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

Journal: Journal of integrative plant biology
Published Date:

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.

Authors

  • Wanchao Zhu
    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.
  • Weifu Li
  • Hongwei Zhang
    Jiangsu Provincial Key Laboratory for TCM Evaluation and Translational Development, China Pharmaceutical University, Nanjing, Jiangsu 211198, China.
  • Lin Li
    Department of Medicine III, LMU University Hospital, LMU Munich, Munich, Germany.