{"id":128688,"date":"2026-01-15T10:02:43","date_gmt":"2026-01-15T10:02:43","guid":{"rendered":"https:\/\/chezaspin.com\/blog\/from-trial-and-error-to-algorithms-how-china-is-using-ai-to-rewrite-crop-breeding\/"},"modified":"2026-01-15T10:02:43","modified_gmt":"2026-01-15T10:02:43","slug":"from-trial-and-error-to-algorithms-how-china-is-using-ai-to-rewrite-crop-breeding","status":"publish","type":"post","link":"https:\/\/chezaspin.com\/blog\/from-trial-and-error-to-algorithms-how-china-is-using-ai-to-rewrite-crop-breeding\/","title":{"rendered":"From trial and error to algorithms: How China is using AI to rewrite crop breeding"},"content":{"rendered":"

BEIJING, China, Jan 15 \u2014 In the vast, sun-drenched fields of Yazhou Bay in Sanya, Hainan province, a quiet but monumental shift is taking place. Here, the practice of crop breeding is being rewritten not with a hoe, but with computer algorithms.<\/strong><\/p>\n

For generations, developing a superior seed variety was often an inexact science \u2014 a decade-long pursuit often relying heavily on a breeder\u2019s hunch. Now, a new initiative powered by artificial intelligence promises to slash that timeline in half, aiming to deliver resilient, high-yield crops in just three to four years.<\/p>\n

Officially known as the Future Agriculture Nexus, or Fan, the project is a joint creation of the Yazhou Bay National Laboratory and Chinese tech company Huawei Technologies Co. The hub aims to transform breeding into a precise, predictive science \u2014 a critical move for a nation safeguarding its food security in an era of climate uncertainty.<\/p>\n

The goal is in line with China\u2019s strategic needs, with seeds seen as the \u201cchips\u201d of global agriculture.<\/p>\n

During an inspection of the Yazhou Bay laboratory in April 2022,President Xi Jinping stressed the importance of pursuing agricultural technological breakthroughs to achieve self-dependence in the seed sector. \u201cWe should rely on Chinese seeds to ensure China\u2019s food security,\u201d he said.<\/p>\n

Yuan Xiaohui, a senior scientist at the Yazhou Bay National Laboratory, said that \u201cas the only national-level laboratory in China\u2019s agricultural sector, our lab\u2019s mission is to develop major strategic crop varieties to meet real demand\u201d.<\/p>\n

\u201cWe are fully aware that AI holds immense potential to empower agricultural science, but data remains the core bottleneck hindering its practical application,\u201d Yuan said.<\/p>\n

\u201cThere is an urgent need for us to build a system capable of integrating global field and laboratory data while providing intelligent analytical capabilities.\u201d<\/p>\n

Chen Fan, deputy director of the laboratory, outlined the fundamental shift required.<\/p>\n

\u201cTraditional breeding work relies heavily on experience. Moving from traditional to precision breeding requires analyzing correlations between massive amounts of data on crop traits and genotypes,\u201d Chen said.<\/p>\n

A researcher works at a laboratory of the China National Seed Group Co at Yazhou Bay in September. [Photo provided to CHINA DAILY]<\/p>\n

Connecting data islands<\/strong><\/h2>\n

For generations, breeders have operated like explorers in a vast, uncharted biological wilderness. The process of selecting parent plants, crossbreeding, and evaluating thousands of progeny over multiple growing seasons is painstakingly slow, with the success rate often below 1 percent, experts said.<\/p>\n

This challenge, Chen added, is compounded by deeply entrenched \u201cdata silos\u201d.<\/p>\n

\u201cData on genotype, phenotype, environment, and even soil are all kept separate. This fragmentation creates a critical bottleneck,\u201d he said.<\/p>\n

Yuan said: \u201cResearchers often know neither the source and quality of data, nor can they discern which data AI can understand. This causes AI to falter \u2014 or worse, produce erroneous results.\u201d<\/p>\n

It is this precise problem that the Fan project is engineered to solve, acting as a \u201ccentral nervous system\u201d to connect disparate data islands \u2014 a full-chain AI technical system built on Huawei\u2019s AI data solution.<\/p>\n

Yuan Yuan, vice-president of Huawei\u2019s data storage product line, said the Fan platform tackles the problem in three ways. First, it aggregates and standardizes multisource data on environment, traits, phenotype, and genotype from across the country.<\/p>\n

Second, it utilizes specialized tools to enable the rapid construction of customized, industry-specific AI large language models, which can cut model development time from 15 days to five, Yuan said.<\/p>\n

Finally, its core \u201cbreeding AI agent\u201d can intelligently screen this unified data, automate complex analysis workflows, and validate models to identify optimal breeding pathways, he said.<\/p>\n

