: EU research project

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",

year = "2019",

month = nov,

doi = "10.1016/j.tibtech.2019.05.007",

language = "English",

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AU - Harfouche, Antoine L.

AU - Jacobson, Daniel A.

AU - Kainer, David

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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Y1 - 2019/11

N2 - 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.

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.

KW - augmented breeding

KW - explainable AI

KW - field phenomics

KW - genomics

KW - next-generation artificial intelligence

KW - smart farming

U2 - 10.1016/j.tibtech.2019.05.007

DO - 10.1016/j.tibtech.2019.05.007

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JO - Trends in Biotechnology

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ER -

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

Abstract

Keywords

UN SDGs

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Projects

WATBIO: Development of improved perennial non-food biomass and bioproduct crops for water stressed environments

Cite this