[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2656":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":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":71},2656,"DeepPhenoTree-Apple Edition: a multi-site apple phenology RGB annotated dataset with deep learning baseline models","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13007-026-01591-w","Abstract In machine learning-driven plant phenotyping, well-annotated image datasets are essential for developing robust models capable of capturing phenological variability across environments. Here, we introduce DeepPhenoTree-Apple Edition , a multi-site and multi-variety RGB image dataset dedicated to the detection of key phenological stages in apple trees. The dataset includes 48,320 time-stamped RGB images acquired across four European orchards of the Apple REFPOP consortium under biological, agronomic, and environmental variability, including differences in genotypes, orchard architectures, phenological development, temperature, and humidity conditions. From this large corpus, a carefully curated subset of 808 representative images was manually annotated. It includes 241,600 expert annotations covering developmental stages from dormant bud to fruit maturity. Images were acquired using a standardized tractor-mounted phenotyping platform equipped with active flash illumination. Active flash illumination was used to reduce illumination variability and homogenize exposure, shadows, and sunlight differences across sites. Phenological structures were annotated following the BBCH scale, with bounding boxes adapted to organ visibility and developmental stage. In addition to the dataset, we provide deep-learning baseline experiments to illustrate detection performance and detection performance across locations.","在机器学习驱动的植物表型分析中，标注良好的图像数据集对于开发能够捕捉不同环境下物候变异的稳健模型至关重要。在此，我们介绍DeepPhenoTree-Apple Edition，一个用于检测苹果树关键物候阶段的多地点、多品种RGB图像数据集。该数据集包含48,320张带有时间戳的RGB图像，采集自Apple REFPOP联盟的四个欧洲果园，涵盖了生物学、农艺学和环境变异，包括基因型、果园结构、物候发育、温度和湿度条件的差异。从这一大型语料库中，我们精心挑选了808张具有代表性的图像子集进行人工标注。该子集包含241,600条专家标注，覆盖从休眠芽到果实成熟的发育阶段。图像使用配备主动闪光照明（active flash illumination）的标准化拖拉机搭载表型分析平台采集。主动闪光照明用于减少光照变异性，并统一不同地点的曝光、阴影和阳光差异。物候结构按照BBCH scale（BBCH尺度）进行标注，边界框根据器官可见性和发育阶段进行调整。除数据集外，我们还提供了深度学习基线实验，以展示检测性能以及不同地点的检测性能。",null,"Plant Methods","2026-09-15T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"多站点苹果物候RGB标注数据集，规模大、标注专业并附深度学习基线，对作物表型与智慧果园研究有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","苹果","表型组学","图像数据集",0,"10.1186\u002Fs13007-026-01591-w",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":64,"direction":68,"ingested_from":70},"W7213246841",[36,39,41,43,46,49,52,54,56,58,60,62],{"name":37,"orcid":38},"Herearii Metuarea","https:\u002F\u002Forcid.org\u002F0009-0008-2716-0617",{"name":40,"orcid":9},"Abdoul-Djalil Ousseini-Hamza",{"name":42,"orcid":9},"Walter Guerra",{"name":44,"orcid":45},"Francesca Zuffa","https:\u002F\u002Forcid.org\u002F0009-0009-8797-0205",{"name":47,"orcid":48},"Francesco Panzeri","https:\u002F\u002Forcid.org\u002F0009-0005-8774-5201",{"name":50,"orcid":51},"Andrea Patocchi","https:\u002F\u002Forcid.org\u002F0000-0002-0919-2702",{"name":53,"orcid":9},"Lidia Lozano",{"name":55,"orcid":9},"Shauny Van Hoye",{"name":57,"orcid":9},"François Laurens",{"name":59,"orcid":9},"Jeremy Labrosse",{"name":61,"orcid":9},"Pejman Rasti",{"name":63,"orcid":9},"David Rousseau",{"tldr":65,"method":66,"finding":67,"direction":68,"opportunity":69},"发布多站点多品种苹果物候RGB图像数据集，并给出深度学习基线检测模型。","采集4个欧洲果园48320张图像，精选808张按BBCH标框标注，用主动闪光和拖","数据集覆盖从休眠芽到果实成熟的241600个标注，基线模型可跨地点检测物候期。","农业遥感与作物表型","可基于该多站点数据研究跨环境域适应与物候期细粒度检测，提升模型泛化能力。","openalex","2026-09-16T23:30:24.293828Z"]