{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T18:11:02Z","timestamp":1790014262938,"version":"4.0.1"},"reference-count":54,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2024MD020"],"award-info":[{"award-number":["ZR2024MD020"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2024MF147"],"award-info":[{"award-number":["ZR2024MF147"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Precise flower grading in modern agriculture is critical for determining market value but remains hindered by inefficient manual labor and the computational constraints of edge devices. To address these challenges, this paper introduces Flora-YOLO, a lightweight and robust detection framework built upon the YOLO11n architecture. To overcome the inherent bottlenecks of agricultural vision, the framework incorporates four targeted optimizations, explicitly distinguishing novel components from adapted architectures: (1) an adapted C3k2-based Sandglass-Gated Bottleneck (C3k2_SGB) module to extract and amplify minute floral textures; (2) an integrated Adaptive Downsampling (ADown) module to preserve critical high-frequency spatial cues under severe foliage occlusion; (3) a newly proposed CSP-based Variance-Gated Attention (C2VGA) hybrid attention mechanism to amplify the weak semantic signals of early-stage buds while suppressing background noise; and (4) a novel Focused Shape-Intersection over Union (FSIoU) loss function to provide shape-aware gradient guidance for the precise bounding-box regression of irregular, non-rigid floral boundaries. Evaluated via 5-fold cross-validation on a custom dataset, the model achieves a stable 86.7% mAP@0.5 and 50.4% mAP@0.5:0.95. This represents a 3.4% accuracy improvement over the baseline, achieved alongside a 28.6% reduction in computational load, requiring only 1.73 M parameters and 4.5 GFLOPs. Furthermore, out-of-domain testing and evaluation on public agricultural datasets confirm the model\u2019s cross-species generalization. Finally, hardware deployment on both Android terminals and an NPU-accelerated NXP i.MX 8M Plus platform yields a real-time inference speed of 11.05 FPS with a 90.5 ms latency. These results establish Flora-YOLO as a highly efficient, accurate, and scalable solution for automated, edge-based sorting in precision floriculture.<\/jats:p>","DOI":"10.1371\/journal.pone.0358742","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T17:34:29Z","timestamp":1790012069000},"page":"e0358742","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Flora-YOLO: A lightweight framework for real-time rose grading on edge devices via structural optimization and feature refinement"],"prefix":"10.1371","volume":"21","author":[{"given":"Wenwei","family":"Liu","sequence":"first","affiliation":[{"name":"School of Airspace Science and Engineering, Shandong University","place":["Weihai, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Airspace Science and 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Analyzing and boosting the power of fine-grained visual recognition for multi-modal large language models. arXiv. 2025. https:\/\/arxiv.org\/abs\/2501.15140"},{"issue":"3","key":"pone.0358742.ref012","first-page":"5308","article-title":"An optimized deep learning model for flower classification using NAS-FPN and Faster R-CNN","volume":"9","author":"I Patel","year":"2020","journal-title":"Int J Sci Technol Res"},{"issue":"10","key":"pone.0358742.ref013","doi-asserted-by":"crossref","first-page":"2596","DOI":"10.3390\/agronomy13102596","article-title":"Improved Faster Region-Based Convolutional Neural Networks (R-CNN) Model Based on Split Attention for the Detection of Safflower Filaments in Natural Environments","volume":"13","author":"Z Zhang","year":"2023","journal-title":"Agronomy"},{"issue":"2","key":"pone.0358742.ref014","doi-asserted-by":"crossref","first-page":"421","DOI":"10.3390\/agronomy15020421","article-title":"Classification of Garden Chrysanthemum Flowering Period Using Digital Imagery from Unmanned Aerial Vehicle (UAV)","volume":"15","author":"J Zhang","year":"2025","journal-title":"Agronomy"},{"issue":"6","key":"pone.0358742.ref015","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"S Ren","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"pone.0358742.ref016","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017. 