\u201cThe impact is transformative,\u201d Yuan said.<\/p>\n

\u201cIt can shorten the traditional 20-generation cultivation cycle for crops like rice, which usually takes eight to 10 years, to just five generations, or three to four years.\u201d<\/p>\n

This represents a 50 percent reduction in the breeding cycle and can boost overall efficiency by an estimated 30 percent.<\/p>\n

The project represents more than a technical advancement. It is also a statement of strategic intent aligned with a national blueprint. \u201cThis intelligent system currently does not exist globally,\u201d said Chen.<\/p>\n

The goal is to rapidly advance the construction of the \u201cNanfan Silicon Valley\u201d and establish a leading hub for future agriculture.<\/p>\n

\u201cNanfan\u201d refers to a unique breeding method using Hainan\u2019s warm winters as a natural way to accelerate the process. According to a national plan, the Nanfan breeding base, located in Hainan, is set to evolve into the \u201cSilicon Valley\u201d of China\u2019s seed industry by 2030, serving as a comprehensive hub for agricultural research, industry, and technology exchange.<\/p>\n

This ambition mirrors high-level national directives. On Nov 13, 2025,China\u2019s Ministry of Agriculture and Rural Affairs convened a national conference to advance the seed industry revitalization action, charting the course for the 15th Five-Year Plan period (2026-30). The conference called for accelerating the realization of self-reliance and self-improvement in seed technology and securing a firm grip on seed sources.<\/p>\n

At the industry level, the plan emphasizes upgrading the Nanfan Silicon Valley scientific base into a national seed innovation hub that integrates research, commercialization, and application.<\/p>\n

\u201cDigitalization and intelligence are undoubtedly the future directions for building the Nanfan Silicon Valley,\u201d Chen said. \u201cWe must use advanced technology to serve and transform both agricultural production and research.\u201d<\/p>\n

This initiative is part of a broader push to harness AI for agricultural progress across the nation. In 2024,Yazhou Bay National Laboratory researchers, in collaboration with China Agricultural University and the Shanghai Artificial Intelligence Laboratory, developed China\u2019s first large language model for seed design, known as SeedLLM, or Fengdeng.<\/p>\n

This AI platform provides expert insights on breeding, cultivation and industry trends \u2014 empowering farmers and researchers with practical knowledge.<\/p>\n

In July 2025, Fengdeng was upgraded to an AI agent with three core research functions, said Yang Fan, a scientist at the laboratory.<\/p>\n

The first function is knowledge summarization, which addresses key questions like \u201cwhich traits are regulated by what type of genes\u201d. It does this by automatically integrating over 98 percent of relevant global crop research literature to build a gene-trait-environment association map.<\/p>\n

The second is gene-trait association prediction, enabling autonomous genome-wide screening of key genes beyond traditional reasoning.<\/p>\n

The third is experimental reasoning and design optimization, where it simulates expert logic to automate the entire research cycle from hypothesis generation and experimental design to result analysis, Yang said.<\/p>\n

Agricultural researchers study agronomic traits of rice at Nanfan breeding base, Yazhou Bay, on Feb 10. ZHAO YINGQUAN\/XINHUA<\/p>\n

Nationwide effort<\/strong><\/h2>\n

Agricultural innovation is also advancing at other Chinese institutions and research bodies.<\/p>\n

At the China National Seed Group, researchers use an AI-powered, cloud-based system to remotely monitor fields and collect real-time data on crop health, enabling prompt intervention.<\/p>\n

The Chinese Academy of Agricultural Sciences is also exploring the transition from experience-driven to data-driven breeding.<\/p>\n

In the past, breeders tested thousands of combinations to find a single superior hybrid. Now, AI-powered genomic analysis predicts high-yield combinations before field trials begin, said Li Huihui, deputy director of the National Nanfan Research Institute of the Chinese Academy of Agricultural Sciences.<\/p>\n

Li Jiayang, an academician at the Chinese Academy of Sciences, spoke highly of the concept of \u201cintelligent creation of intelligent varieties\u201d, underscoring the potential of integrating AI, biotechnology and information technology to develop crops that autonomously adapt to environmental challenges.<\/p>\n

Despite these advancements, challenges remain.<\/p>\n

\u201cOur country\u2019s total number of research papers in the seed field has surpassed that of the United States,\u201d said Wan Jianmin, an academician of the Chinese Academy of Engineering and former vice-president of the Chinese Academy of Agricultural Sciences.<\/p>\n

\u201cHowever, the connection between basic research and breeding application is not tight enough, and the innovation capacity in breeding theory and methodology is relatively weak,\u201d Wan said.<\/p>\n

