{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T10:55:21Z","timestamp":1781520921509,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,3]],"date-time":"2024-03-03T00:00:00Z","timestamp":1709424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Program of Shanxi Province","award":["202303021212115"],"award-info":[{"award-number":["202303021212115"]}]},{"name":"Fundamental Research Program of Shanxi Province","award":["2020QC17"],"award-info":[{"award-number":["2020QC17"]}]},{"name":"Shanxi Agricultural University Youth Science and Technology Innovation Fund","award":["202303021212115"],"award-info":[{"award-number":["202303021212115"]}]},{"name":"Shanxi Agricultural University Youth Science and Technology Innovation Fund","award":["2020QC17"],"award-info":[{"award-number":["2020QC17"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wheat seed detection has important applications in calculating thousand-grain weight and crop breeding. In order to solve the problems of seed accumulation, adhesion, and occlusion that can lead to low counting accuracy, while ensuring fast detection speed with high accuracy, a wheat seed counting method is proposed to provide technical support for the development of the embedded platform of the seed counter. This study proposes a lightweight real-time wheat seed detection model, YOLOv8-HD, based on YOLOv8. Firstly, we introduce the concept of shared convolutional layers to improve the YOLOv8 detection head, reducing the number of parameters and achieving a lightweight design to improve runtime speed. Secondly, we incorporate the Vision Transformer with a Deformable Attention mechanism into the C2f module of the backbone network to enhance the network\u2019s feature extraction capability and improve detection accuracy. The results show that in the stacked scenes with impurities (severe seed adhesion), the YOLOv8-HD model achieves an average detection accuracy (mAP) of 77.6%, which is 9.1% higher than YOLOv8. In all scenes, the YOLOv8-HD model achieves an average detection accuracy (mAP) of 99.3%, which is 16.8% higher than YOLOv8. The memory size of the YOLOv8-HD model is 6.35 MB, approximately 4\/5 of YOLOv8. The GFLOPs of YOLOv8-HD decrease by 16%. The inference time of YOLOv8-HD is 2.86 ms (on GPU), which is lower than YOLOv8. Finally, we conducted numerous experiments and the results showed that YOLOv8-HD outperforms other mainstream networks in terms of mAP, speed, and model size. Therefore, our YOLOv8-HD can efficiently detect wheat seeds in various scenarios, providing technical support for the development of seed counting instruments.<\/jats:p>","DOI":"10.3390\/s24051654","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T04:36:21Z","timestamp":1709526981000},"page":"1654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Wheat Seed Detection and Counting Method Based on Improved YOLOv8 Model"],"prefix":"10.3390","volume":"24","author":[{"given":"Na","family":"Ma","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Shanxi Agricultural University, Taigu District, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaxin","family":"Su","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shanxi Agricultural University, Taigu District, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lexin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shanxi Agricultural University, Taigu District, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongtao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shanxi Agricultural University, Taigu District, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwen","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shanxi Agricultural University, Taigu District, Jinzhong 030801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xing, X., Liu, C., Han, J., Feng, Q., Lu, Q., and Feng, Y. (2023). Wheat-Seed Variety Recognition Based on the GC_DRNet Model. Agriculture, 13.","DOI":"10.3390\/agriculture13112056"},{"key":"ref_2","unstructured":"Zhou, L. (2022). Research on Wheat Phenotypic Information Perception Method Based on Spectrum and Image. [Ph.D. Thesis, Zhejiang University]. (In Chinese with English Abstract)."},{"key":"ref_3","first-page":"230","article-title":"Research on the Production Pattern and Fertilization Status of Wheat in China\u2019s Dominant Regions","volume":"44","author":"Yan","year":"2024","journal-title":"J. Titioeae Crops"},{"key":"ref_4","first-page":"171","article-title":"Detection of rice seed vigor level by using deep feature of hyperspectral images","volume":"37","author":"Sun","year":"2021","journal-title":"Trans. CSAE"},{"key":"ref_5","unstructured":"Liu, X. (2022). Research on Automatic Counting of Wheat Seed Based on Image Processing. [Bachelor\u2019s Thesis, Anhui Agriculture University]. (In Chinese with English Abstract)."