Accelerating climate resilient plant breeding by applying next-generation artificial intelligence
Autor: | Joost J. B. Keurentjes, Menachem Moshelion, Arie Altman, Gerald A. Tuskan, Antoine Harfouche, Jonathon Romero, Antoine H. Harfouche, Daniel Jacobson, David Kainer, Giuseppe Scarascia Mugnozza |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
0301 basic medicine
Crops Agricultural Genotype Computer science media_common.quotation_subject Climate Climate Change Big data Bioengineering Genomics 02 engineering and technology Laboratorium voor Erfelijkheidsleer Adaptability Ecosystem services explainable AI Smart farming 03 medical and health sciences smart farming Phenomics Artificial Intelligence genomics Humans Field phenomics Plant breeding Biomass Augmented breeding Ecosystem media_common Food security business.industry field phenomics 021001 nanoscience & nanotechnology Plant Breeding augmented breeding 030104 developmental biology Phenotype Explainable AI Laboratory of Genetics Artificial intelligence Next-generation artificial intelligence Genotype to phenotype EPS 0210 nano-technology business next-generation artificial intelligence Biotechnology |
Zdroj: | Trends in Biotechnology 37 (2019) 11 Trends in Biotechnology, 37(11), 1217-1235 |
ISSN: | 0167-7799 |
Popis: | 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. |
Databáze: | OpenAIRE |
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