数字农科院2.0

Breeding 5.0: Artificial intelligence (AI)‐decoded germplasm for accelerated crop innovation

文献类型： 外文期刊

作者： 符加宜;;郑守志;;樊龙江;;郑晓明;;钱前

关键词： artificial intelligence (AI); breeding; explainable AI; germplasm resources; robotics

期刊名称： Journal of Integrative Plant Biology

ISSN： 1672-9072

年卷期： 2025 年

页码：

收录情况： SCIE（2025版） ; ; CSCD（2025-2026年度） ; ; 科技核心（2024版）

摘要： Crop breeding technologies are vital for global food security. While traditional methods have improved yield, stress tolerance, and nutrition, rising chal- lenges such as climate instability, land loss, and pest pressure now demand new solutions. This study introduces the Breeding 5.0 framework, driven by artificial intelligence (AI) and robotics, marking a shift from empirical selection to intelligent systems. Central to this transformation is AI's emerging ability to deeply “understand germplasm,” not merely by identifying genetic markers but also by decoding its architecture, plasticity, regulatory logic, and envi- ronmental interactions. This germplasm intelligence enables predictive trait modeling, optimized parental design, and targeted selection. We define four technical paradigms enabling this shift: (i) Multi- modal data integration to bridge genotype and phenotype; (ii) Omni‐simulated environments for virtual performance testing; (iii) Peopleless data capture for scalable precision; and (iv) Expert, explainable AI for biologically grounded decisions. Together, these technologies algorithmically convert germplasm into actionable breeding insights, accelerating the full cycle from ideal plant type de- sign to elite line development. We further propose the “breeding flywheel,” a self‐reinforcing system that continuously amplifies phenotypic gains and refines breeding strategies, thereby enabling faster and smarter crop improvement to ensure a sustainable food future.

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