{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T02:29:41Z","timestamp":1773368981273,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2021MC168"],"award-info":[{"award-number":["ZR2021MC168"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The number of wheat ears in a field is an important parameter for accurately estimating wheat yield. In a large field, however, it is hard to conduct an automated and accurate counting of wheat ears because of their density and mutual overlay. Unlike the majority of the studies conducted on deep learning-based methods that usually count wheat ears via a collection of static images, this paper proposes a counting method based directly on a UAV video multi-objective tracking method and better counting efficiency results. Firstly, we optimized the YOLOv7 model because the basis of the multi-target tracking algorithm is target detection. Simultaneously, the omni-dimensional dynamic convolution (ODConv) design was applied to the network structure to significantly improve the feature-extraction capability of the model, strengthen the interaction between dimensions, and improve the performance of the detection model. Furthermore, the global context network (GCNet) and coordinate attention (CA) mechanisms were adopted in the backbone network to implement the effective utilization of wheat features. Secondly, this study improved the DeepSort multi-objective tracking algorithm by replacing the DeepSort feature extractor with a modified ResNet network structure to achieve a better extraction of wheat-ear-feature information, and the constructed dataset was then trained for the re-identification of wheat ears. Finally, the improved DeepSort algorithm was used to calculate the number of different IDs that appear in the video, and an improved method based on YOLOv7 and DeepSort algorithms was then created to calculate the number of wheat ears in large fields. The results show that the mean average precision (mAP) of the improved YOLOv7 detection model is 2.5% higher than that of the original YOLOv7 model, reaching 96.2%. The multiple-object tracking accuracy (MOTA) of the improved YOLOv7\u2013DeepSort model reached 75.4%. By verifying the number of wheat ears captured by the UAV method, it can be determined that the average value of an L1 loss is 4.2 and the accuracy rate is between 95 and 98%; thus, detection and tracking methods can be effectively performed, and the efficient counting of wheat ears can be achieved according to the ID value in the video.<\/jats:p>","DOI":"10.3390\/s23104880","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:55:29Z","timestamp":1684457729000},"page":"4880","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0613-0620","authenticated-orcid":false,"given":"Tianle","family":"Wu","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suyang","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Geng","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"032006","DOI":"10.1088\/1742-6596\/1802\/3\/032006","article-title":"An Efficient Lane Line Detection Method Based on Computer Vision","volume":"1802","author":"Zhu","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"012036","DOI":"10.1088\/1742-6596\/1732\/1\/012036","article-title":"Cricket Video Events Recognition using HOG, LBP and Multi-class SVM","volume":"1732","author":"Abbas","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_4","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_5","first-page":"405","article-title":"Detection and analysis of wheat spikes using Convolutional Neural Networks","volume":"15","author":"Hasan","year":"2018","journal-title":"Plant. Methods"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, J., Li, C., Fei, S., Ma, C., and Xiao, Z. (2021). Wheat Ear Recognition Based on RetinaNet and Transfer Learning. Sensors, 21.","DOI":"10.3390\/s21144845"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.compag.2022.107087","article-title":"A deep learning method for oriented and small wheat spike detection (OSWSDet) in UAV images","volume":"198","author":"Zhao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9846158","DOI":"10.34133\/2021\/9846158","article-title":"Global Wheat Head Detection 2021:An Improved Dataset for Benchmarking Wheat Head Detection Methods","volume":"2021","author":"David","year":"2021","journal-title":"Plant. Phenomics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.agrformet.2018.10.013","article-title":"Ear density estimation from high resolution RGB imagery using deep learning technique","volume":"264","author":"Madec","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"189043","DOI":"10.1109\/ACCESS.2020.3031896","article-title":"A Robust Method for Wheatear Detection Using UAV in Natural Scenes","volume":"8","author":"He","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.3390\/rs13132496","article-title":"Parts-per-Object Count in Agricultural Images: Solving Phenotyping Problems via a Single Deep Neural Network","volume":"13","year":"2021","journal-title":"Remote Sens."