{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T04:41:50Z","timestamp":1775191310811,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi","award":["2021L141"],"award-info":[{"award-number":["2021L141"]}]},{"name":"Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi","award":["20210302124374"],"award-info":[{"award-number":["20210302124374"]}]},{"name":"Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi","award":["CARS-06-14.5-A28"],"award-info":[{"award-number":["CARS-06-14.5-A28"]}]},{"name":"Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi","award":["2020CG026"],"award-info":[{"award-number":["2020CG026"]}]},{"name":"Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi","award":["2020-068"],"award-info":[{"award-number":["2020-068"]}]},{"name":"Fundamental Research Program of Shanxi Province","award":["2021L141"],"award-info":[{"award-number":["2021L141"]}]},{"name":"Fundamental Research Program of Shanxi Province","award":["20210302124374"],"award-info":[{"award-number":["20210302124374"]}]},{"name":"Fundamental Research Program of Shanxi Province","award":["CARS-06-14.5-A28"],"award-info":[{"award-number":["CARS-06-14.5-A28"]}]},{"name":"Fundamental Research Program of Shanxi Province","award":["2020CG026"],"award-info":[{"award-number":["2020CG026"]}]},{"name":"Fundamental Research Program of Shanxi Province","award":["2020-068"],"award-info":[{"award-number":["2020-068"]}]},{"name":"China Agriculture Research System","award":["2021L141"],"award-info":[{"award-number":["2021L141"]}]},{"name":"China Agriculture Research System","award":["20210302124374"],"award-info":[{"award-number":["20210302124374"]}]},{"name":"China Agriculture Research System","award":["CARS-06-14.5-A28"],"award-info":[{"award-number":["CARS-06-14.5-A28"]}]},{"name":"China Agriculture Research System","award":["2020CG026"],"award-info":[{"award-number":["2020CG026"]}]},{"name":"China Agriculture Research System","award":["2020-068"],"award-info":[{"award-number":["2020-068"]}]},{"name":"Science and Technology Achievements Transformation and Cultivation Project of Colleges and Universities in Shanxi Province","award":["2021L141"],"award-info":[{"award-number":["2021L141"]}]},{"name":"Science and Technology Achievements Transformation and Cultivation Project of Colleges and Universities in Shanxi Province","award":["20210302124374"],"award-info":[{"award-number":["20210302124374"]}]},{"name":"Science and Technology Achievements Transformation and Cultivation Project of Colleges and Universities in Shanxi Province","award":["CARS-06-14.5-A28"],"award-info":[{"award-number":["CARS-06-14.5-A28"]}]},{"name":"Science and Technology Achievements Transformation and Cultivation Project of Colleges and Universities in Shanxi Province","award":["2020CG026"],"award-info":[{"award-number":["2020CG026"]}]},{"name":"Science and Technology Achievements Transformation and Cultivation Project of Colleges and Universities in Shanxi Province","award":["2020-068"],"award-info":[{"award-number":["2020-068"]}]},{"name":"Research Project Supported by Shanxi Scholarship Council of China","award":["2021L141"],"award-info":[{"award-number":["2021L141"]}]},{"name":"Research Project Supported by Shanxi Scholarship Council of China","award":["20210302124374"],"award-info":[{"award-number":["20210302124374"]}]},{"name":"Research Project Supported by Shanxi Scholarship Council of China","award":["CARS-06-14.5-A28"],"award-info":[{"award-number":["CARS-06-14.5-A28"]}]},{"name":"Research Project Supported by Shanxi Scholarship Council of China","award":["2020CG026"],"award-info":[{"award-number":["2020CG026"]}]},{"name":"Research Project Supported by Shanxi Scholarship Council of China","award":["2020-068"],"award-info":[{"award-number":["2020-068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the foxtail millet field, due to the dense distribution of the foxtail millet ears, morphological differences among foxtail millet ears, severe shading of stems and leaves, and complex background, it is difficult to identify the foxtail millet ears. To solve these practical problems, this study proposes a lightweight foxtail millet ear detection method based on improved YOLOv5. The improved model proposes to use the GhostNet module to optimize the model structure of the original YOLOv5, which can reduce the model parameters and the amount of calculation. This study adopts an approach that incorporates the Coordinate Attention (CA) mechanism into the model structure and adjusts the loss function to the Efficient Intersection over Union (EIOU) loss function. Experimental results show that these methods can effectively improve the detection effect of occlusion and small-sized foxtail millet ears. The recall, precision, F1 score, and mean Average Precision (mAP) of the improved model were 97.70%, 93.80%, 95.81%, and 96.60%, respectively, the average detection time per image was 0.0181 s, and the model size was 8.12 MB. Comparing the improved model in this study with three lightweight object detection algorithms: YOLOv3_tiny, YOLOv5-Mobilenetv3small, and YOLOv5-Shufflenetv2, the improved model in this study shows better detection performance. It provides technical support to achieve rapid and accurate identification of multiple foxtail millet ear targets in complex environments in the field, which is important for improving foxtail millet ear yield and thus achieving intelligent detection of foxtail millet.<\/jats:p>","DOI":"10.3390\/s22218206","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T07:17:48Z","timestamp":1666768668000},"page":"8206","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Foxtail Millet Ear Detection Method Based on Attention Mechanism and Improved YOLOv5"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6892-9268","authenticated-orcid":false,"given":"Shujin","family":"Qiu","sequence":"first","affiliation":[{"name":"College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9310-325X","authenticated-orcid":false,"given":"Huamin","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyang","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Agricultural, Shanxi Agricultural University, Jinzhong 030801, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"ref_1","first-page":"459","article-title":"Current status and future prospective of foxtail millet production and seed industry in China","volume":"54","author":"Li","year":"2021","journal-title":"Sci. Agric. Sin."},{"key":"ref_2","first-page":"3097","article-title":"New vision and policy recommendations for nutrition-oriented food security in China","volume":"52","author":"Chen","year":"2019","journal-title":"Sci. Agric. Sin."