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

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

doi:10.1016/j.tibtech.2019.05.007

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

Author(s)

Harfouche, Antoine

Jacobson, Daniel A

Kainer, David

Romero, Jonathon C

Scarascia Mugnozza, Giuseppe

more

Date Issued

2019

Type

article

Volume

37

Issue

11

Start Page

1217-1235

End Page

1235

DOI

10.1016/j.tibtech.2019.05.007

ISSN

0167-7799

Journal

CELL PRESERVATION TECHNOLOGY

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.

Handle

http://hdl.handle.net/2067/43009

File(s)

Name

Articolo 4.pdf

Description

Versione open per VQR

Size

1.22 MB

Format

Adobe PDF

Checksum (MD5)

c0030170ad6c7cd620404d4a2e00a70a