{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T06:30:01Z","timestamp":1787725801186,"version":"build-2784847793"},"reference-count":15,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>Accurate detection of apple flowers and buds at the ground level is a prerequisite for precision orchard management, yet it remains constrained by the scarcity of densely annotated, in-canopy datasets. In this study, we present a dataset for apple flower and bud detection collected in a commercial apple orchard in Shaanxi Province, China. The dataset comprises 3,117 images and 52,181 annotated instances, capturing the structural complexity of tree canopies under variable illumination and partial occlusion. A key characteristic of this dataset is the concurrent presence of fully opened flowers and unopened buds, alongside considerable morphological variability arising from differential petal loss. High-precision bounding boxes distinguish the two classes, with annotations provided in XML, YOLO (TXT), and COCO (JSON) formats to ensure broad compatibility. Standardized train\/validation\/test splits (8:1:1) are supplied, and baseline performance is established using representative architectures, including YOLOv11n, RT-DETR, and DEIMv2. Evaluation results reveal a clear performance gap between buds and flowers, underscoring the inherent detection difficulty of buds due to morphological ambiguity and occlusion. By providing this high-quality dataset, we aim to support the development of robust vision algorithms for early bloom monitoring, facilitating critical management decisions such as site-specific flower thinning and yield estimation.<\/jats:p>","DOI":"10.3389\/fpls.2026.1927541","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T06:03:20Z","timestamp":1787724200000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A high-quality apple flower detection dataset for precision agriculture"],"prefix":"10.3389","volume":"17","author":[{"given":"Dandan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Communication and Information Engineering, Xi\u2019an University of Science and Technology","place":["Xi\u2019an, Shaanxi, China"]},{"name":"Xi\u2019an Key Laboratory of Network Convergence Communication","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi\u2019an Jiaotong University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"15810","DOI":"10.1016\/j.ifacol.2020.12.216","article-title":"Automatic blossom detection in apple trees using deep learning","volume":"53","author":"Bhattarai","year":"2020","journal-title":"Ifac-Papersonline"},{"key":"B2","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.compind.2018.03.010","article-title":"Apple flower detection using deep convolutional networks","volume":"99","author":"Dias","year":"2018","journal-title":"Comput. 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