{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T05:46:23Z","timestamp":1773899183656,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32171899"],"award-info":[{"award-number":["32171899"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An improved YOLOv5 algorithm for the efficient recognition and detection of asparagus with a high accuracy in complex environments was proposed in this study to realize the intelligent machine harvesting of green asparagus. The coordinate attention (CA) mechanism was added to the backbone feature extraction network, which focused more attention on the growth characteristics of asparagus. In the neck part of the algorithm, PANet was replaced with BiFPN, which enhanced the feature propagation and reuse. At the same time, a dataset of asparagus in complex environments under different weather conditions was constructed, and the performance variations of the models with distinct attention mechanisms and feature fusion networks were compared through experiments. Experimental results showed that the mAP@0.5 of the improved YOLOv5 model increased by 4.22% and reached 98.69%, compared with the YOLOv5 prototype network. Thus, the improved YOLOv5 algorithm can effectively detect asparagus and provide technical support for intelligent machine harvesting of asparagus in different weather conditions and complex environments.<\/jats:p>","DOI":"10.3390\/s23031562","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T01:36:59Z","timestamp":1675215419000},"page":"1562","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Detection of Green Asparagus in Complex Environments Based on the Improved YOLOv5 Algorithm"],"prefix":"10.3390","volume":"23","author":[{"given":"Weiwei","family":"Hong","sequence":"first","affiliation":[{"name":"Faculty of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"},{"name":"Special Equipment Institute, Hangzhou Vocational & Technical College, Hangzhou 310018, China"}]},{"given":"Zenghong","family":"Ma","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"},{"name":"Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province, Hangzhou 310018, China"}]},{"given":"Bingliang","family":"Ye","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"},{"name":"Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province, Hangzhou 310018, China"}]},{"given":"Gaohong","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"},{"name":"Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province, Hangzhou 310018, China"}]},{"given":"Tao","family":"Tang","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Mingfeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"},{"name":"Special Equipment Institute, Hangzhou Vocational & Technical College, Hangzhou 310018, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,1]]},"reference":[{"key":"ref_1","first-page":"3","article-title":"Development status and trend of asparagus industry in China","volume":"12","author":"Lu","year":"2018","journal-title":"Shanghai Veg."},{"key":"ref_2","first-page":"123","article-title":"Analysis of the industrial layout optimization of asparagus in China","volume":"36","author":"Peng","year":"2015","journal-title":"Chin. J. Agric. Res. Reg. Plann."},{"key":"ref_3","first-page":"96","article-title":"Analysis of Global Asparagus Production Situation in Recent Fifty Years","volume":"9","author":"Li","year":"2019","journal-title":"Hunan Agric. Sci."},{"key":"ref_4","first-page":"33","article-title":"Development status and prospect of asparagus industry in China","volume":"5","author":"He","year":"2022","journal-title":"Vegatables"},{"key":"ref_5","first-page":"105","article-title":"Accurate Position Detecting during Asparagus Spear Harvesting using a Laser Sensor","volume":"6","author":"Sakai","year":"2013","journal-title":"Eng. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"77","DOI":"10.4028\/www.scientific.net\/AMM.884.77","article-title":"Overview of Sensor Technologies Used for 3D Localization of Asparagus Spears for Robotic Harvesting","volume":"884","author":"Peebles","year":"2018","journal-title":"Appl. Mech. Mater."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Peebles, M., Shen, H.L., Streeter, L., Duke, M., and Chi, K.A. (2018, January 19\u201321). Ground Plane Segmentation of Time-of-Flight Images for Asparagus Harvesting. Proceedings of the 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), Auckland, New Zealand.","DOI":"10.1109\/IVCNZ.2018.8634650"},{"key":"ref_8","first-page":"288","article-title":"A Perception Pipeline for Robotic Harvesting of Green Asparagus","volume":"52","author":"Kennedy","year":"2019","journal-title":"IFAC-Pap."},{"key":"ref_9","first-page":"283","article-title":"Investigation of Optimal Network Architecture for Asparagus Spear Detection in Robotic Harvesting\u2014ScienceDirect","volume":"52","author":"Peebles","year":"2019","journal-title":"IFAC-Pap."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1109\/TMECH.2017.2735861","article-title":"Robotic green asparagus selective harvesting","volume":"22","author":"Leu","year":"2017","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105302","DOI":"10.1016\/j.compag.2020.105302","article-title":"Fruit detection, segmentation and 3D visualisation of environments in apple orchards","volume":"171","author":"Kang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"510","DOI":"10.3389\/fpls.2020.00510","article-title":"Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review","volume":"11","author":"Tang","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_13","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_16","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_17","first-page":"164","article-title":"Apple positioning based on YOLO deep convolutional neural network for picking robot in complex background","volume":"35","author":"Zhao","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106954","DOI":"10.1016\/j.compag.2022.106954","article-title":"A visual identification method for the apple growth forms in the orchard","volume":"197","author":"Lv","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","first-page":"199","article-title":"Citrus Detection Method in Night Environment Based on Improved YOLO v3 Network","volume":"51","author":"Xiong","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_20","first-page":"17","article-title":"Winter Jujube Fruit Recognition Method Based on Improved YOLO v3 under Natural Scene","volume":"52","author":"Liu","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106800","DOI":"10.1016\/j.compag.2022.106800","article-title":"Fast detection of banana bunches and stalks in the natural environment based on deep learning","volume":"194","author":"Fu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106547","DOI":"10.1016\/j.compag.2021.106547","article-title":"Detection and classification of tea buds based on deep learning","volume":"192","author":"Xu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106789","DOI":"10.1016\/j.compag.2022.106789","article-title":"Multi-network fusion algorithm with transfer learning for green cucumber segmentation and recognition under complex natural environment","volume":"194","author":"Bai","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","first-page":"89","article-title":"Research on recognition of maize seedlings and weeds in maize mield based on YOLO v4 convolutional neural network","volume":"52","author":"Quan","year":"2021","journal-title":"J. Northeast Agric.Univ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106641","DOI":"10.1016\/j.compag.2021.106641","article-title":"Real-time detection of kiwifruit flower and bud simultaneously in orchard using YOLOv4 for robotic pollination","volume":"193","author":"Li","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106780","DOI":"10.1016\/j.compag.2022.106780","article-title":"An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease","volume":"194","author":"Qi","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107484","DOI":"10.1016\/j.compag.2022.107484","article-title":"Accurate cotton diseases and pests detection in complex background based on an improved YOLOX model","volume":"203","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107491","DOI":"10.1016\/j.compag.2022.107491","article-title":"CBAM+ASFF-YOLOXs: An improved YOLOXs for guiding agronomic operation based on the identification of key growth stages of lettuce","volume":"203","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107563","DOI":"10.1016\/j.compag.2022.107563","article-title":"Faster and accurate green pepper detection using NSGA-II-based pruned YOLOv5l in the field environment","volume":"205","author":"Nan","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107590","DOI":"10.1016\/j.compag.2022.107590","article-title":"Real-time and accurate detection of citrus in complex scenes based on HPL-YOLOv4","volume":"205","author":"Xu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106715","DOI":"10.1016\/j.compag.2022.106715","article-title":"Real-time defects detection for apple sorting using NIR cameras with pruning-based YOLOV4 network","volume":"193","author":"Fan","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1562\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:20:27Z","timestamp":1760120427000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1562"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,1]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031562"],"URL":"https:\/\/doi.org\/10.3390\/s23031562","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,1]]}}}