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, H., Ji, J., Ma, H., Guo, H., Liu, N., and Cui, H. (2023). Wheat Seed Phenotype Detection Device and Its Application. Agriculture, 13.","DOI":"10.3390\/agriculture13030706"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3394","DOI":"10.1080\/01431161.2017.1295482","article-title":"Yield estimation and forecasting for winter wheat in hungary using time series of MODIS data","volume":"38","author":"Kern","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1007\/s11032-020-01147-3","article-title":"A major and stable QTL controlling wheat thousand seed weight: Identification, characterization, and CAPS marker development","volume":"40","author":"Duan","year":"2020","journal-title":"Mol. Breed."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zuo, Z., Zhang, Z., Huang, D., Fan, Y., Yu, S., Zhuang, J., and Zhu, Y. (2022). Control of thousand-grain weight by OsMADS56 in rice. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23010125"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.tifs.2016.07.011","article-title":"Machine vision system for food grain quality evaluation: A review","volume":"56","author":"Vithu","year":"2016","journal-title":"Trends Food Sci. Technol."},{"key":"ref_11","first-page":"1","article-title":"Computer vision technology in agricultural automation\u2014A review","volume":"7","author":"Tian","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"ref_12","first-page":"18","article-title":"Research Status and Prospect of Rice and Wheat Grain Counting Methods","volume":"12","author":"Zhou","year":"2020","journal-title":"Mod. Agric. Sci. Technol."},{"key":"ref_13","first-page":"86","article-title":"Estimation and counting of wheat ears density in field based on deep convolutional neural network","volume":"36","author":"Bao","year":"2020","journal-title":"Trans. CSAE"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, P., and Li, Y. (2009, January 19\u201320). Grain counting method based on image processing. Proceedings of the 2009 International Conference on Information Engineering and Computer Science, Wuhan, China.","DOI":"10.1109\/ICIECS.2009.5364719"},{"key":"ref_15","unstructured":"Zhao, M., Qin, J., Li, S., Liu, Z., Yao, X., Ye, S., and Li, L. (2015). IFIP Advances in Information and Communication Technology, Springer International Publishing."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"122","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_17","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/s13042-020-01096-5","article-title":"Recent advances in deep learning","volume":"11","author":"Wang","year":"2020","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100379","DOI":"10.1016\/j.cosrev.2021.100379","article-title":"A survey on deep learning and its applications","volume":"40","author":"Dong","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","article-title":"Machine learning and deep learning","volume":"31","author":"Janiesch","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"56683","DOI":"10.1109\/ACCESS.2021.3069646","article-title":"Plant disease detection and classification by deep learning\u2014A review","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/s13198-020-00972-1","article-title":"Systematic review of deep learning techniques in plant disease detection","volume":"11","author":"Nagaraju","year":"2020","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ashok, S., Kishore, G., Rajesh, V., Suchitra, S., Sophia, S., and Pavithra, B. (2020). Tomato Leaf Disease Detection Using Deep Learning Techniques, IEEE.","DOI":"10.1109\/ICCES48766.2020.9137986"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"294","DOI":"10.3390\/agriengineering3020020","article-title":"Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques","volume":"3","author":"Chowdhury","year":"2021","journal-title":"AgriEngineering"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Meng, X., Li, C., Li, J., Li, X., Guo, F., and Xiao, Z. (2023). YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting. Remote Sens., 15.","DOI":"10.3390\/rs15153770"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wu, T., Zhong, S., Chen, H., and Geng, X. (2023). Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort. Sensors, 23.","DOI":"10.3390\/s23104880"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1111\/tpj.14799","article-title":"Automatic wheat ear counting using machine learning based on RGB UAV imagery","volume":"103","author":"Lootens","year":"2020","journal-title":"Plant J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"645899","DOI":"10.3389\/fpls.2021.645899","article-title":"Occlusion robust wheat ear counting algorithm based on deep learning","volume":"12","author":"Wang","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jiang, Y., and Li, C. (2020). Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review, NAU.","DOI":"10.34133\/2020\/4152816"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s10681-022-02992-3","article-title":"Deep learning: As the new frontier in high-throughput plant phenotyping","volume":"218","author":"Arya","year":"2022","journal-title":"Euphytica"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xiong, J., Yu, D., Liu, S., Shu, L., Wang, X., and Liu, Z. (2021). A review of plant phenotypic image recognition technology based on deep learning. Electronics, 10.","DOI":"10.3390\/electronics10010081"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"274","DOI":"10.3390\/ai2020017","article-title":"Artificial intelligence in smart farms: Plant phenotyping for species recognition and health condition identification using deep learning","volume":"2","author":"Hati","year":"2021","journal-title":"AI"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Deng, R., Tao, M., Huang, X., Bangura, K., Jiang, Q., Jiang, Y., and Qi, L. (2021). Automated counting grains on the rice panicle based on deep learning method. Sensors, 21.","DOI":"10.3390\/s21010281"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"64177","DOI":"10.1109\/ACCESS.2019.2916931","article-title":"Soybean seed counting based on pod image using two-column convolution neural network","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Devasena, D., Dharshan, Y., Sharmila, B., Aarthi, S., Preethi, S., and Shuruthi, M. (2023). Mobile Application Based Seed Counting Analysis Using Deep-Learning, IEEE.","DOI":"10.1109\/ACCTHPA57160.2023.10083344"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shi, L., Sun, J., Dang, Y., Zhang, S., Sun, X., Xi, L., and Wang, J. (2023). YOLOv5s-T: A Lightweight Small Object Detection Method for Wheat Spikelet Counting. Agriculture, 13.","DOI":"10.3390\/agriculture13040872"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Feng, A., Li, H., Liu, Z., Luo, Y., Pu, H., Lin, B., and Liu, T. (2021). Research on a rice counting algorithm based on an improved MCNN and a density map. Entropy, 23.","DOI":"10.3390\/e23060721"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e13787","DOI":"10.1111\/jfpe.13787","article-title":"Deep learning optimization method for counting overlapping rice seeds","volume":"44","author":"Sun","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_38","first-page":"193","article-title":"Research on wheat external quality inspection method based on machine vision","volume":"15","author":"Zhang","year":"2019","journal-title":"Comput. Knowl. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.biosystemseng.2016.04.008","article-title":"A two-camera machine vision approach to separating and identifying laboratory sprouted wheat kernels","volume":"147","author":"Shrestha","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_40","first-page":"87","article-title":"Research on the segmentation method of corn kernel cohesion based on image","volume":"36","author":"Yang","year":"2019","journal-title":"Sci. Technol. Innov."},{"key":"ref_41","first-page":"245","article-title":"Detection Method of Severe Adhesive Wheat Grain Based on YOLOv5-MDC Model","volume":"53","author":"Song","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xia, Z., Pan, X., Song, S., Li, L., and Huang, G. (2022, January 18\u201324). Vision transformer with deformable attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00475"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Wang, C., Bochkovskiy, A., and Liao, H. (2023, January 17\u201324). 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 (CVPR), Vancouver, BC, Canada. Available online: https:\/\/arxiv.org\/abs\/2207.02696.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_45","unstructured":"Chen, Y., Yuan, X., Wu, R., Wang, J., Hou, Q., and Cheng, M. (2023). YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection. arXiv."},{"key":"ref_46","unstructured":"Zhang, X., Liu, C., Yang, D., Song, T., Ye, Y., Li, K., and Song, Y. (2023). RFAConv: Innovating Spatital Attention and Standard Convolutional Operation. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1654\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:08:41Z","timestamp":1760105321000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,3]]},"references-count":46,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24051654"],"URL":"https:\/\/doi.org\/10.3390\/s24051654","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,3]]}}}