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Updated: Aug 29, 2025

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Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.

Yunbi Xu1, Xingping Zhang2, Huihui Li3

  • 1Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.

Molecular Plant
|September 9, 2022
PubMed
Summary

Smart breeding integrates multiomics data and artificial intelligence to predict plant performance, enhancing crop redesign for genetic gain. This approach, called integrated genomic-enviromic prediction (iGEP), addresses genotype by environment interactions.

Keywords:
artificial intelligencebig datacrop designgenomic selectionintegrated genomic-enviromic selectionmachine and deep learningsmart breedingspatiotemporal omics

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Area of Science:

  • Plant breeding and genetics
  • Computational biology
  • Agricultural science

Background:

  • Traditional plant breeding relies on phenotypic observation and statistical models.
  • Plant performance is influenced by genotype (G), environment (E), and their interaction (GEI).
  • Predictive breeding aims to improve accuracy by integrating multi-source data, including omics and spatiotemporal information.

Purpose of the Study:

  • To review innovative technologies for predictive breeding.
  • To evaluate multidimensional information profiles, emphasizing underutilized envirotypic data.
  • To propose an integrated genomic-enviromic prediction (iGEP) scheme for smart breeding.

Main Methods:

  • Review of innovative predictive breeding technologies.
  • Evaluation of multiomics data integration, including genomics, phenomics, and enviromics.
  • Proposal of the integrated genomic-enviromic prediction (iGEP) scheme utilizing big data and AI (machine/deep learning).

Main Results:

  • Identified opportunities and challenges in integrating 3D (G-P-E) information profiles.
  • Highlighted the neglect of envirotypic data in current predictive breeding models.
  • Proposed iGEP as an extension of genomic prediction for enhanced accuracy.

Conclusions:

  • iGEP offers a smart breeding scheme using integrated multiomics, big data, and AI.
  • A strategy for prediction-based crop redesign at macro and micro scales is proposed.
  • Coordinated efforts in smart breeding, iGEP, and open-source initiatives are crucial for genetic gain.