2961\u20139. https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"issue":"17","key":"pone.0358742.ref017","doi-asserted-by":"crossref","first-page":"7602","DOI":"10.3390\/su17177602","article-title":"Application of Deep Learning Technology in Monitoring Plant Attribute Changes","volume":"17","author":"S Han","year":"2025","journal-title":"Sustainability"},{"key":"pone.0358742.ref018","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in Vision: A Survey","volume":"54","author":"S Khan","year":"2022","journal-title":"ACM Comput Surv"},{"issue":"6","key":"pone.0358742.ref019","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/LMWT.2026.3660663","article-title":"Low-Bitwidth Generalized Feed-Forward Neural Network Digital Predistortion Based on Joint Optimization of Pruning and Quantization","volume":"36","author":"Z Chen","year":"2026","journal-title":"IEEE Microw Wireless Tech Lett"},{"issue":"1","key":"pone.0358742.ref020","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/TAFE.2025.3529724","article-title":"AMA-Net: Adaptive Masking Attention Network for Agricultural Crop Classification From UAV Images","volume":"3","author":"X Wang","year":"2025","journal-title":"IEEE Trans Agri Elect"},{"key":"pone.0358742.ref021","doi-asserted-by":"crossref","first-page":"4192","DOI":"10.1109\/TIP.2026.3684428","article-title":"EDVD: Cross-Modal Spatio-Temporal Fusion With Event and Diffusion for Video Deblurring","volume":"35","author":"Y Fu","year":"2026","journal-title":"IEEE Transactions on Image Processing"},{"issue":"6","key":"pone.0358742.ref022","doi-asserted-by":"crossref","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","article-title":"Object detection using YOLO: challenges, architectural successors, datasets and applications","volume":"82","author":"T Diwan","year":"2023","journal-title":"Multimed Tools Appl"},{"key":"pone.0358742.ref023","doi-asserted-by":"crossref","first-page":"64358","DOI":"10.1109\/ACCESS.2023.3286545","article-title":"Cucumber Flower Detection Based on YOLOv5s-SE7 Within Greenhouse Environments","volume":"11","author":"X Xu","year":"2023","journal-title":"IEEE Access"},{"issue":"9","key":"pone.0358742.ref024","doi-asserted-by":"crossref","first-page":"4234","DOI":"10.3390\/s23094234","article-title":"Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm","volume":"23","author":"W Zhao","year":"2023","journal-title":"Sensors (Basel)"},{"key":"pone.0358742.ref025","doi-asserted-by":"crossref","first-page":"1607582","DOI":"10.3389\/fpls.2025.1607582","article-title":"Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring","volume":"16","author":"S You","year":"2025","journal-title":"Front Plant Sci"},{"key":"pone.0358742.ref026","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV. EfficientDet: Scalable and Efficient Object Detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 10778\u201387. https:\/\/doi.org\/10.1109\/cvpr42600.2020.01079","DOI":"10.1109\/CVPR42600.2020.01079"},{"issue":"3","key":"pone.0358742.ref027","first-page":"468","article-title":"VM-YOLO: YOLO with VMamba for Strawberry Flowers Detection","volume":"14","author":"Y Wang","year":"2025","journal-title":"Plants (Basel)"},{"issue":"6","key":"pone.0358742.ref028","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.3390\/agronomy14061285","article-title":"Light-FC-YOLO: A Lightweight Method for Flower Counting Based on Enhanced Feature Fusion with a New Efficient Detection Head","volume":"14","author":"X Yi","year":"2024","journal-title":"Agronomy"},{"key":"pone.0358742.ref029","first-page":"81","article-title":"Lightweight and Robust YOLOv5s with MobileNetV3 and GhostNet for Precision Apple Flower Detection for Pollination Drones","volume-title":"Smart Agriculture","author":"SH Manzoor","year":"2024"},{"key":"pone.0358742.ref030","doi-asserted-by":"crossref","first-page":"600854","DOI":"10.3389\/fpls.2020.600854","article-title":"Fine-Grained Image Classification for Crop Disease Based on Attention Mechanism","volume":"11","author":"G Yang","year":"2020","journal-title":"Front Plant Sci"},{"issue":"3","key":"pone.0358742.ref031","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.3934\/mbe.2021103","article-title":"Visual attentional-driven deep learning method for flower recognition","volume":"18","author":"S Cao","year":"2021","journal-title":"Math Biosci Eng"},{"key":"pone.0358742.ref032","article-title":"Improving apple object detection with occlusion-enhanced distillation","author":"Y Shi","year":"2024","journal-title":"SSRN Electron J"},{"issue":"2","key":"pone.0358742.ref033","doi-asserted-by":"crossref","first-page":"8914","DOI":"10.1016\/j.ifacol.2023.10.096","article-title":"Machine Vision System for Early-stage Apple Flowers and Flower Clusters Detection for Precision Thinning and Pollination","volume":"56","author":"SR Khanal","year":"2023","journal-title":"IFAC-PapersOnLine"},{"issue":"1","key":"pone.0358742.ref034","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1109\/TCSVT.2023.3286896","article-title":"High-Resolution Feature Pyramid Network for Small Object Detection on Drone View","volume":"34","author":"Z Chen","year":"2024","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"1","key":"pone.0358742.ref035","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/s11119-025-10246-0","article-title":"Assessing