{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:10:21Z","timestamp":1792789821155,"version":"4.5.0"},"reference-count":49,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T00:00:00Z","timestamp":1789862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation Project of Heilongjiang Province","award":["LH2024F052"],"award-info":[{"award-number":["LH2024F052"]}]},{"name":"Natural Science Foundation Project of Heilongjiang Province","award":["XQ2026T009"],"award-info":[{"award-number":["XQ2026T009"]}]},{"name":"Ecological and Environmental Protection Research Project in Heilongjiang Province","award":["HST2025TR010"],"award-info":[{"award-number":["HST2025TR010"]}]},{"DOI":"10.13039\/501100008828","name":"Heilongjiang Bayi Agricultural University","doi-asserted-by":"publisher","award":["XYB202613"],"award-info":[{"award-number":["XYB202613"]}],"id":[{"id":"10.13039\/501100008828","id-type":"DOI","asserted-by":"publisher"}]},{"award":["XYB202613"],"award-info":[{"award-number":["XYB202613"]}],"id":[{"id":"https:\/\/ror.org\/030jxf285","id-type":"ROR","asserted-by":"publisher"}]},{"name":"\u201cDouble First Class\u201d Discipline Collaborative Innovation Achievement Project in Heilongjiang Province","award":["LJGXCG2026-P44"],"award-info":[{"award-number":["LJGXCG2026-P44"]}]},{"name":"Key R&D Program Project of Mudanjiang City","award":["XDHB25BY003"],"award-info":[{"award-number":["XDHB25BY003"]}]},{"name":"China Agriculture Research System of MOF and MARA","award":["CARS-04-PS32"],"award-info":[{"award-number":["CARS-04-PS32"]}],"id":[{"id":"https:\/\/ror.org\/03p5eky76","id-type":"ROR","asserted-by":"crossref"}]},{"award":["CARS-04-PS32"],"award-info":[{"award-number":["CARS-04-PS32"]}],"id":[{"id":"https:\/\/ror.org\/03p5eky76","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>Accurate identification of maize seedlings and weeds at the seedling stage in dry farmland of cold regions serves as an essential prerequisite for field variable weeding and intelligent plant protection operations. Existing YOLO networks exhibit prominent limitations in feature extraction for fine weeds that grow close to the ground and feature fusion across different scales, and show poor adaptability to complex field conditions including illumination fluctuations, tiny weed interference, and mixed soil textures. To address these issues, this study focuses on maize fields in the cold region at a high latitude in Heilongjiang and collects RGB field images of maize plants at the two to five leaf stage. Using YOLOv12n as the baseline model, the internal convolutional blocks within the C3k2 structure are replaced with RepViTBlock to construct the C3k2-R module. The Multi-Frequency in Multi-Scale Attention (MFMSA) module is embedded into A2C2f to obtain A2C2f-M. BiFPN is adopted to replace the original concatenation block to form Concat-B. By integrating the above three optimized modules, the improved model YOLOv12n-RMB is developed. The results demonstrate that YOLOv12n-RMB achieves an AP@0.5 of 0.9745 for maize and 0.7820 for weeds, with an overall mAP@0.5 improvement of 0.0421 relative to the original YOLOv12n. The proposed model can perform robustly under diverse field working conditions such as strong light, weak light, and varying seedling ages. This research provides visual technical support for field weed density evaluation and intelligent decision-making for variable weeding equipment.<\/jats:p>","DOI":"10.3390\/plants15182876","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T11:05:53Z","timestamp":1789988753000},"page":"2876","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLOv12n-RMB: An Improved Dual Target Recognition Network for Seedling Maize and Field Weeds in Cold Regions"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3986-1663","authenticated-orcid":false,"given":"Jinyang","family":"Li","sequence":"first","affiliation":[{"name":"College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Yagang","family":"Du","sequence":"additional","affiliation":[{"name":"Heilongjiang Academy of Land Reclamation Sciences, Harbin 150036, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"given":"Xianbin","family":"Wu","sequence":"additional","affiliation":[{"name":"Heilongjiang Academy of Land Reclamation Sciences, Harbin 150036, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Hongfei","family":"Dai","sequence":"additional","affiliation":[{"name":"Heilongjiang