[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2742":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":61},2742,"LeafTrackNet: A deep learning framework for robust leaf tracking in top-down plant phenotyping","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112417","LeafTrackNet: A deep learning framework for robust leaf tracking in top-down plant phenotyping。Computers and Electronics in Agriculture","LeafTrackNet：一种用于自上而下植物表型分析中稳健叶片追踪的深度学习框架。",null,"Computers and Electronics in Agriculture","2026-09-16T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,17,14,9,1,"核心期刊发表的叶片追踪深度学习框架，方法有创新但属细分技术进展，产业影响有限，可作为农业AI主题聚合素材。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","深度学习","作物监测","植物表型",0,"10.1016\u002Fj.compag.2026.112417",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":54,"direction":58,"ingested_from":60},"W7213430727",[37,40,43,45,47,49,52],{"name":38,"orcid":39},"Shanghua Liu","https:\u002F\u002Forcid.org\u002F0009-0009-9855-9040",{"name":41,"orcid":42},"Majharulislam Babor","https:\u002F\u002Forcid.org\u002F0000-0002-5440-7573",{"name":44,"orcid":9},"Christoph Verduyn",{"name":46,"orcid":9},"Breght Vandenberghe",{"name":48,"orcid":9},"Bruno Betoni Parodi",{"name":50,"orcid":51},"Cornelia Weltzien","https:\u002F\u002Forcid.org\u002F0000-0002-2951-3614",{"name":53,"orcid":9},"Marina M. -C. Höhne",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"提出LeafTrackNet深度学习框架，实现俯视植物表型中稳健的叶片追踪。","基于深度学习的叶片追踪框架，用于俯视植物表型图像序列。","该框架能稳健追踪叶片，提升植物表型分析的自动化与准确性。","农业遥感与作物表型","可探索多物种、遮挡与生长形变下的长期叶片追踪及三维表型融合。","openalex","2026-09-17T23:30:01.562015Z"]