{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:45:39Z","timestamp":1768344339637,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nature Science Foundation of Fujian Province of P. R. China","award":["2025J01585"],"award-info":[{"award-number":["2025J01585"]}]},{"name":"Nature Science Foundation of Fujian Province of P. R. China","award":["2025J01586"],"award-info":[{"award-number":["2025J01586"]}]},{"name":"Special Fund for Scientific and Technological Innovation of Fujian Agriculture and Forestry University","award":["KFB23158"],"award-info":[{"award-number":["KFB23158"]}]},{"name":"Special Fund for Scientific and Technological Innovation of Fujian Agriculture and Forestry University","award":["KFB24048"],"award-info":[{"award-number":["KFB24048"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cotton is an important economic crop, and its weed management directly affects yield and quality. In actual cotton fields, detection accuracy still faces challenges due to the complex types of weeds, variable morphologies, and environmental factors. Most existing models rely on the attention mechanism to improve performance, but channel attention tends to ignore spatial information, while full spatial attention brings high computational costs. Therefore, this paper proposes a grouped enhanced fusion attention mechanism (GEFA), which combines grouped convolution and local spatial attention to reduce complexity and parameter quantity while effectively enhancing feature expression ability. The GEFAY detection model constructed based on GEFA achieves good balance in efficiency, accuracy, and complexity on the CottonWeedDet12, VOC, and COCO datasets. Compared with classic attention methods, this model has the smallest increase in parameters and computational costs while significantly improving accuracy. It is more suitable for deployment on edge devices. The further designed end-to-end intelligent weed detection system and edge device deployment can achieve image detection on local maps and real-time cameras, with good practicality and scalability, providing effective technical support for intelligent visual applications in precision agriculture.<\/jats:p>","DOI":"10.3390\/s26020540","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T15:52:57Z","timestamp":1768319577000},"page":"540","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GEFA-YOLO: Lightweight Weed Detection with Group-Enhanced Fusion Attention"],"prefix":"10.3390","volume":"26","author":[{"given":"Huicheng","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"Key Laboratory of Smart Agriculture and Forestry, Fujian Province University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pushi","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"Key Laboratory of Smart Agriculture and Forestry, Fujian Province University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Kang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"Key Laboratory of Smart Agriculture and Forestry, Fujian Province University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuting","family":"Su","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"Key Laboratory of Smart Agriculture and Forestry, Fujian Province University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1210-3758","authenticated-orcid":false,"given":"Qi","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"Key Laboratory of Smart Agriculture and Forestry, Fujian Province University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhou","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"Key Laboratory of Smart Agriculture and Forestry, Fujian Province University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4002-4894","authenticated-orcid":false,"given":"Lijin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"Key Laboratory of Smart Agriculture and Forestry, Fujian Province University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Li, H., Cai, Z., Zhong, Y., Zhong, F., Lin, X., and Wang, L. (2025). YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection. Sensors, 25.","DOI":"10.3390\/s25051635"},{"key":"ref_2","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 (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_3","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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Balasubramaniam, A., and Pasricha, S. (2022). Object Detection in Autonomous Vehicles: Status and Open Challenges. arXiv.","DOI":"10.1007\/978-3-031-28016-0_17"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Juyal, A., Sharma, S., and Matta, P. (2021, January 3\u20135). Deep Learning Methods for Object Detection in Autonomous Vehicles. Proceedings of the 5th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, Tamil Nadu, India.","DOI":"10.1109\/ICOEI51242.2021.9452932"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8875910","DOI":"10.1155\/2020\/8875910","article-title":"Deep Learning for Retail Product Recognition: Challenges and Techniques","volume":"2020","author":"Wei","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_8","unstructured":"Sinha, A., Banerjee, S., and Chattopadhyay, P. (2022). An Improved Deep Learning Approach for Product Recognition on Racks in Retail Stores. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Ta, T.N., Nguyen, N.C., Bui, V.T., Pham, H.M., and Nguyen, D.M. (2021, January 13\u201315). YOLO-Based Real-Time Human Detection for Smart Video Surveillance at the Edge. Proceedings of the IEEE International Conference on Communications and Electronics (ICCE), Phu Quoc Island, Vietnam.","DOI":"10.1109\/ICCE48956.2021.9352144"},{"key":"ref_10","first-page":"506","article-title":"Tea-Bud Detection Based on a Hybrid Attention Mechanism","volume":"17","author":"Wang","year":"2025","journal-title":"J. Nanjing Univ. Inf. Sci. Technol. (Natural Sci. Ed.)"