Next-Generation AI and Big Data: Transforming Crop Breeding

A new study published in Engineering explores how next-generation artificial intelligence (AI) and big data are revolutionizing crop breeding, with potential far-reaching implications for global food security.

Crop breeding has come a long way, evolving through distinct stages from domestication breeding to the current era of big data intelligent design breeding. The latest stage, “Breeding 4.0,” integrates biotechnology, big data, and AI. This convergence aims to achieve efficient, personalized breeding of new crop varieties, marking a significant shift from traditional “scientific” to “intelligent” breeding approaches.

One of the key applications of AI and big data in crop breeding is high-throughput phenotyping. Traditional methods of trait acquisition were limited, but new phenotyping equipment systems, leveraging sensors and AI, can now collect high-throughput, automated phenotypic data. For example, high-resolution UAV photography can identify various crop traits, and advanced platforms can conduct continuous nondestructive testing under adverse conditions. This not only improves the efficiency and precision of trait acquisition but also helps in identifying stress resistance genes, promoting intelligent and precise breeding.

Multiomics databases and management systems have also been crucial in this transformation. These databases integrate various omics data, offering a more comprehensive view of genetic variation. For instance, databases like ZEAMAP for maize and SoyMD for soybean provide rich data resources for researchers to mine candidate genes and understand genetic regulatory mechanisms.

AI-based integrated multi-omics analysis is another significant advancement. By analyzing complex genetic regulatory networks, scientists can better understand crop traits. A research team from Huazhong Agricultural University constructed a multi-omics integrated network map for maize, accurately predicting important functional genes and regulatory pathways. This approach accelerates gene function discovery and helps in constructing precise regulatory network models.

The development of AI-powered breeding software tools further accelerates crop improvement. These tools, integrate big data and AI to optimize breeding decisions, shorten breeding cycles, and improve selection accuracy.

However, China’s seed industry technology development still lags behind international leaders in several aspects. Although China has made progress in germplasm resource identification and digital transformation of breeding, there are gaps in scientific innovation, core technologies, intelligent breeding systems, germplasm resource utilization, and market competitiveness.

To address these challenges, the study proposes a focus on developing automated intelligent crop phenotype acquisition technology, advancing information fusion mechanisms, and creating omics big data analysis algorithms. By 2040, China aims to develop frontier core technologies, establish a precision breeding decision system, and transform its seed industry through multidisciplinary integration, data-driven precision breeding, and collaborative innovation platform construction.