[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2607":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":8,"paper":32,"created_at":41},2607,"YOLO-Based Deep Learning Pipeline for Detecting Nutrient Deficiency Symptoms in Roselle (Hibiscus sabdariffa L.)","https:\u002F\u002Fwww.mdpi.com\u002F2673-4117\u002F7\u002F9\u002F472","Ramírez-Pedraza等发表。研究基于YOLO的深度学习流程检测玫瑰茄中钙、钾、铁、磷缺乏的视觉症状及健康样本。在为期8周的温室条件下使用多设备数据集评估YOLOv11、YOLOv12、YOLOv13架构。YOLOv13达到最高的整体定位精度，YOLOv12在精度-召回平衡上表现最佳，YOLOv11每周性能最稳定。",null,"MDPI Eng 7(9):472","2026-09-12T01:00:00Z","论文",10,false,73,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},15,20,17,13,8,1,"基于YOLO多版本对比的作物营养缺乏视觉检测研究，方法新颖、数据规模明确，对智慧农业病害诊断有参考价值，但属细分作物应用，影响范围有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","目标检测","玫瑰茄","作物营养诊断",0,{"doi":8,"openalex_id":8,"authors":33,"venue":8,"cited_by_count":31,"oa_url":8,"card":34,"direction":38,"ingested_from":40},[],{"tldr":35,"method":36,"finding":37,"direction":38,"opportunity":39},"用YOLO系列模型检测玫瑰茄钙钾铁磷缺乏的视觉症状。","8周温室多设备图像数据集，对比YOLOv11\u002Fv12\u002Fv13架构。","YOLOv13定位精度最高，YOLOv12精度-召回最均衡，YOLOv11周稳定性最好。","农业人工智能与决策模型","可扩展到多作物、田间自然场景及早期缺素预警与施肥决策联动。","agent","2026-09-16T00:03:52.146855Z"]