Università degli studi della Tuscia
Published June 21, 2019 | Version v1
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Accelerating climate resilient plant breeding by applying next-generation artificial intelligence

Creators

  • 1. University of Tuscia
  • 2. Oak Ridge National Laboratory, University of Tennessee
  • 3. Oak Ridge National Laboratory
  • 4. Université Paris Nanterre
  • 5. The Hebrew University of Jerusalem
  • 6. Wageningen University & Research

Description

This is the accepted manuscript of the paper "Accelerating climate resilient plant breeding by applying next-generation artificial intelligence", published as final paper in "Trends in Biotechnology Volume 37, Issue 11, 01 November 2019, Pages 1217–1235 https://doi.org/10.1016/j.tibtech.2019.05.007”.

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.

Notes

Corresponding author: Antoine Harfouche and Arie Altman First author: Antoine Harfouche 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.

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Additional details

Funding

European Commission
WATBIO - Development of improved perennial non-food biomass and bioproduct crops for water stressed environments 311929