AI4Root:人工智能驱动的植物根系研究进展OA
AI4Root:a Framework to Advance AI-Driven Plant Root Research
随着科学智能(AI for science,AI4Science)(即用人工智能技术来驱动科学研究)作为科学研究新范式的兴起,人工智能(AI)正深刻推动植物生命系统的研究向数据驱动与智能化转型.根系作为植物获取水分和养分、感知环境胁迫并参与地上-地下互作的关键器官,对作物产量和生态系统的功能起着决定性作用.然而,由于根系生长在土壤中,其复杂表型难以直接观测,导致对根系结构和功能的认识长期滞后于地上部分.人工智能的发展为破解这一"地下黑箱"提供了新工具和新路径.如何以人工智能为核心驱动力,整合多源的根系与环境数据、前沿算法及根系科学知识体系,进而揭示根系结构与功能的关键关系,成为当前根系研究的前沿方向.该文从数据获取、结构建模、机制推理和管理决策等方面系统综述了AI在根系研究中的主要应用与发展趋势,基于AI的图像分析可实现根系结构的自动识别与量化;建模与数据融合可揭示根系-土壤多尺度、多过程的互作.虽然AI在根系表型测定、构型模拟、分泌物及微生物互作解析等方面显示出巨大潜力,为深入理解根系功能提供了前所未有的手段.然而,AI应用于根系研究仍面临田间高质量数据获取困难、不同实验尺度之间的数据融合不足,以及根系-土壤-环境耦合过程的模拟与刻画不充分等挑战.该文对根系研究如何依托多源数据深度整合、跨学科算法优化及数字农业平台协同应用进行探讨,以推动相关关键核心技术突破,促进根系科学向智能化、精准化方向发展,为新一代根系育种和资源高效利用提供理论依据与技术支撑.
With AI for Science(AI4Science)emerging as a new paradigm for scientific research,artificial intelligence(AI)is driving a transition toward data-driven and intelligent research in plant science.Roots,as the key organs responsible for water and nutrient acquisition,environmental sensing,and above-belowground interactions,are decisive for crop productivity and ecosystem functioning.However,due to their hidden growth in soil,structural complexity,and limited observability,our understanding of root structure and function has long lagged behind that of aboveground parts.Recent advances in AI have provided new tools and pathways for decoding the"underground black box".Using AI as a key driver to integrate mul-ti-source root and environmental data,advanced algorithms,and the broader knowledge base of root science has emerged as a frontier direction in belowground research.In this context,we systematically review the major applications and emerging trends of AI in root studies,encompassing data acquisition,structural modeling,mechanistic inference,and management decision support.AI-based image analysis enables automated recognition and quantification of root architectures,while modeling and data-fusion approaches reveal the multi-scale and multi-process interactions between roots and soil.AI ap-plications in root phenotyping,architectural simulation,and deciphering root exudate-microbiome interactions are also showing great potential,offering unprecedented tools to advance our understanding of root function.However,the applica-tion of AI in root research still faces several challenges,including the difficulty of acquiring high-quality field data,insufficient integration across experimental scales,and limited capacity to model the complexity of root-soil-environment interactions.Looking ahead,the deep integration of multi-source data,cross-disciplinary algorithmic advancements,and the coordinated development of digital agriculture platforms are expected to propel root science into a new era of intelligent research,of-fering innovative pathways for crop improvement and the advancement of smart agriculture.
方博;张玉成;郑聪聪;高树琴;段世名;马慧敏;赵洪龙;蒋浩;杨艳敏;龙隆;贺祖光
德国于利希研究中心植物科学研究所(IBG-2),于利希52428中国科学院计算技术研究所,北京 100190中国科学院计算技术研究所,北京 100190中国科学院计算技术研究所,北京 100190中国农业大学水利与土木工程学院,北京 100193吉林农业大学农学院,长春 130118中国科学院计算技术研究所,北京 100190中国科学院计算技术研究所,北京 100190中国科学院遗传与发育生物学研究所农业资源研究中心,石家庄 050021中国科学院计算技术研究所,北京 100190中国科学院计算技术研究所,北京 100190
根系人工智能深度学习智慧农业AI4Root
rootsartificial intelligencedeep learningsmart agricultureAI4Root
《植物学报》 2026 (4)
598-610,13
中国科学院科研项目
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