生成式AI赋能都市农业的逻辑机理与困境OA

This study constructed a"knowledge-data-space"triad coupling framework,analyzed three empowerment mechanisms:converted implicit agronomic knowledge into actionable instructions,synthesized scarce training data,and optimized agricultural spa-tial layouts to formed an intelligent closed loop.Simultaneously,it revealed four major dilemmas:data fragmentation,small-scale data,and privacy concerns;model hallucinations;conflicts between AI carbon emissions and decarbonization goals;"secondary knowledge payment"triggered by technological monopolies;and ambiguous responsibilities with governance gaps.Finally,it proposed targeted multi-level governance strategies:resolving data challenges through data trusts and federated learning,alleviating energy pressures via green electricity and edge computing,balancing power structures through open-source communities and public interventions,and ad-dressing governance challenges with regulatory sandboxes and insurance mechanisms.This research provides theoretical and practical support for generative AI innovation in urban agriculture.