J Integr Plant Biol.

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Big data and artificial intelligence-aided crop breeding: Progress and prospects

Wanchao Zhu1,2†, Weifu Li3,4†, Hongwei Zhang5* and Lin Li2*   

  1. 1. Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
    2. National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
    3. College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
    4. Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan 430070, China
    5. State Key Laboratory of Crop Gene Resources and Breeding, National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    These authors contributed equally to the article.
    *Correspondences: Hongwei Zhang (zhanghongwei@caas.cn); Lin Li (hzaulilin@mail.hzau.edu.cn, Dr. Li is fully responsible for all materials associated with this article)
  • Received:2024-06-30 Accepted:2024-09-10 Online:2024-10-28
  • Supported by:
    This work was supported by the National Key Research and Development Program of China (2023YFF1000100) (to L.L. and W.L.) and the National Natural Science Foundation of China (32321005) (to L.L.).

Abstract: The past decade has witnessed rapid developments in gene discovery, biological big data (BBD), artificial intelligence (AI)-aided technologies, and molecular breeding. These advancements are expected to accelerate crop breeding under the pressure of increasing demands for food. Here, we first summarize current breeding methods and discuss the need for new ways to support breeding efforts. Then, we review how to combine BBD and AI technologies for genetic dissection, exploring functional genes, predicting regulatory elements and functional domains, and phenotypic prediction. Finally, we propose the concept of intelligent precision design breeding (IPDB) driven by AI technology and offer ideas about how to implement IPDB. We hope that IPDB will enhance the predictability, efficiency, and cost of crop breeding compared with current technologies. As an example of IPDB, we explore the possibilities offered by CropGPT, which combines biological techniques, bioinformatics, and breeding art from breeders, and presents an open, shareable, and cooperative breeding system. IPDB provides integrated services and communication platforms for biologists, bioinformatics experts, germplasm resource specialists, breeders, dealers, and farmers, and should be well suited for future breeding.

Key words: artificial intelligence, biological big data, breeding, precision design breeding, systems biology

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