{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T09:07:54Z","timestamp":1786093674224,"version":"3.56.0"},"reference-count":30,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Forestry Science and Technology Project","award":["2023SY08"],"award-info":[{"award-number":["2023SY08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate recognition of the flowering stage is a prerequisite for flower yield estimation. In order to improve the recognition accuracy based on the complex image background, such as flowers partially covered by leaves and flowers with insignificant differences in various fluorescence, this paper proposed an improved CR-YOLOv5s to recognize flower buds and blooms for chrysanthemums by emphasizing feature representation through an attention mechanism. The coordinate attention mechanism module has been introduced to the backbone of the YOLOv5s so that the network can pay more attention to chrysanthemum flowers, thereby improving detection accuracy and robustness. Specifically, we replaced the convolution blocks in the backbone network of YOLOv5s with the convolution blocks from the RepVGG block structure to improve the feature representation ability of YOLOv5s through a multi-branch structure, further improving the accuracy and robustness of detection. The results showed that the average accuracy of the improved CR-YOLOv5s was as high as 93.9%, which is 4.5% better than that of normal YOLOv5s. This research provides the basis for the automatic picking and grading of flowers, as well as a decision-making basis for estimating flower yield.<\/jats:p>","DOI":"10.3390\/s23094234","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T06:20:42Z","timestamp":1682317242000},"page":"4234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm"],"prefix":"10.3390","volume":"23","author":[{"given":"Wentao","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China"},{"name":"Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dasheng","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China"},{"name":"Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China"},{"name":"Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106150","DOI":"10.1016\/j.compag.2021.106150","article-title":"Apple, Peach, and Pear Flower Detection Using Semantic Segmentation Network and Shape Constraint Level Set","volume":"185","author":"Sun","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","first-page":"1","article-title":"Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device","volume":"2020","author":"Cheng","year":"2020","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","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. Ind."},{"key":"ref_4","first-page":"104","article-title":"Detecting Tomato Flowers in Greenhouses Using Computer Vision","volume":"11","author":"Oppenheim","year":"2017","journal-title":"Int. J. Comput. Inf. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.measurement.2019.01.041","article-title":"Efficient Deep Features Selections and Classification for Flower Species Recognition","volume":"137","author":"Budak","year":"2019","journal-title":"Measurement"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"032045","DOI":"10.1088\/1742-6596\/1237\/3\/032045","article-title":"Detection and Recognition of Flower Image Based on SSD Network in Video Stream","volume":"1237","author":"Tian","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Feng, J., Wang, Z., Zha, M., and Cao, X. (2019, January 20\u201322). Flower Recognition Based on Transfer Learning and Adam Deep Learning Optimization Algorithm. Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence\u2014RICAI 2019, Shanghai, China.","DOI":"10.1145\/3366194.3366301"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1038\/s41598-021-81216-5","article-title":"Tomato Detection Based on Modified YOLOv3 Framework","volume":"11","author":"Lawal","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105742","DOI":"10.1016\/j.compag.2020.105742","article-title":"Using Channel Pruning-Based YOLO v4 Deep Learning Algorithm for the Real-Time and Accurate Detection of Apple Flowers in Natural Environments","volume":"178","author":"Wu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s11119-019-09679-1","article-title":"Detection and Counting of Flowers on Apple Trees for Better Chemical Thinning Decisions","volume":"21","author":"Farjon","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, Y., Rao, Y., Jin, X., Jiang, Z., Wang, Y., Wang, T., Wang, F., Luo, Q., and Liu, L. (2023). YOLOv5s-FP: A Novel Method for In-Field Pear Detection Using a Transformer Encoder and Multi-Scale Collaboration Perception. Sensors, 23.","DOI":"10.3390\/s23010030"},{"key":"ref_12","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv."},{"key":"ref_13","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2022). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. arXiv."},{"key":"ref_14","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2023, March 20). YOLO by Ultralytics. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_15","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wang, Y., Yang, T., Zhang, X., Cheng, J., and Sun, J. (2021, January 19\u201325). You Only Look One-Level Feature. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01284"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106092","DOI":"10.1016\/j.compag.2021.106092","article-title":"Estimation of Corn Yield Based on Hyperspectral Imagery and Convolutional Neural Network","volume":"184","author":"Yang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yan, Q., Yang, B., Wang, W., Wang, B., Chen, P., and Zhang, J. (2020). Apple Leaf Diseases Recognition Based on An Improved Convolutional Neural Network. Sensors, 20.","DOI":"10.3390\/s20123535"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, G., Chen, L., Zhao, S., and Liu, Y. (2022, January 8\u201310). Efficient Aircraft Object Detection Based on Faster R-CNN in Remote Sensing Images. Proceedings of the Second International Conference on Digital Signal and Computer Communications, Changchun, China.","DOI":"10.1117\/12.2641804"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lu, J., Lin, W., Chen, P., Lan, Y., Deng, X., Niu, H., Mo, J., Li, J., and Luo, S. (2021). Research on Lightweight Citrus Flowering Rate Statistical Model Combined with Anchor Frame Clustering Optimization. Sensors, 21.","DOI":"10.3390\/s21237929"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lee, U., Islam, M.P., Kochi, N., Tokuda, K., Nakano, Y., Naito, H., Kawasaki, Y., Ota, T., Sugiyama, T., and Ahn, D.-H. (2022). An Automated, Clip-Type, Small Internet of Things Camera-Based Tomato Flower and Fruit Monitoring and Harvest Prediction System. Sensors, 22.","DOI":"10.3390\/s22072456"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lyu, S., Zhao, Y., Li, R., Li, Z., Fan, R., and Li, Q. (2022). Embedded Sensing System for Recognizing Citrus Flowers Using Cascaded Fusion YOLOv4-CF + FPGA. Sensors, 22.","DOI":"10.3390\/s22031255"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). Proceedings of the Computer Vision\u2014ECCV 2018, Springer International Publishing.","DOI":"10.1007\/978-3-030-01270-0"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 19\u201325). RepVGG: Making VGG-Style ConvNets Great Again. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"ref_27","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":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Proceedings of the Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46478-7"},{"key":"ref_29","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_30","unstructured":"Jocher, G., Stoken, A., Borovec, J., Changyu, L., and Hogan, A. (2022, March 20). Ultralytics\/Yolov5: V3.1\u2014Bug Fixes and Performance Improvements. Available online: https:\/\/github.com\/ultralytics\/yolov5."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4234\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:22:17Z","timestamp":1760124137000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,24]]},"references-count":30,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094234"],"URL":"https:\/\/doi.org\/10.3390\/s23094234","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,24]]}}}