{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:04:17Z","timestamp":1783184657178,"version":"3.54.6"},"reference-count":38,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Provincial Science &amp; Technology Department under Grant","award":["2023NSFSC1985"],"award-info":[{"award-number":["2023NSFSC1985"]}]},{"name":"Sichuan Provincial Science &amp; Technology Department under Grant","award":["2023YFG0046"],"award-info":[{"award-number":["2023YFG0046"]}]},{"name":"Sichuan Provincial Science &amp; Technology Department under Grant","award":["2021YFG0133"],"award-info":[{"award-number":["2021YFG0133"]}]},{"name":"Sichuan Provincial Science &amp; Technology Department under Grant","award":["KYTZ202148"],"award-info":[{"award-number":["KYTZ202148"]}]},{"name":"Sichuan Provincial Science &amp; Technology Department under Grant","award":["KYTZ202149"],"award-info":[{"award-number":["KYTZ202149"]}]},{"name":"Sichuan Provincial Science &amp; Technology Department under Grant","award":["JQZN2022-001"],"award-info":[{"award-number":["JQZN2022-001"]}]},{"name":"Sichuan Provincial Science &amp; Technology Department under Grant","award":["220900882063927"],"award-info":[{"award-number":["220900882063927"]}]},{"name":"Sichuan Provincial Science &amp; Technology Department under Grant","award":["220900882293657"],"award-info":[{"award-number":["220900882293657"]}]},{"name":"Sichuan Provincial Science &amp; Technology Department under Grant","award":["JXZRZH202304"],"award-info":[{"award-number":["JXZRZH202304"]}]},{"name":"Sichuan Provincial Science &amp; Technology Department under Grant","award":["DSWL23-36"],"award-info":[{"award-number":["DSWL23-36"]}]},{"name":"Sichuan Provincial Science &amp; Technology Department under Grant","award":["DSWL23-35"],"award-info":[{"award-number":["DSWL23-35"]}]},{"name":"School Project of Chengdu University of Information Technology","award":["2023NSFSC1985"],"award-info":[{"award-number":["2023NSFSC1985"]}]},{"name":"School Project of Chengdu University of Information Technology","award":["2023YFG0046"],"award-info":[{"award-number":["2023YFG0046"]}]},{"name":"School Project of Chengdu University of Information Technology","award":["2021YFG0133"],"award-info":[{"award-number":["2021YFG0133"]}]},{"name":"School Project of Chengdu University of Information Technology","award":["KYTZ202148"],"award-info":[{"award-number":["KYTZ202148"]}]},{"name":"School Project of Chengdu University of Information Technology","award":["KYTZ202149"],"award-info":[{"award-number":["KYTZ202149"]}]},{"name":"School Project of Chengdu University of Information Technology","award":["JQZN2022-001"],"award-info":[{"award-number":["JQZN2022-001"]}]},{"name":"School Project of Chengdu University of Information Technology","award":["220900882063927"],"award-info":[{"award-number":["220900882063927"]}]},{"name":"School Project of Chengdu University of Information Technology","award":["220900882293657"],"award-info":[{"award-number":["220900882293657"]}]},{"name":"School Project of Chengdu University of Information Technology","award":["JXZRZH202304"],"award-info":[{"award-number":["JXZRZH202304"]}]},{"name":"School Project of Chengdu University of Information Technology","award":["DSWL23-36"],"award-info":[{"award-number":["DSWL23-36"]}]},{"name":"School Project of Chengdu University of Information Technology","award":["DSWL23-35"],"award-info":[{"award-number":["DSWL23-35"]}]},{"name":"International Joint Research Center of Robots and Intelligence Program","award":["2023NSFSC1985"],"award-info":[{"award-number":["2023NSFSC1985"]}]},{"name":"International Joint Research Center of Robots and Intelligence Program","award":["2023YFG0046"],"award-info":[{"award-number":["2023YFG0046"]}]},{"name":"International Joint Research Center of Robots and Intelligence Program","award":["2021YFG0133"],"award-info":[{"award-number":["2021YFG0133"]}]},{"name":"International Joint Research Center of Robots and Intelligence Program","award":["KYTZ202148"],"award-info":[{"award-number":["KYTZ202148"]}]},{"name":"International Joint Research Center of Robots and Intelligence Program","award":["KYTZ202149"],"award-info":[{"award-number":["KYTZ202149"]}]},{"name":"International Joint Research Center of Robots and Intelligence Program","award":["JQZN2022-001"],"award-info":[{"award-number":["JQZN2022-001"]}]},{"name":"International Joint Research Center of Robots and Intelligence