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Open Access Publications from the University of California

Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals?

  • Author(s): Streich, Jared
  • Romero, Jonathon
  • Gazolla, João Gabriel Felipe Machado
  • Kainer, David
  • Cliff, Ashley
  • Prates, Erica Teixeira
  • Brown, James B
  • Khoury, Sacha
  • Tuskan, Gerald A
  • Garvin, Michael
  • Jacobson, Daniel
  • Harfouche, Antoine L
  • et al.

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Human population growth and accelerated climate change necessitate agricultural improvements using designer crop ideotypes (idealized plants that can grow in niche environments). Diverse and highly skilled research groups must integrate efforts to bridge the gaps needed to achieve international goals toward sustainable agriculture. Given the scale of global agricultural needs and the breadth of multiple types of omics data needed to optimize these efforts, explainable artificial intelligence (AI with a decipherable decision making process that provides a meaningful explanation to humans) and exascale computing (computers that can perform 1018 floating-point operations per second, or exaflops) are crucial. Accurate phenotyping and daily-resolution climatype associations are equally important for refining ideotype production to specific environments at various levels of granularity. We review advances toward tackling technological hurdles to solve multiple United Nations Sustainable Development Goals and discuss a vision to overcome gaps between research and policy.

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