{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T14:36:10Z","timestamp":1782398170346,"version":"3.54.5"},"reference-count":32,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,12]],"date-time":"2023-11-12T00:00:00Z","timestamp":1699747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Key R&amp;D Project of Anhui","award":["202104g01020013"],"award-info":[{"award-number":["202104g01020013"]}]},{"name":"the Key R&amp;D Project of Anhui","award":["202204c06020018"],"award-info":[{"award-number":["202204c06020018"]}]},{"name":"the Key R&amp;D Project of Anhui","award":["X202310364120"],"award-info":[{"award-number":["X202310364120"]}]},{"name":"Entrepreneurship Training Project for the University Students","award":["202104g01020013"],"award-info":[{"award-number":["202104g01020013"]}]},{"name":"Entrepreneurship Training Project for the University Students","award":["202204c06020018"],"award-info":[{"award-number":["202204c06020018"]}]},{"name":"Entrepreneurship Training Project for the University Students","award":["X202310364120"],"award-info":[{"award-number":["X202310364120"]}]},{"name":"Anhui Province New Energy Vehicle and Intelligent Connected Automobile Industry Technology Innovation Project","award":["202104g01020013"],"award-info":[{"award-number":["202104g01020013"]}]},{"name":"Anhui Province New Energy Vehicle and Intelligent Connected Automobile Industry Technology Innovation Project","award":["202204c06020018"],"award-info":[{"award-number":["202204c06020018"]}]},{"name":"Anhui Province New Energy Vehicle and Intelligent Connected Automobile Industry Technology Innovation Project","award":["X202310364120"],"award-info":[{"award-number":["X202310364120"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Thousand-grain weight is the main parameter for accurately estimating rice yields, and it is an important indicator for variety breeding and cultivation management. The accurate detection and counting of rice grains is an important prerequisite for thousand-grain weight measurements. However, because rice grains are small targets with high overall similarity and different degrees of adhesion, there are still considerable challenges preventing the accurate detection and counting of rice grains during thousand-grain weight measurements. A deep learning model based on a transformer encoder and coordinate attention module was, therefore, designed for detecting and counting rice grains, and named TCLE-YOLO in which YOLOv5 was used as the backbone network. Specifically, to improve the feature representation of the model for small target regions, a coordinate attention (CA) module was introduced into the backbone module of YOLOv5. In addition, another detection head for small targets was designed based on a low-level, high-resolution feature map, and the transformer encoder was applied to the neck module to expand the receptive field of the network and enhance the extraction of key feature of detected targets. This enabled our additional detection head to be more sensitive to rice grains, especially heavily adhesive grains. Finally, EIoU loss was used to further improve accuracy. The experimental results show that, when applied to the self-built rice grain dataset, the precision, recall, and mAP@0.5 of the TCLE\u2013YOLO model were 99.20%, 99.10%, and 99.20%, respectively. Compared with several state-of-the-art models, the proposed TCLE\u2013YOLO model achieves better detection performance. In summary, the rice grain detection method built in this study is suitable for rice grain recognition and counting, and it can provide guidance for accurate thousand-grain weight measurements and the effective evaluation of rice breeding.<\/jats:p>","DOI":"10.3390\/s23229129","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T02:46:47Z","timestamp":1699843607000},"page":"9129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Rice Grain Detection and Counting Method Based on TCLE\u2013YOLO Model"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4109-6702","authenticated-orcid":false,"given":"Yu","family":"Zou","sequence":"first","affiliation":[{"name":"Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zefeng","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Engineering, Anhui Agricultural University, Hefei 230036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawen","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Engineering, Anhui Agricultural University, Hefei 230036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Agriculture, Anhui Science and Technology University, Chuzhou 239000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hefei Institute of Technology Innovation Engineering, Chinese Academy of Sciences, Hefei 230094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8137-671X","authenticated-orcid":false,"given":"Lu","family":"Liu","sequence":"additional","affiliation":[{"name":"Hefei Institute of Technology Innovation Engineering, Chinese Academy of Sciences, Hefei 230094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peijiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinlong","family":"Ni","sequence":"additional","affiliation":[{"name":"Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bascon, M., Nakata, T., Shibata, S., Takata, I., Kobayashi, N., Kato, Y., Inoue, S., Doi, K., Murase, J., and Nishiuchi, S. (2022). Estimating yield-related traits using UAV-derived multispectral images to improve rice grain yield prediction. Agriculture, 12.","DOI":"10.3390\/agriculture12081141"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.compag.2017.08.011","article-title":"Rice and wheat grain counting method and software development based on Android system","volume":"141","author":"Liu","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wu, W., Zhou, L., Chen, J., Qiu, Z., and He, Y. (2018). GainTKW: A measurement system of thousand kernel weight based on the android platform. Agronomy, 8.","DOI":"10.3390\/agronomy8090178"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.compag.2019.04.030","article-title":"Segmentation and counting algorithm for touching hybrid rice grains","volume":"162","author":"Tan","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1007\/s11119-022-09899-y","article-title":"Estimating maize seedling number with UAV RGB images and advanced image processing methods","volume":"23","author":"Liu","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-019-0510-0","article-title":"Image analysis-based recognition and quantification of grain number per panicle in rice","volume":"15","author":"Wu","year":"2019","journal-title":"Plant Methods"},{"key":"ref_7","first-page":"233","article-title":"Image based leaf segmentation and counting in rosette plants","volume":"6","author":"Kumar","year":"2019","journal-title":"Inf. Process. