关键词: 根系, 人工智能, 深度学习, 智慧农业, AI4Root 

Abstract: With AI4Science (AI-driven scientific discovery) emerging as a new paradigm for scientific discovery, artificial intelligence (AI) is reshaping plant science toward data-driven and intelligent frameworks. 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 artificial intelligence have provided new tools and pathways for decoding the “underground black box.” Leveraging AI as a core driving force to integrate multi-source root and environmental data, cutting-edge algorithms, and the broader knowledge base of root science has increasingly become a frontier direction in belowground research. In this context, we outline AI4Root, which summarizes 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 also shows great potential in root phenotyping, architectural simulation, and deciphering root exudate-microbiome interactions, offering unprecedented tools to advance our understanding of root function. However, the application 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, offering innovative pathways for crop improvement and the advancement of smart agriculture.

Key words: roots, artificial intelligence, deep learning, smart farming, AI4Root