A recent study in Engineering reveals how artificial intelligence (AI) and big data are revolutionizing crop breeding, a shift that holds significant implications for global food security. This “Breeding 4.0” approach, merging AI, big data, and biotechnology, represents a move from traditional breeding to intelligent, customized crop development.
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High-throughput phenotyping is one of the main uses of AI and big data in crop breeding. Traditional methods had few options for acquiring traits, but new phenotyping equipment systems that use sensors and artificial intelligence (AI) can now gather automated, high-throughput phenotypic data.
Sophisticated platforms can carry out ongoing nondestructive testing in challenging circumstances, and high-resolution UAV photography can detect various crop characteristics. This promotes intelligent and accurate breeding by increasing the efficiency and accuracy of trait acquisition as well as aiding in the identification of genes that resist stress.
Databases and management systems for multiomics have also played a significant role in this change. These databases provide a more thorough understanding of genetic variation by integrating different omics data. For example, researchers can mine candidate genes and comprehend genetic regulatory mechanisms using databases such as ZEAMAP for maize and SoyMD for soybeans, which offer rich data resources.
Another important development is AI-based integrated multi-omics analysis. Scientists can better understand crop traits by examining intricate genetic regulatory networks. To accurately predict significant functional genes and regulatory pathways, a research team from Huazhong Agricultural University created a multi-omics integrated network map for maize. This method aids in the creation of accurate regulatory network models and speeds up the discovery of gene functions.
The creation of AI-driven breeding software tools speeds up crop improvement even more. These tools combine AI and big data to increase selection accuracy, reduce breeding cycles, and optimize breeding decisions.
However, China's technological advancements in the seed industry continue to lag behind those of global leaders in several areas. The identification of germplasm resources and the digital transformation of breeding have advanced in China, but there are still gaps in core technologies, scientific innovation, intelligent breeding systems, the use of germplasm resources, and market competitiveness.
The study suggests improving information fusion mechanisms, developing omics big data analysis algorithms, and developing automated intelligent crop phenotype acquisition technology to address these issues. Through multidisciplinary integration, data-driven precision breeding, and the development of collaborative innovation platforms, China hopes to transform its seed industry by 2040, develop frontier core technologies, and set up a precision breeding decision system.
This study offers important new information about crop breeding's future. By facilitating more effective and sustainable crop breeding methods, artificial intelligence (AI) and big data technologies will probably become even more important in guaranteeing global food security as they develop.
Journal Reference:
Zhang, Y., et al. (2025) Revolutionizing Crop Breeding: Next-Generation Artificial Intelligence and Big Data-Driven Intelligent Design. Engineering. doi.org/10.1016/j.eng.2024.11.034