the capability of YOLO- and transformer-based object detectors for real-time weed detection","volume":"26","author":"A Allmendinger","year":"2025","journal-title":"Precis Agric"},{"key":"pone.0358742.ref036","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2025.3606474","article-title":"CTPEM: A Cross-Temporal Progressive Enhancement Model Tackling Object-Level Building Damage Detection and Vanishing Small Features","volume":"63","author":"Z Guo","year":"2025","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"pone.0358742.ref037","doi-asserted-by":"crossref","unstructured":"Wang CY, Yeh IH, Liao HYM. YOLOv9: Learning what you want to learn using programmable gradient information. In: Proceedings of the European Conference on Computer Vision (ECCV); 2024 Sep 29\u2013Oct 4; Milan, Italy. p. 1\u201321. doi: 10.1007\/978-3-031-72751-1_1","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"pone.0358742.ref038","doi-asserted-by":"crossref","unstructured":"Liu H, Jia C, Shi F, Cheng X, Chen S. SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures. In: 2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 29406\u201316. https:\/\/doi.org\/10.1109\/cvpr52734.2025.02738","DOI":"10.1109\/CVPR52734.2025.02738"},{"issue":"4","key":"pone.0358742.ref039","doi-asserted-by":"crossref","first-page":"5355","DOI":"10.1109\/TNNLS.2022.3204090","article-title":"SwinPA-Net: Swin Transformer-Based Multiscale Feature Pyramid Aggregation Network for Medical Image Segmentation","volume":"35","author":"H Du","year":"2024","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"pone.0358742.ref040","unstructured":"Zhang H, Zhang S. Shape-IoU: More accurate metric considering bounding box shape and scale. arXiv. 2023. https:\/\/arxiv.org\/abs\/2312.17663"},{"key":"pone.0358742.ref041","unstructured":"Tong Z, Chen Y, Xu Z, Yu R. Wise-IoU: Bounding box regression loss with dynamic focusing mechanism. arXiv. 2023. https:\/\/arxiv.org\/abs\/2301.10051"},{"key":"pone.0358742.ref042","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon IS. CBAM: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 2018. 3\u201319. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"pone.0358742.ref043","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 11531\u20139. https:\/\/doi.org\/10.1109\/cvpr42600.2020.01155","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"pone.0358742.ref044","unstructured":"Yang L, Zhang RY, Li L, Xie X. SimAM: A simple, parameter-free attention module for convolutional neural networks. In: Proceedings of the 38th International Conference on Machine Learning (ICML), 2021. 11863\u201374."},{"key":"pone.0358742.ref045","doi-asserted-by":"crossref","unstructured":"Ouyang D, He S, Zhang G, Luo M, Guo H, Zhan J, et al. Efficient Multi-Scale Attention Module with Cross-Spatial Learning. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. 1\u20135. https:\/\/doi.org\/10.1109\/icassp49357.2023.10096516","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"pone.0358742.ref046","doi-asserted-by":"crossref","unstructured":"Zhu L, Wang X, Ke Z, Zhang W, Lau R. BiFormer: Vision Transformer with Bi-Level Routing Attention. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 10323\u201333. https:\/\/doi.org\/10.1109\/cvpr52729.2023.00995","DOI":"10.1109\/CVPR52729.2023.00995"},{"key":"pone.0358742.ref047","doi-asserted-by":"crossref","first-page":"108784","DOI":"10.1016\/j.compbiomed.2024.108784","article-title":"Channel prior convolutional attention for medical image segmentation","volume":"178","author":"H Huang","year":"2024","journal-title":"Comput Biol Med"},{"key":"pone.0358742.ref048","unstructured":"Ma S, Xu Y. MPDIoU: A loss for efficient and accurate bounding box regression. arXiv. 2023. https:\/\/arxiv.org\/abs\/2307.07662"},{"key":"pone.0358742.ref049","doi-asserted-by":"crossref","unstructured":"Kumar S, Kumar A, Kumar K. GhostNet-YOLO Algorithm for Object Detection in UAV Image. In: 2023 Seventh International Conference on Image Information Processing (ICIIP), 2023. 293\u20139. https:\/\/doi.org\/10.1109\/iciip61524.2023.10537737","DOI":"10.1109\/ICIIP61524.2023.10537737"},{"issue":"4","key":"pone.0358742.ref050","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.1109\/TPAMI.2024.3524377","article-title":"Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation","volume":"47","author":"Y Feng","year":"2025","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"pone.0358742.ref051","doi-asserted-by":"crossref","unstructured":"Zhang J, White TS, Zhang H, Hu W, Wunsch DC, Liu J. MS-YOLO: Infrared Object Detection for Edge Deployment via MobileNetV4 and SlideLoss. 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