{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T04:41:46Z","timestamp":1783917706133,"version":"3.55.0"},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T00:00:00Z","timestamp":1738972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61901221"],"award-info":[{"award-number":["61901221"]}]},{"name":"National Natural Science Foundation of China","award":["SJCX24_0363"],"award-info":[{"award-number":["SJCX24_0363"]}]},{"name":"National Natural Science Foundation of China","award":["SJCX24_0364"],"award-info":[{"award-number":["SJCX24_0364"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["61901221"],"award-info":[{"award-number":["61901221"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["SJCX24_0363"],"award-info":[{"award-number":["SJCX24_0363"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["SJCX24_0364"],"award-info":[{"award-number":["SJCX24_0364"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>To address the issues of low detection accuracy and poor adaptability in complex orchard environments (such as varying lighting conditions, branch and leaf occlusion, fruit overlap, and small targets), this paper proposes an improved pear detection model based on YOLO11, called YOLO11-Pear. First, to improve the model\u2019s capability in detecting occluded pears, the C2PSS module is introduced to replace the original C2PSA module. Second, a small target detection layer is added to improve the model\u2019s ability to detect small pears. Finally, the upsampling process is replaced with DySample, which not only maintains a high efficiency but also improves the processing speed and expands the model\u2019s application range. To validate the effectiveness of the model, a dataset of images of Qiu Yue pears and Cui Guan pears was constructed. The experimental results showed that the improved YOLO11-Pear model achieved precision, recall, mAP50, and mAP50\u201395 values of 96.3%, 84.2%, 92.1%, and 80.2%, respectively, outperforming YOLO11n by 3.6%, 1%, 2.1%, and 3.2%. With only a 2.4% increase in the number of parameters compared to the original model, YOLO11-Pear enables fast and accurate pear detection in complex orchard environments.<\/jats:p>","DOI":"10.3390\/sym17020255","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T06:43:07Z","timestamp":1739169787000},"page":"255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Pear Object Detection in Complex Orchard Environment Based on Improved YOLO11"],"prefix":"10.3390","volume":"17","author":[{"given":"Mingming","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shutong","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7542-1270","authenticated-orcid":false,"given":"Chao","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/j.scienta.2018.11.091","article-title":"Storage of pears","volume":"246","author":"Saquet","year":"2019","journal-title":"Sci. Hortic."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"uhab040","DOI":"10.1093\/hr\/uhab040","article-title":"Pear genetics: Recent advances, new prospects, and a roadmap for the future","volume":"9","author":"Li","year":"2022","journal-title":"Hortic. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ren, R., Sun, H., Zhang, S., Wang, N., Lu, X., Jing, J., Xin, M., and Cui, T. (2023). Intelligent Detection of Lightweight \u201cYuluxiang\u201d Pear in Non-Structural Environment Based on YOLO-GEW. Agronomy, 13.","DOI":"10.3390\/agronomy13092418"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1007\/s11119-023-10009-9","article-title":"Optimization strategies of fruit detection to overcome the challenge of unstructured background in field orchard environment: A review","volume":"24","author":"Tang","year":"2023","journal-title":"Precis. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xiao, F., Wang, H., Li, Y., Cao, Y., Lv, X., and Xu, G. (2023). Object Detection and Recognition Techniques Based on Digital Image Processing and Traditional Machine Learning for Fruit and Vegetable Harvesting Robots: An Overview and Review. Agronomy, 13.","DOI":"10.3390\/agronomy13030639"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wei, J., Ni, L., Luo, L., Chen, M., You, M., Sun, Y., and Hu, T. (2024). GFS-YOLO11: A Maturity Detection Model for Multi-Variety Tomato. Agronomy, 14.","DOI":"10.3390\/agronomy14112644"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.biosystemseng.2018.08.011","article-title":"Recognising blueberry fruit of different maturity using histogram oriented gradients and colour features in outdoor scenes","volume":"176","author":"Tan","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.biosystemseng.2018.04.009","article-title":"Immature citrus fruit detection based on local binary pattern feature and hierarchical contour analysis","volume":"171","author":"Lu","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.compag.2011.07.001","article-title":"Green citrus detection using \u201ceigenfruit\u201d, color and circular Gabor texture features under natural outdoor conditions","volume":"78","author":"Kurtulmus","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"uhac003","DOI":"10.1093\/hr\/uhac003","article-title":"Deep-learning-based in-field citrus fruit detection and tracking","volume":"9","author":"Zhang","year":"2022","journal-title":"Hortic. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108984","DOI":"10.1016\/j.compag.2024.108984","article-title":"YOLOv5s-CEDB: A robust and efficiency Camellia oleifera fruit detection algorithm in complex natural scenes","volume":"221","author":"Zhu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13895","DOI":"10.1007\/s00521-021-06029-z","article-title":"A detection algorithm for cherry fruits based on the improved YOLO-v4 model","volume":"35","author":"Gai","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sun, H., Wang, B., and Xue, J. (2023). YOLO-P: An efficient method for pear fast detection in complex orchard picking environment. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.1089454"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, S., Zheng, J., Sun, S., and Zhang, L. (2022). An Improved YOLO Algorithm for Fast and Accurate Underwater Object Detection. Symmetry, 14.","DOI":"10.2139\/ssrn.4079287"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hao, X., Liu, L., Yang, R., Yin, L., Zhang, L., and Li, X. (2023). A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition. Remote Sens., 15.","DOI":"10.3390\/rs15030827"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2019.04.017","article-title":"Deep learning\u2014Method overview and review of use for fruit detection and yield estimation","volume":"162","author":"Koirala","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Liu, Y., Gong, C., Chen, Y., and Yu, H. (2020). Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review. Sensors, 20.","DOI":"10.3390\/s20051520"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, H., Xia, H., Fan, C., Lan, T., Liu, Y., Yang, Y., Shen, Y., and Yu, W. (2024). Intelligent Detection Method for Surface Defects of Particleboard Based on Super-Resolution Reconstruction. Forests, 15.","DOI":"10.3390\/f15122196"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"35479","DOI":"10.1109\/ACCESS.2023.3266093","article-title":"Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review","volume":"11","author":"Amjoud","year":"2023","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103910","DOI":"10.1016\/j.imavis.2020.103910","article-title":"Recent advances in small object detection based on deep learning: A review","volume":"97","author":"Tong","year":"2020","journal-title":"Image Vis. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, A., Yi, J., Song, Y., and Chehri, A. (2023). Small Object Detection Based on Deep Learning for Remote Sensing: A Comprehensive Review. Remote Sens., 15.","DOI":"10.3390\/rs15133265"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ni, J., Zhu, S., Tang, G., Ke, C., and Wang, T. (2024). A Small-Object Detection Model Based on Improved YOLOv8s for UAV Image Scenarios. Remote Sens., 16.","DOI":"10.3390\/rs16132465"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, W., Lu, H., Fu, H., and Cao, Z. (2023, January 2\u20136). Learning to upsample by learning to sample. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00554"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, Z., Yun, B., and Zheng, Y. (2024). LD-YOLO: A Lightweight Dynamic Forest Fire and Smoke Detection Model with Dysample and Spatial Context Awareness Module. Forests, 15.","DOI":"10.3390\/f15091630"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109090","DOI":"10.1016\/j.compag.2024.109090","article-title":"Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review","volume":"223","author":"Badgujar","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6961","DOI":"10.1007\/s11760-024-03366-z","article-title":"Research on the detection method of phenotypic information of Pinus massoniana Lamb. seedling root system","volume":"18","author":"Li","year":"2024","journal-title":"Signal Image Video Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_28","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Adarsh, P., Rathi, P., and Kumar, M. (2020, January 6\u20137). YOLO v3-Tiny: Object Detection and Recognition using one stage improved model. Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS48705.2020.9074315"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Ju, Z., Sun, T., Dong, F., Li, J., Yang, R., Fu, Q., Lian, C., and Shan, P. (2023). TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism. Drones, 7.","DOI":"10.3390\/drones7070446"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Norkobil Saydirasulovich, S., Abdusalomov, A., Jamil, M.K., Nasimov, R., Kozhamzharova, D., and Cho, Y.I. (2023). A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments. Sensors, 23.","DOI":"10.3390\/s23063161"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhou, G., and Jiang, H. (2023). Student Behavior Detection in the Classroom Based on Improved YOLOv8. Sensors, 23.","DOI":"10.3390\/s23208385"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, Y., Rong, Q., and Hu, C. (2024). Ripe Tomato Detection Algorithm Based on Improved YOLOv9. Plants, 13.","DOI":"10.3390\/plants13223253"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huang, Y., Wang, D., Wu, B., and An, D. (2024). NST-YOLO11: ViT Merged Model with Neuron Attention for Arbitrary-Oriented Ship Detection in SAR Images. Remote Sens., 16.","DOI":"10.3390\/rs16244760"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/2\/255\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:29:13Z","timestamp":1760027353000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/2\/255"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,8]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["sym17020255"],"URL":"https:\/\/doi.org\/10.3390\/sym17020255","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,8]]}}}