Xian Zhang , Jiamin Wang , Yunchao Huang

Hainan Provincial Key Laboratory of Crop Molecular Breeding, Sanya, 572025, Hainan, China
Author    Correspondence author
Biological Evidence, 2025, Vol. 15, No. 5   
Received: 26 Aug., 2025    Accepted: 30 Sep., 2025    Published: 15 Oct., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

With the development of high-throughput omics, remote sensing and artificial intelligence, big data is transforming corn breeding. Research shows that the combination of machine learning and multi-omics can better predict and screen the yield and stress resistance of corn, and also accelerate the breeding speed of new varieties. The emergence of unmanned aerial vehicle (UAV) sensors, deep learning, and federated learning has made high-throughput phenotyping, early yield prediction, and multi-party collaborative breeding work easier to achieve. Meanwhile, the multi-genome database of corn and the intelligent analysis platform have also laid the foundation for the integration and sharing of global resources. Of course, this process also poses many challenges, such as different data sources, the complexity of biological issues themselves, and the influence of socio-economic factors. Overall, however, big data has become an important force driving corn breeding to be more intelligent, precise and sustainable. Next, it is necessary to strike a balance between technological innovation and green development and enhance cooperation. Our research objective is to explore how these new methods can be utilized to help corn breeding serve global food security more efficiently.

Corn breeding; Big data analysis; Machine learning; High-throughput phenotype; Intelligent breeding