},{"key":"ref_3","first-page":"1","article-title":"Research progress of image sensing and deep learning in agriculture","volume":"51","author":"Sun","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_4","first-page":"105","article-title":"Application and research progress of deep learning in agriculture","volume":"25","author":"Fu","year":"2020","journal-title":"J. China Agric. Univ."},{"key":"ref_5","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_6","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_7","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_8","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_9","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016). Ssd: Single shot multibox detector. Computer Vision\u2014ECCV 2016, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_12","first-page":"200","article-title":"Recognition and counting of citrus flowers based on instance segmentation","volume":"36","author":"Deng","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_13","first-page":"237","article-title":"Improved YOLOv5\u2019s method for detecting the growth status of apple flowers","volume":"58","author":"Yang","year":"2022","journal-title":"Comput. Eng. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106220","DOI":"10.1016\/j.compag.2021.106220","article-title":"Grape stem detection using regression convolutional neural networks","volume":"186","author":"Kalampokas","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","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_16","first-page":"217","article-title":"Tea Bud Detection Based on Faster R-CNN Network","volume":"5","author":"Zhu","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_17","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_18","first-page":"201","article-title":"Fast recognition of tomato fruit in greenhouse at night based on improved YOLO v5","volume":"53","author":"He","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ren, R., Zhang, S., Sun, H., and Gao, T. (2021). Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network. Sensors, 21.","DOI":"10.3390\/s21165305"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106864","DOI":"10.1016\/j.compag.2022.106864","article-title":"Fusion of Mask RCNN and attention mechanism for instance segmentation of apples under complex background","volume":"196","author":"Wang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yan, B., Fan, P., Lei, X., Liu, Z., and Yang, F. (2021). A real-time apple targets detection method for picking robot based on improved YOLOv5. Remote Sens., 13.","DOI":"10.3390\/rs13091619"},{"key":"ref_22","first-page":"170","article-title":"Citrus fruit recognition method based on the improved model of YOLOv5","volume":"41","author":"Huang","year":"2022","journal-title":"J. Huazhong Agric. Univ."},{"key":"ref_23","first-page":"253","article-title":"Wheat Spikes Detection Based on Pyramidal Network of Channel Space Attention Mechanism","volume":"52","author":"Zhang","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_24","first-page":"231","article-title":"Rice panicle detection method based on improved faster R-CNN","volume":"52","author":"Zhang","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_25","first-page":"227","article-title":"Deep learning-based extraction of rice phenotypic characteristics and prediction of rice panicle weight","volume":"40","author":"Yang","year":"2021","journal-title":"J. Huazhong Agric. Univ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhang, X., Yan, J., Qiu, X., Yao, X., Tian, Y., Zhu, Y., and Cao, W. (2021). A wheat spike detection method in UAV images based on improved YOLOv5. Remote Sens., 13.","DOI":"10.3390\/rs13163095"},{"key":"ref_27","first-page":"63","article-title":"Foxtail Millet ear detection approach based on YOLOv4 and adaptive anchor box adjustment","volume":"3","author":"Hao","year":"2021","journal-title":"Smart Agric."},{"key":"ref_28","first-page":"170","article-title":"Potato detection in complex environment based on improved YoloV4 model","volume":"37","author":"Zhang","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_29","first-page":"293","article-title":"Establishment and Experimental Verification of Deep Learning Model for On-line recognition of Field Cabbage","volume":"53","author":"Zhai","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_30","first-page":"156","article-title":"Combining lightweight wheat spikes detecting model and offline Android software development for in-field wheat yield prediction","volume":"37","author":"Chen","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_31","first-page":"17","article-title":"Apple detection method based on light-YOLOv3 convolutional neural network","volume":"51","author":"Wu","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_32","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_33","first-page":"265","article-title":"Fast Recognition Method for Multiple Apple Targets in Dense Scenes Based on CenterNet","volume":"53","author":"Yang","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"106503","DOI":"10.1016\/j.compag.2021.106503","article-title":"Fast and accurate green pepper detection in complex backgrounds via an improved Yolov4-tiny model","volume":"191","author":"Li","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","first-page":"170","article-title":"Blueberry maturity recognition method based on improved YOLOv4-Tiny","volume":"37","author":"Wang","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_37","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 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 14\u201319). Ghostnet: More features from cheap operations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Zhan, Y., Xu, Y., Zhang, C., Xu, Z., and Guo, B. (2022, January 25\u201327). An Irregularly Dropped Garbage Detection Method Based on Improved YOLOv5s. Proceedings of the 4th International Symposium on Signal Processing Systems, New York, NY, USA.","DOI":"10.1145\/3532342.3532344"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","article-title":"Focal and efficient IOU loss for accurate bounding box regression","volume":"506","author":"Zhang","year":"2022","journal-title":"Neurocomputing"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8206\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:03:14Z","timestamp":1760144594000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8206"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,26]]},"references-count":41,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218206"],"URL":"https:\/\/doi.org\/10.3390\/s22218206","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,26]]}}}