},{"key":"ref_12","first-page":"1","article-title":"TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression","volume":"60","author":"Lu","year":"2022","journal-title":"IEEE Trans. Geosci. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.3390\/agronomy11061202","article-title":"Rapid Detection and Counting of Wheat Ears in the Field Using YOLOv4 with Attention Module","volume":"11","author":"Zhu","year":"2021","journal-title":"Agronomy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhang, X., and Yan, J.A. (2021). Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5. Remote Sens., 13.","DOI":"10.3390\/rs13163095"},{"key":"ref_15","first-page":"6517","article-title":"YOLO9000: Better, Faster, Stronger","volume":"1612","author":"Redmon","year":"2017","journal-title":"Ieice T Fund Electr."},{"key":"ref_16","unstructured":"Redmon, J., and Farhadi, A. (2018, January 18\u201322). YOLOv3: An Incremental Improvement. Proceedings of the Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_17","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H. (2020, January 14\u201319). YOLOv4: Optimal Speed and Accuracy of Object Detection. Proceedings of the Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_18","unstructured":"(2022, April 03). GitHub. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H. (2022, January 19\u201324). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Osman, Y., Dennis, R., and Elgazzar, K. (2021). Yield Estimation and Visualization Solution for Precision Agriculture. Sensors, 21.","DOI":"10.3390\/s21196657"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ge, Y., Lin, S., Zhang, Y., Li, Z., Cheng, H., Dong, J., Shao, S., Zhang, J., Qi, X., and Wu, Z. (2022). Tracking and Counting of Tomato at Different Growth Period Using an Improving YOLO-Deepsort Network for Inspection Robot. Machines, 10.","DOI":"10.3390\/machines10060489"},{"key":"ref_22","first-page":"22147","article-title":"An efficient online citrus counting system for large-scale unstructured orchards based on the unmanned aerial vehicle","volume":"10","author":"Zheng","year":"2022","journal-title":"J. Field Robot."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7748350","DOI":"10.1155\/2021\/7748350","article-title":"Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction","volume":"2021","author":"Xu","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7918165","DOI":"10.1155\/2021\/7918165","article-title":"Real-Time Human Ear Detection Based on the Joint of Yolo and RetinaFace","volume":"2021","author":"Quoc","year":"2021","journal-title":"Complexity"},{"key":"ref_25","unstructured":"Li, C., Zhou, A.J., and Yao, A.B. (2022, January 25\u201329). Omni-Dimensional Dynamic Convolution. Proceedings of the International Conference on Learning Representations, Online."},{"key":"ref_26","unstructured":"Lin, X., Guo, Y.A., and Wang, J. (2021, January 19\u201325). In Global Correlation Network: End-to-End Joint Multi-Object Detection and Tracking. Proceedings of the Computer Vision and Pattern Recognition, Online."},{"key":"ref_27","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 Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017, January 17\u201320). Simple Online and Realtime Tracking with a Deep Association Metric. Proceedings of the IEEE International Conference on Image Processing, Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_29","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_30","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 Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_31","unstructured":"Zhang, X., Yin, W., Gou, M., Sznaier, M., and Camps, O. (July, January 26). In Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold. Proceedings of the Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_32","first-page":"4400","article-title":"Network In Network","volume":"1312","author":"Lin","year":"2013","journal-title":"Ieice T Fund Electr."},{"key":"ref_33","unstructured":"Yang, B., Bender, G., Ngiam, J., and Le, Q.V. (2020, January 14\u201319). CondConv: Conditionally Parameterized Convolutions for Efficient Inference. Proceedings of the Computer Vision and Pattern Recognition, Long Beach, CA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., and Liu, Z. (2020, January 14\u201319). Dynamic Convolution: Attention Over Convolution Kernels. Proceedings of the Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jiang, B., Luo, R., Mao, J., Xiao, T., and Jiang, Y. (2018, January 8\u201314). Acquisition of Localization Confidence for Accurate Object Detection. Proceedings of the European conference on computer vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_48"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4880\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:37:58Z","timestamp":1760125078000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4880"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"references-count":35,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104880"],"URL":"https:\/\/doi.org\/10.3390\/s23104880","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,18]]}}}