[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2611":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"view_count":30,"doi":8,"paper":31,"created_at":40},2611,"无人机遥感在水稻高通量表型分析中的研究进展 系统综述","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260911171723051.htm","发表于Smart Agricultural Technology。依据PRISMA 2020规范系统检索文献最终纳入199项研究(2014–2026年)。研究发现：先进传感、特征集成和建模技术日益支持氮素和叶绿素估算及产量预测；轻量级模型和边缘计算系统在倒伏和病害监测任务中显示出实时部署的可行性；跨区域泛化受环境背景干扰以及地点品种偏倚制约；199项研究中有6项(3.0%)将UAV衍生性状与遗传关联分析联系起来。",null,"Smart Agricultural Technology","2026-09-11T01:00:00Z","论文",10,false,77,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,13,6,1,"基于PRISMA规范纳入199项研究的系统综述，方法严谨、数据规模大，对水稻表型与智慧育种有实质参考价值，但属细分领域学术进展，公共影响有限。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","水稻","无人机遥感","高通量表型",0,{"doi":8,"openalex_id":8,"authors":32,"venue":8,"cited_by_count":30,"oa_url":8,"card":33,"direction":37,"ingested_from":39},[],{"tldr":34,"method":35,"finding":36,"direction":37,"opportunity":38},"系统综述199项研究，梳理无人机遥感在水稻高通量表型分析中的应用进展与瓶颈。","依据PRISMA 2020系统检索2014–2026年199项研究并归纳分析。","传感与建模支撑氮素、产量预测，跨区域泛化受环境与品种偏倚制约，基因关联研究仅占3%。","农业遥感与作物表型","UAV表型与遗传关联分析严重不足，可探索跨区域泛化建模及表型-基因型融合方向。","agent","2026-09-16T00:03:52.470505Z"]