[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2399":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":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":42},2399,"Lightweight architecture optimization of YOLOv12n for improved cotton verticillium wilt detection","https:\u002F\u002Fwww.frontiersin.org\u002Farticles\u002F10.3389\u002Ffpls.2026.1822081\u002Ffull","Frontiers in Plant Science 17:1822081（2026）。Ye Zhuang等基于YOLOv12n框架提出轻量精准检测模型YOLO-SCOD。引入StarNet架构作为骨干网；颈网络C3k模块集成通道聚合块；检测头用全维动态卷积替代深度卷积。精度和召回率分别提升至0.960和0.911，mAP50-95提升6.436%；参数量、FLOPs、模型大小分别减少13.728%、20.635%、12.727%，推理速度提升4.167%。",null,"Frontiers in Plant Science | 2026-09","2026-09-08T00:00:00Z","论文",10,false,76,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,21,18,13,8,1,"基于YOLOv12n的轻量化检测模型在棉花黄萎病识别上兼顾精度与效率，方法新颖、数据扎实，对智慧植保具参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","棉花黄萎病","轻量化模型","作物病害检测",0,"10.3389\u002Ffpls.2026.1822081\u002Ffull",{"doi":32,"openalex_id":8,"authors":34,"venue":8,"cited_by_count":31,"oa_url":8,"card":35,"direction":39,"ingested_from":41},[],{"tldr":36,"method":37,"finding":38,"direction":39,"opportunity":40},"提出轻量模型YOLO-SCOD，实现棉花黄萎病精准检测。","基于YOLOv12n，引入StarNet骨干、C3k模块与全维动态卷积。","精度召回率达0.960和0.911，mAP提升6.436%，模型更轻更快。","农业遥感与作物表型","可探索轻量模型在移动端或无人机实时病害检测中的部署与泛化。","agent","2026-09-14T00:06:33.263987Z"]