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Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs.

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Plant breeding utilizes big data and artificial intelligence (AI), specifically machine learning (ML), to accelerate crop improvement for enhanced food security. This review explores AI and ML applications, challenges, and opportunities in modern plant breeding for climate resilience.

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Area of Science:

  • Plant Science
  • Agricultural Technology
  • Bioinformatics

Background:

  • Global population growth necessitates sustainable food security solutions.
  • High-throughput omics technologies generate vast plant genetic data.
  • Climate change, pests, and diseases threaten crop yields.

Purpose of the Study:

  • To review the application of big data, artificial intelligence (AI), and machine learning (ML) in plant breeding.
  • To discuss the challenges and opportunities presented by these technologies.
  • To explore data integration strategies and future prospects for novel algorithms.

Main Methods:

  • Review of existing literature on big data, AI, and ML in plant breeding.
  • Analysis of commonly used machine learning algorithms and their functions.
  • Discussion of data integration strategies for breeding datasets.

Main Results:

  • Big data and ML offer powerful tools for analyzing complex plant genetic information.
  • AI and ML can accelerate the development of new plant varieties with improved traits.
  • Effective data integration is crucial for leveraging machine learning in breeding.

Conclusions:

  • Machine learning algorithms provide efficient tools for plant breeders.
  • The integration of big data and AI/ML can revolutionize plant breeding and enhance food security.
  • Addressing challenges and exploring novel algorithms will further advance crop improvement.