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Artificial intelligence (AI) is revolutionizing plant breeding by integrating diverse data for improved crop traits. While not a universal solution, AI offers significant potential to enhance breeding efficiency, sustainability, and global food security.

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Area of Science:

  • Plant breeding and genetics
  • Agricultural science
  • Computational biology

Background:

  • Vast datasets from diverse sources are essential for advancing crop improvement.
  • Artificial intelligence (AI) methods have a long history in breeding, with recent advancements offering new potential.
  • Integrating multi-scale, multi-platform, and multi-species data is key for modern breeding programs.

Purpose of the Study:

  • To explore the transformative potential of AI in plant breeding.
  • To highlight AI's role in integrating diverse data streams for enhanced crop improvement.
  • To discuss AI applications in predicting traits, biological inferences, and optimizing breeding cycles.

Main Methods:

  • Review of existing AI applications in plant breeding.
  • Analysis of data integration capabilities of AI tools.
  • Exploration of AI's impact on predictive accuracy and biological insights.

Main Results:

  • AI applications span data mining, phenotyping, genetics, multi-omics, environmental monitoring, and management practices.
  • AI can improve predictive accuracy for plant traits, accelerating breeding cycles and resource management.
  • AI offers potential for enhanced biological inferences, gene discovery, and gene editing.

Conclusions:

  • AI is transforming plant breeding, with significant potential for improving crop resilience, yield, and sustainability.
  • Successful data integration via AI can support global food security and inspire future breeding technologies.
  • While AI shows promise, its full potential is still unfolding, with mixed results depending on specific applications and datasets.