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 journalReview articlepeer-review

135 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 languageAmerican English
Pages (from-to)1217-1235
Number of pages19
JournalTrends in Biotechnology
Volume37
Issue number11
DOIs
StatePublished - Nov 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Ltd

Keywords

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

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