[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2492":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},2492,"BiFormer-Enhanced YOLOv11n for Accurate Maize Ear Detection in Seed Production Fields(玉米穗识别 BiF-YOLO)","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181780","青岛农业大学机电工程学院 2026 年 9 月 10 日在《Agronomy》(Section Precision and Digital Agriculture)发表。提出 BiF-YOLO 模型基于 YOLOv11n 和 BiFormer 架构,三项目标性增强策略:①在特征提取网络嵌入 C3k2_AdditiveBlock 模块以增强田间被遮挡和小型玉米穗的识别能力;②构建 C2BRA 双层路由注意力机制(Cross-stage partial 2 Bottleneck with Residual Attention)以抑制背景干扰;③采用 ShapeIoU 损失函数。从胶州和张掖种子生产田采集玉米穗图像。BiF-YOLO 模型有效适应复杂田间作业场景,为制种玉米收获机的实时识别与产量估计提供技术支撑。",null,"MDPI Agronomy 2026-09-10","2026-09-09T16:00:00Z","论文",10,false,74,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,21,17,13,7,1,"面向制种玉米收获的轻量化检测模型改进，方法有针对性增强且数据来自真实制种田，对智能农机与产量估计有实用价值，但属细分技术进展，影响面有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","种业振兴","玉米","目标检测",0,"10.3390\u002Fagronomy16181780",{"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BiF-YOLO模型，在制种田实现玉米穗精准检测。","基于YOLOv11n，融合BiFormer、C3k2_AdditiveBlock","BiF-YOLO有效适应复杂田间场景，提升遮挡和小目标玉米穗检测精度。","农业人工智能与决策模型","可探索轻量化模型在收获机边缘设备上的实时部署与产量估计集成。","agent","2026-09-15T00:04:27.207493Z"]