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

Antoine L Harfouche1, Daniel A Jacobson2, David Kainer3

  • 1Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy.

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Summary
This summary is machine-generated.

Breeding crops for climate adaptability requires integrating genomics and phenomics data. Next-generation artificial intelligence (AI) offers a path to link genotype to phenotype for improved crop traits and food security.

Keywords:
augmented breedingexplainable AIfield phenomicsgenomicsnext-generation artificial intelligencesmart farming

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Area of Science:

  • Plant science
  • Genomics
  • Phenomics
  • Artificial Intelligence

Background:

  • Ensuring food security and ecosystem services necessitates breeding crops for high yield and climate adaptability.
  • Genomics and phenomics offer insights into plant responses to environmental changes.
  • A major challenge is linking genetic makeup (genotype) to observable traits (phenotype) for advanced breeding.

Purpose of the Study:

  • To review the current status of genomics and field phenomics.
  • To explore challenges and emerging approaches for integrating multiomics big data using next-generation artificial intelligence (AI).
  • To propose a viable strategy for improving crop breeding.

Main Methods:

  • Review of current genomics and field phenomics technologies.
  • Exploration of multiomics big data integration strategies.
  • Application of next-generation artificial intelligence (AI) for genotype-phenotype linkage.

Main Results:

  • Genomics and phenomics advancements provide valuable data but face challenges in genotype-phenotype interpretation.
  • Next-generation AI presents promising approaches for assimilating and interpreting complex biological data.
  • Effective data integration is crucial for optimizing crop breeding programs.

Conclusions:

  • Integrating multiomics data with advanced AI is essential for overcoming genotype-phenotype gaps in crop breeding.
  • This approach can enhance crop yield and adaptability to variable climates, supporting global food security.
  • A clear path forward involves leveraging AI for biologically meaningful interpretations of big data in plant science.