Wan also highlighted gaps in frontier biotechnology.<\/p>\n

\u201cOur R&D capability and level in biotechnology still lag noticeably behind the US. This is evident in core patents. While China\u2019s core patent quantity ranks second globally, the US holds far more high-value patents and controls the majority of core biotechnology patents,\u201d he added.<\/p>\n

China\u2019s smart breeding sector also trails global seed giants in terms of data-sharing infrastructure and commercialization, said Qian Qian, another Chinese Academy of Sciences academician.<\/p>\n

\u201cGiven the complexity of crop traits, understanding the relationship between genes and traits requires computational power and advanced algorithms,\u201d Qian said.<\/p>\n

\u201cAccelerating the development of high-yield, high-quality and climate-resilient \u2018super varieties\u2019 is crucial,\u201d Qian said, calling for interdisciplinary collaboration among breeding institutions, AI researchers and agribusinesses, to drive innovations in smart breeding.<\/p>\n

Experts examine bananas at a trial plantation in Yazhou Bay science and technology city in July. YANG GUANYU\/XINHUA<\/p>\n

Global quest<\/strong><\/h2>\n

These endeavors have been built on a foundation of immense biological resources.<\/p>\n

China hosts the world\u2019s largest and most structurally diverse repository of agricultural germplasm, according to the Ministry of Agriculture and Rural Affairs.<\/p>\n

The latest national census of agricultural germplasm resources collected 139,000 new crop germplasm resources, providing a rich \u201csource supply\u201d for future breeding innovation.<\/p>\n

The industry\u2019s scale is also expanding. The domestic seed market value surpassed 150 billion yuan ($21.51 billion) for the first time in 2023, while R&D spending reached 7.6 billion yuan, a 20 percent increase from 2021, according to People\u2019s Daily.<\/p>\n

The use of AI in agriculture is not confined to China. Scientists and entrepreneurs worldwide are using algorithms to build a more resilient and productive food system.<\/p>\n

In the US, the drive is spearheaded by a vibrant ecosystem of startups emerging from top research hubs.<\/p>\n

Heritable Agriculture, a spin-off from Google X\u2019s moonshot factory, applies machine learning to analyze plant genomes, aiming to identify genetic combinations that enhance yield, reduce water use, and increase soil carbon storage \u2014 all without direct genetic modification.<\/p>\n

Researchers from Longping Biotechnology (Hainan) Co work on corn hybridization in Yazhou Bay science and technology city in February. ZHAO YINGQUAN\/XINHUA<\/p>\n

To share the technological progress it has made, the Yazhou Bay National Laboratory is also deepening international cooperation. In December, it signed a cooperation memorandum of understanding with agricultural research institutions from Colombia, Peru, Ecuador, and Chile.<\/p>\n

\u201cIt is positive to strengthen Global South collaboration, integrating experience and knowledge from both sides to tackle food security and sustainability issues,\u201d said Agustin Zsogon, a professor at Brazil\u2019s Federal University of Vicosa.<\/p>\n

Santiago Signorelli, a biochemistry professor at the University of the Republic in Uruguay, said China\u2019s advanced technologies hold great potential for contributing to scientific work in Uruguay.<\/p>\n

From the experimental fields of Hainan to farms worldwide, a common narrative is emerging. The daunting challenges of climate change and resource scarcity are being met with the converging power of advanced biology and AI.<\/p>\n

Huawei\u2019s Yuan said: \u201cOur future collaboration prospects are very broad; this is just the beginning.\u201d<\/p>\n

For more visit\u00a0China Daily<\/a><\/p>\n

For subscriptions on news from China Daily, or inquiries, please contact China Daily Africa Ltd on +254 20 6920900 or write to enquiries@chinadailyafrica.com<\/p>","protected":false},"excerpt":{"rendered":"

BEIJING, China, Jan 15 \u2014 In the vast, sun-drenched fields of Yazhou Bay in Sanya, Hainan province, a quiet but monumental shift is taking place. Here, the practice of crop breeding is being rewritten not with a hoe, but with computer algorithms. For generations, developing a superior seed variety was often an inexact science \u2014 […]<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-128688","post","type-post","status-publish","format-standard","hentry","category-uncategorized","entry"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/chezaspin.com\/blog\/wp-json\/wp\/v2\/posts\/128688","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/chezaspin.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/chezaspin.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/chezaspin.com\/blog\/wp-json\/wp\/v2\/comments?post=128688"}],"version-history":[{"count":0,"href":"https:\/\/chezaspin.com\/blog\/wp-json\/wp\/v2\/posts\/128688\/revisions"}],"wp:attachment":[{"href":"https:\/\/chezaspin.com\/blog\/wp-json\/wp\/v2\/media?parent=128688"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/chezaspin.com\/blog\/wp-json\/wp\/v2\/categories?post=128688"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/chezaspin.com\/blog\/wp-json\/wp\/v2\/tags?post=128688"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}