AI and Big Data Drive the Future of Crop Breeding with Intelligent Precision

Scientists are using AI and big data to revolutionize crop breeding, accelerating genetic discoveries and optimizing traits for better yield, resilience, and sustainability in the face of global food challenges.

Research: Revolutionizing Crop Breeding: Next-Generation Artificial Intelligence and Big Data-Driven Intelligent Design. Image Credit: Shutterstock AI

Research: Revolutionizing Crop Breeding: Next-Generation Artificial Intelligence and Big Data-Driven Intelligent Design. Image Credit: Shutterstock AI

A new study published in the journal Engineering explores how next-generation artificial intelligence (AI) and big data are revolutionizing crop breeding, with potentially 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 improves the efficiency and precision of trait acquisition and helps identify 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 essential functional genes and regulatory pathways. This approach accelerates gene function discovery and helps construct 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 technology development in the seed industry still lags behind international leaders in several aspects. Although China has made progress in germplasm resource identification and digital breeding transformation, 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.

This research provides valuable insights into the future of crop breeding. As AI and big data technologies continue to evolve, they will likely play an even more significant role in ensuring global food security by enabling more efficient and sustainable crop breeding practices.

Source:
Journal reference:
  • Ying Zhang, Guanmin Huang, Yanxin Zhao, Xianju Lu, Yanru Wang, Chuanyu Wang, Xinyu Guo, Chunjiang Zhao. Revolutionizing Crop Breeding: Next-Generation Artificial Intelligence and Big Data-Driven Intelligent Design. Engineering, 2025, 44(1): 245‒255 DOI: 10.1016/j.eng.2024.11.034, https://www.engineering.org.cn/engi/EN/10.1016/j.eng.2024.11.034

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