Insights from the development of smart breeding <?A3B2 pi6?>in developed countries

Global food security is increasingly threatened by the combined pressures of rapid population growth, accelerating climate change, and persistent geopolitical instability. Conventional crop breeding, characterized by long breeding cycles, relatively low efficiency, and heavy reliance on breeders’ empirical experience, struggles to meet the urgent demands of future agricultural production. In this context, the integration of high-throughput omics, artificial intelligence (AI), and big data analytics has given rise to “smart breeding”, a transformative paradigm widely recognized as a central driver of global seed industry competition. By enhancing efficiency, precision, and sustainability, smart breeding creates unprecedented opportunities for the development of breakthrough crop varieties, thereby offering new solutions to the food security crisis. For China, the world’s most populous country and a key player in agricultural science and technology, accelerating smart breeding innovation is essential both to meet domestic food demand and to contribute to the stability of the global food system.In recent years, developed economies have substantially increased policy support and investment in smart breeding. Governments have launched dedicated funding programs and strategic frameworks; research institutions are initiating new basic and applied research agendas; leading seed enterprises are actively adopting AI-based technologies; and AI-focused technology companies are expanding into agriculture, further deepening the convergence of digital and biological sciences. A systematic assessment of these countries’ strategic orientations, institutional arrangements, and industrial practices is thus critical for informing China’s strategic planning in this field.This article aims to analyze and compare the policy frameworks, investment mechanisms, and developmental trajectories of agriculturally advanced nations—represented by the United States, the European Union (with the Netherlands as a representative case), and Japan—regarding their promotion of smart breeding. Using policy document analysis, comparative case studies, and systematic literature review, we traced the conceptual evolution of smart breeding: from its early stage as the auxiliary use of AI in conventional breeding, to its current role as a comprehensive, data-driven paradigm. While the concept is still evolving, an international consensus is emerging that smart breeding builds upon AI-enabled multi-omics data acquisition, integration, and deep mining, with the ultimate goal of breeding climate-resilient crop varieties in shorter cycles and at lower costs, while better addressing human needs.Technological features of smart breeding can be summarized into three dimensions. First, the deep integration of AI and modern biotechnology has become a defining hallmark, fostering cross-disciplinary innovations. Second, AI technologies are increasingly embedded throughout the breeding process, from genetic information decoding and high-throughput phenotyping to data integration, trait prediction, and target discovery. Third, these innovations substantially improve genetic enhancement efficiency, facilitating precise identification, combination, and engineering of trait-related molecular modules.Comparative analysis highlights three distinctive development models. The United States emphasizes frontier research in agricultural AI by leveraging multidisciplinary collaboration and large-scale federal funding. The Netherlands has established specialized national-level institutions that integrate breeding science with digital technologies, highlighting the role of institutional concentration. Japan has built a data-driven breeding ecosystem structured around government-led platforms, thereby facilitating integration of national databases and breeding resources. These divergent approaches reflect the interplay of national contexts, technological foundations, and institutional designs, demonstrating that smart breeding is evolving along multiple pathways rather than converging toward a single dominant model.Drawing on international experience and China’s unique circumstances, this article proposes three recommendations: strengthening top-level policy design and sustained public investment, establishing cross-disciplinary research institutions and national data platforms, and fostering collaboration between seed enterprises and AI technology firms. Together, these measures could accelerate the formation of a uniquely Chinese smart breeding system, significantly enhance breeding efficiency, advance sustainable agricultural development, and provide stronger scientific support for global food security. In doing so, China is positioned to play a more responsible and influential role in shaping the future of global agriculture in the smart breeding era.