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, Z., Su, Y., Kang, F., Wang, L., Lin, Y., Wu, Q., Li, H., and Cai, Z. (2025). PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection. Sensors, 25.","DOI":"10.3390\/s25020348"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"He, C., Wan, F., Ma, G., Mou, X., Zhang, K., Wu, X., and Huang, X. (2024). Analysis of the Impact of Different Improvement Methods Based on YOLOv8 for Weed Detection. Agriculture, 14.","DOI":"10.3390\/agriculture14050674"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, J.L., Su, W.H., Zhang, H.Y., and Peng, Y. (2022). SE-YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables. Agronomy, 12.","DOI":"10.3390\/agronomy12092061"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107412","DOI":"10.1016\/j.compag.2022.107412","article-title":"Weed Detection in Sesame Fields Using a YOLO Model with an Enhanced Attention Mechanism and Feature Fusion","volume":"202","author":"Chen","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, Q., Qiao, Y., Zhang, X., Lu, C., and Wang, C. (2024). Precision Weed Management for Straw-Mulched Maize Field: Advanced Weed Detection and Targeted Spraying Based on Enhanced YOLOv5s. Agriculture, 14.","DOI":"10.3390\/agriculture14122134"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guo, X., Ou, Y., Deng, K., Fan, X., Gao, R., and Zhou, Z. (2025). A Unmanned Aerial Vehicle-Based Image Information Acquisition Technique for the Middle and Lower Sections of Rice Plants and a Predictive Algorithm Model for Pest and Disease Detection. Agriculture, 15.","DOI":"10.3390\/agriculture15070790"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sun, C., Zhou, X., Zhang, M., and Qin, A. (2023). SE-VisionTransformer: Hybrid Network for Diagnosing Sugarcane Leaf Diseases Based on Attention Mechanism. Sensors, 23.","DOI":"10.3390\/s23208529"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, Z., Chen, X., Li, M., Chi, Y., and Shi, D. (2024). Construction and Validation of Peanut Leaf Spot Disease Prediction Model Based on Long Time Series Data and Deep Learning. Agronomy, 14.","DOI":"10.3390\/agronomy14020294"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhang, P., Wu, F., and Li, X. (2021, January 10\u201317). FCANet: Frequency Channel Attention Networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"ref_22","unstructured":"Gao, Z., Xie, J., Wang, Q., and Li, P. (, January 15\u201320June). Global Second-Order Pooling Convolutional Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Beach, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106442","DOI":"10.1016\/j.engappai.2023.106442","article-title":"Mixed Local Channel Attention for Object Detection","volume":"123","author":"Wan","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 15\u201320). Selective Kernel Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_26","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_27","first-page":"16362","article-title":"Bayesian Attention Modules","volume":"33","author":"Fan","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","unstructured":"Li, X., Zhong, Z., Wu, J., Yang, Y., Lin, Z., and Liu, H. (November, January 27). Expectation-Maximization Attention Networks for Semantic Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_29","unstructured":"Zhang, L., Winn, J., and Tomioka, R. (2016). Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering. arXiv."},{"key":"ref_30","unstructured":"Ultralytics (2023, February 13). YOLOv5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107655","DOI":"10.1016\/j.compag.2023.107655","article-title":"YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems","volume":"205","author":"Dang","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. (2018, January 12\u201315). Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., Zhang, H., Dong, Y., and Wei, S. (2019). A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds. Remote Sens., 11.","DOI":"10.3390\/rs11070765"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5592878","DOI":"10.1155\/2021\/5592878","article-title":"A New Steel Defect Detection Algorithm Based on Deep Learning","volume":"2021","author":"Zhao","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_39","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (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, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_41","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Jiang, C. (2025). DAHD-YOLO: A New High Robustness and Real-Time Method for Smoking Detection. Sensors, 25.","DOI":"10.3390\/s25051433"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kurniawan, H., Arief, M.A.A., Manggala, B., Kim, H., Lee, S., Kim, M.S., Baek, I., and Cho, B.K. (2025). Development of an Intelligent Inspection System Based on YOLOv7 for Real-Time Detection of Foreign Materials in Fresh-Cut Vegetables. Agriculture, 15.","DOI":"10.3390\/agriculture15212297"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/2\/540\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T15:55:48Z","timestamp":1768319748000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/2\/540"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,13]]},"references-count":43,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["s26020540"],"URL":"https:\/\/doi.org\/10.3390\/s26020540","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,13]]}}}