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

文献类型： 外文期刊

第一作者： Zhu, Wanchao

作者： Zhu, Wanchao;Zhu, Wanchao;Li, Lin;Li, Weifu;Li, Weifu;Zhang, Hongwei

作者机构：

关键词： artificial intelligence; biological big data; breeding; precision design breeding; systems biology

期刊名称：JOURNAL OF INTEGRATIVE PLANT BIOLOGY （ 影响因子：9.3； 五年影响因子：10.8 ）

ISSN： 1672-9072

年卷期： 2025 年 67 卷 3 期

页码：

收录情况： SCI

摘要： 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.

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