Academy of Land Reclamation Sciences, Harbin 150036, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"given":"Chuntao","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Ziyi","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163319, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"formal-analysis","vocabulary":"credit"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111219","DOI":"10.1016\/j.compag.2025.111219","article-title":"3D sensing and perception for precision weed management: A review of modalities, reconstruction pipelines, and context-aware detection","volume":"240","author":"Malik","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107237","DOI":"10.1016\/j.cropro.2025.107237","article-title":"Deep learning models for detection and recognition of weed species in corn crop","volume":"195","author":"Wirti","year":"2025","journal-title":"Crop Prot."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104449","DOI":"10.1016\/j.cropro.2018.02.001","article-title":"Weed research status, challenges, and opportunities in China","volume":"134","author":"Zhu","year":"2020","journal-title":"Crop Prot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.compag.2019.02.005","article-title":"A review on weed detection using ground-based machine vision and image processing techniques","volume":"158","author":"Wang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"115938","DOI":"10.1016\/j.measurement.2024.115938","article-title":"Recognition of maize seedling under weed disturbance using improved YOLOv5 algorithm","volume":"242","author":"Tang","year":"2025","journal-title":"Measurement"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Liu, Y., Gong, C., Chen, Y., and Yu, H. (2020). Applications of deep learning for dense scenes analysis in agriculture: A review. Sensors, 20.","DOI":"10.3390\/s20051520"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"133404","DOI":"10.1016\/j.neucom.2026.133404","article-title":"YOLO-CottWed: A lightweight network for fine-grained weed detection in cotton fields","volume":"681","author":"Li","year":"2026","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107076","DOI":"10.1016\/j.cropro.2024.107076","article-title":"Advancing precision agriculture with computer vision: A comparative study of YOLO models for weed and crop recognition","volume":"190","author":"Zoubek","year":"2025","journal-title":"Crop Prot."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zheng, L., Yi, J., He, P., Tie, J., Zhang, Y., Wu, W., and Long, L. (2024). Improvement of the YOLOv8 Model in the Optimization of the Weed Recognition Algorithm in Cotton Field. Plants, 13.","DOI":"10.3390\/plants13131843"},{"key":"ref_10","first-page":"102144","article-title":"Multiclass weed and crop detection using optimized YOLO models on edge devices","volume":"22","author":"Upadhyay","year":"2025","journal-title":"J. Agric. Food Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"113329","DOI":"10.1016\/j.engappai.2025.113329","article-title":"An enhanced segmentation network built upon the you only look once framework for precise weed recognition in early-stage cotton","volume":"164","author":"Qin","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"114183","DOI":"10.1016\/j.asoc.2025.114183","article-title":"A lightweight weed detection algorithm designed based on YOLOv9","volume":"186","author":"Liu","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107451","DOI":"10.1016\/j.cropro.2025.107451","article-title":"A lightweight detection algorithm for cereal weed recognition based on deep learning","volume":"199","author":"Dong","year":"2025","journal-title":"Crop Prot."},{"key":"ref_14","first-page":"275","article-title":"Corn and Weeds Faster Identification Method Based on Improved YOLOv8n in Field","volume":"57","author":"Qi","year":"2026","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101134","DOI":"10.1016\/j.atech.2025.101134","article-title":"YOLOv9-GSSA model for efficient soybean seedlings and weeds detection","volume":"12","author":"Liang","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"110306","DOI":"10.1016\/j.compag.2025.110306","article-title":"Star-YOLO: A lightweight and efficient model for weed detection in cotton fields using advanced YOLOv8 improvements","volume":"235","author":"Lu","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","first-page":"682","article-title":"Accurate Real-Time Urban Waste Detection Using an Enhanced YOLOv9-EfficientViT Framework","volume":"25","author":"Maiza","year":"2026","journal-title":"Inform. Autom."