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108036","DOI":"10.1016\/j.compag.2023.108036","article-title":"NDMFCS: An automatic fruit counting system in modern apple orchard using abatement of abnormal fruit detection","volume":"211","author":"Wu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","first-page":"82","article-title":"Yield performance estimation of corn hybrids using machine learning algorithms","volume":"5","author":"Sarijaloo","year":"2021","journal-title":"Artif. Intell. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fuentes-Pe\u00f1ailillo, F., Carrasco Silva, G., P\u00e9rez Guzm\u00e1n, R., Burgos, I., and Ewertz, F. (2023). Automating seedling counts in horticulture using computer vision and AI. Horticulturae, 9.","DOI":"10.3390\/horticulturae9101134"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Khaki, S., Pham, H., Han, Y., Kuhl, A., Kent, W., and Wang, L. (2020). Convolutional neural networks for image-based corn kernel detection and counting. Sensors, 20.","DOI":"10.3390\/s20092721"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gong, L., and Fan, S. (2022). A CNN-Based Method for Counting Grains within a Panicle. Machines, 10.","DOI":"10.3390\/machines10010030"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106683","DOI":"10.1016\/j.compag.2021.106683","article-title":"Towards real-time tracking and counting of seedlings with a one-stage detector and optical flow","volume":"193","author":"Tan","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lyu, S., Li, R., Zhao, Y., Li, Z., Fan, R., and Liu, S. (2022). Green citrus detection and counting in orchards based on YOLOv5-CS and AI edge system. Sensors, 22.","DOI":"10.3390\/s22020576"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107741","DOI":"10.1016\/j.compag.2023.107741","article-title":"Tomato cluster detection and counting using improved YOLOv5 based on RGB-D fusion","volume":"207","author":"Rong","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106285","DOI":"10.1016\/j.compag.2021.106285","article-title":"Classification of rice varieties with deep learning methods","volume":"187","author":"Koklu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, B., Gao, Z., Gao, Y., and Zhu, Y. (2021). Rapid detection and counting of wheat ears in the field using YOLOv4 with attention module. Agronomy, 11.","DOI":"10.3390\/agronomy11061202"},{"key":"ref_18","first-page":"171","article-title":"Image recognition of soybean pests based on attention convolutional neural network","volume":"41","author":"Sun","year":"2020","journal-title":"J. Chin. Agric. Mech."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, W., Ma, H., Li, X., Liu, X., Jiao, J., Zhang, P., Gu, L., Wang, Q., Bao, W., and Cao, S. (2021). Imperfect wheat grain recognition combined with an attention mechanism and residual network. Appl. Sci., 11.","DOI":"10.3390\/app11115139"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wen, G., Li, S., Liu, F., Luo, X., Er, M.J., Mahmud, M., and Wu, T. (2023). YOLOv5s-CA: A Modified YOLOv5s Network with Coordinate Attention for Underwater Target Detection. Sensors, 23.","DOI":"10.3390\/s23073367"},{"key":"ref_21","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, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_22","first-page":"957","article-title":"Key problems and progress of vision Transformers: The state of the art and prospects","volume":"48","author":"Tian","year":"2022","journal-title":"Acta Autom. Sin."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.compag.2018.08.001","article-title":"Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review","volume":"153","author":"Rieder","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"966495","DOI":"10.3389\/fpls.2022.966495","article-title":"Field rice panicle detection and counting based on deep learning","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lang, X., Ren, Z., Wan, D., Zhang, Y., and Shu, S. (2022). MR-YOLO: An improved YOLOv5 network for detecting magnetic ring surface defects. Sensors, 22.","DOI":"10.3390\/s22249897"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","article-title":"Recent advances in deep learning for object detection","volume":"396","author":"Wu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, C., Chen, M., Zhu, D., Chen, X., and Liao, J. (2022). A lightweight semantic segmentation model of Wucai seedlings based on attention mechanism. Photonics, 9.","DOI":"10.3390\/photonics9060393"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1016\/S2095-3119(19)62803-0","article-title":"Detection and enumeration of wheat grains based on a deep learning method under various scenarios and scales","volume":"19","author":"Wu","year":"2020","journal-title":"J. Integr. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_31","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 15\u201317). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Oxford, UK.","DOI":"10.1109\/CVPR52729.2023.00721"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9129\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:21:49Z","timestamp":1760131309000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,12]]},"references-count":32,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23229129"],"URL":"https:\/\/doi.org\/10.3390\/s23229129","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,12]]}}}