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.aej.2026.02.030","article-title":"DL-LY: Distributed swarm learning empowered light-weight YOLOv11 for heterogeneous weed detection in UAV images","volume":"141","author":"Liu","year":"2026","journal-title":"Alex. Eng. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhuang, X., Zhang, J., Yang, D., Gao, L., and Jin, J. (2026). A Weed Location Method Based on MCS-YOLOv8 and Adaptive Filtering. Agriculture, 16.","DOI":"10.3390\/agriculture16131437"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101924","DOI":"10.1016\/j.atech.2026.101924","article-title":"YOLO-EMA: Efficient mamba attention enhanced YOLOv10 for real-time detection and segmentation of winter wheat weeds on edge AI platforms","volume":"13","author":"Wang","year":"2026","journal-title":"Smart Agric. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, Y., Zhang, R., Fu, R., Wu, Y., Song, X., Chang, H., Xia, S., and Liu, Y. (2026). Toward autonomous agriculture: Integrating computer vision and large language models for intelligent weed management. Artif. Intell. Agric., in press.","DOI":"10.1016\/j.aiia.2026.05.002"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sun, T., Le, F., Cai, C., Jin, Y., Xue, X., and Cui, L. (2025). Soybean\u2013Corn Seedling Crop Row Detection for Agricultural Autonomous Navigation Based on GD-YOLOv10n-Seg. Agriculture, 15.","DOI":"10.3390\/agriculture15070796"},{"key":"ref_23","first-page":"225","article-title":"Laser weeding weed detection method based on LSPKI-YOLO multi-scale feature enhancement","volume":"42","author":"Liang","year":"2026","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Peng, G., Wang, K., Ma, J., Cui, B., and Wang, D. (2025). AGRI-YOLO: A Lightweight Model for Corn Weed Detection with Enhanced YOLO v11n. Agriculture, 15.","DOI":"10.3390\/agriculture15181971"},{"key":"ref_25","first-page":"191","article-title":"Lightweight detection method for corn seedlings and weeds under natural environment based on improved YOLO11n","volume":"42","author":"Meng","year":"2026","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hu, J., Gong, H., Li, S., Mu, Y., Guo, Y., Sun, Y., Hu, T., and Bao, Y. (2024). Cotton Weed-YOLO: A Lightweight and Highly Accurate Cotton Weed Identification Model for Precision Agriculture. Agronomy, 14.","DOI":"10.3390\/agronomy14122911"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, S., and Wei, L. (2026). AVGS-YOLO: A Quad-Synergistic Lightweight Enhanced YOLOv11 Model for Accurate Cotton Weed Detection in Complex Field Environments. Agriculture, 16.","DOI":"10.3390\/agriculture16080828"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gautam, D., Mawardi, Z., Elliott, L., Loewensteiner, D., Whiteside, T., and Brooks, S. (2025). Detection of Invasive Species (Siam Weed) Using Drone-Based Imaging and YOLO Deep Learning Model. Remote Sens., 17.","DOI":"10.3390\/rs17010120"},{"key":"ref_29","first-page":"149","article-title":"Research on Lightweight BACD-YOLO Model for Corn Seedling and Weed Detection Based on Embedded YOLO","volume":"57","author":"Wang","year":"2026","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_30","first-page":"151","article-title":"Real-time detecting cotton seedlings under a lightweight film using PConvCGLU with a heavy parameter detection head","volume":"41","author":"Zhao","year":"2025","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_31","first-page":"182","article-title":"Beef Cattle Object Detection Method Under Occlusion Environment Based on Improved YOLOv12","volume":"7","author":"Liu","year":"2025","journal-title":"Smart Agric."},{"key":"ref_32","first-page":"78433","article-title":"Yolov12: Attention-centric real-time object detectors","volume":"38","author":"Tian","year":"2026","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, L., Chen, S., Wang, Y., Wu, B., and Dong, J. (2025). A real-time end-to-end detector for detecting surface defects on oversized rings. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0330031"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Nam, J.H., Syazwany, N.S., Kim, S.J., and Lee, S.C. (2024). Modality-Agnostic Domain Generalizable Medical Image Segmentation by Multi-frequency in Multi-Scale Attention\/\/2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.01091"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020). Efficientdet: Scalable and Efficient Object Detection\/\/2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, Z., Khan, Z., Shen, Y., and Liu, H. (2025). GTDR-YOLOv12: Optimizing YOLO for Efficient and Accurate Weed Detection in Agriculture. Agronomy, 15.","DOI":"10.3390\/agronomy15081824"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, K., Hu, X., Zheng, H., Lan, M., Liu, C., Liu, Y., Zhong, L., Li, H., and Tan, S. (2024). Weed detection and recognition in complex wheat fields based on an improved YOLOv7. Front. Plant Sci., 15.","DOI":"10.3389\/fpls.2024.1372237"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"101472","DOI":"10.1016\/j.atech.2025.101472","article-title":"CWRepViT-Net: An Encoder-Decoder Deep Learning Framework with RepViT Blocks for Crop Weed Semantic Segmentation in Soybean Fields thorough their Life Journey","volume":"12","author":"Gomroki","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Feng, P., Nie, Z., and Li, G. (2025). Field-deployable lightweight YOLOv8n for real-time detection and counting of Maize seedlings using UAV RGB imagery. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1639533"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1038\/s41598-024-84748-8","article-title":"A lightweight weed detection model for cotton fields based on an improved YOLOv8n","volume":"15","author":"Wang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zeng, F., Diao, H., Zhu, J., Ji, D., Liao, X., and Zhao, Z. (2024). YOLOv8 Model for Weed Detection in Wheat Fields Based on a Visual Converter and Multi-Scale Feature Fusion. Sensors, 24.","DOI":"10.3390\/s24134379"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hua, J., He, R., Zeng, Y., and Chen, Q. (2025). HDMS-YOLO: A multi-scale weed detection model for complex farmland environments. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1696392"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"0031","DOI":"10.34133\/plantphenomics.0031","article-title":"WRA-Net: Wide Receptive Field Attention Network for Motion Deblurring in Crop and Weed Image","volume":"5","author":"Yun","year":"2023","journal-title":"Plant Phenomics"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wang, L., Li, C., and Li, H. (2024). FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean Fields. Agriculture, 14.","DOI":"10.3390\/agriculture14122357"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8295","DOI":"10.1002\/ps.70881","article-title":"ASVLB-Net: A lightweight network for multispectral weed segmentation with NDVI-guided adaptive fusion","volume":"82","author":"Dong","year":"2026","journal-title":"Pest Manag. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Rosle, R., Che\u2019Ya, N.N., Rahmat, F., Sulaiman, N.S., Zakaria, N.-I., Berahim, Z., Omar, M.H., and Ismail, M.R. (2025). Deep learning-based temporal change detection of broadleaved weed infestation in rice fields using UAV multispectral imagery. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1655391"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhong, M., Wei, L., and Mo, H. (2025). Cotton pest and disease diagnosis via YOLOv11-based deep learning and knowledge graphs: A real-time voice-enabled edge solution. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1671755"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Das, S., Upadhyay, A., and Sun, X. (2025). Technology-driven approaches to intelligent mechanical weed control: A systematic review for sustainable weed management. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1734507"}],"container-title":["Plants"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/18\/2876\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T11:54:43Z","timestamp":1789991683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/18\/2876"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,20]]},"references-count":49,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["plants15182876"],"URL":"https:\/\/doi.org\/10.3390\/plants15182876","relation":{},"ISSN":["2223-7747"],"issn-type":[{"value":"2223-7747","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,20]]}}}