Program","award":["220900882063927"],"award-info":[{"award-number":["220900882063927"]}]},{"name":"International Joint Research Center of Robots and Intelligence Program","award":["220900882293657"],"award-info":[{"award-number":["220900882293657"]}]},{"name":"International Joint Research Center of Robots and Intelligence Program","award":["JXZRZH202304"],"award-info":[{"award-number":["JXZRZH202304"]}]},{"name":"International Joint Research Center of Robots and Intelligence Program","award":["DSWL23-36"],"award-info":[{"award-number":["DSWL23-36"]}]},{"name":"International Joint Research Center of Robots and Intelligence Program","award":["DSWL23-35"],"award-info":[{"award-number":["DSWL23-35"]}]},{"name":"Ministry of Education industry-school cooperative education project","award":["2023NSFSC1985"],"award-info":[{"award-number":["2023NSFSC1985"]}]},{"name":"Ministry of Education industry-school cooperative education project","award":["2023YFG0046"],"award-info":[{"award-number":["2023YFG0046"]}]},{"name":"Ministry of Education industry-school cooperative education project","award":["2021YFG0133"],"award-info":[{"award-number":["2021YFG0133"]}]},{"name":"Ministry of Education industry-school cooperative education project","award":["KYTZ202148"],"award-info":[{"award-number":["KYTZ202148"]}]},{"name":"Ministry of Education industry-school cooperative education project","award":["KYTZ202149"],"award-info":[{"award-number":["KYTZ202149"]}]},{"name":"Ministry of Education industry-school cooperative education project","award":["JQZN2022-001"],"award-info":[{"award-number":["JQZN2022-001"]}]},{"name":"Ministry of Education industry-school cooperative education project","award":["220900882063927"],"award-info":[{"award-number":["220900882063927"]}]},{"name":"Ministry of Education industry-school cooperative education project","award":["220900882293657"],"award-info":[{"award-number":["220900882293657"]}]},{"name":"Ministry of Education industry-school cooperative education project","award":["JXZRZH202304"],"award-info":[{"award-number":["JXZRZH202304"]}]},{"name":"Ministry of Education industry-school cooperative education project","award":["DSWL23-36"],"award-info":[{"award-number":["DSWL23-36"]}]},{"name":"Ministry of Education industry-school cooperative education project","award":["DSWL23-35"],"award-info":[{"award-number":["DSWL23-35"]}]},{"name":"Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","award":["2023NSFSC1985"],"award-info":[{"award-number":["2023NSFSC1985"]}]},{"name":"Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","award":["2023YFG0046"],"award-info":[{"award-number":["2023YFG0046"]}]},{"name":"Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","award":["2021YFG0133"],"award-info":[{"award-number":["2021YFG0133"]}]},{"name":"Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","award":["KYTZ202148"],"award-info":[{"award-number":["KYTZ202148"]}]},{"name":"Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","award":["KYTZ202149"],"award-info":[{"award-number":["KYTZ202149"]}]},{"name":"Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","award":["JQZN2022-001"],"award-info":[{"award-number":["JQZN2022-001"]}]},{"name":"Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","award":["220900882063927"],"award-info":[{"award-number":["220900882063927"]}]},{"name":"Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","award":["220900882293657"],"award-info":[{"award-number":["220900882293657"]}]},{"name":"Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","award":["JXZRZH202304"],"award-info":[{"award-number":["JXZRZH202304"]}]},{"name":"Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","award":["DSWL23-36"],"award-info":[{"award-number":["DSWL23-36"]}]},{"name":"Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province (Jiangxi Normal University)","award":["DSWL23-35"],"award-info":[{"award-number":["DSWL23-35"]}]},{"name":"Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics","award":["2023NSFSC1985"],"award-info":[{"award-number":["2023NSFSC1985"]}]},{"name":"Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics","award":["2023YFG0046"],"award-info":[{"award-number":["2023YFG0046"]}]},{"name":"Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics","award":["2021YFG0133"],"award-info":[{"award-number":["2021YFG0133"]}]},{"name":"Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics","award":["KYTZ202148"],"award-info":[{"award-number":["KYTZ202148"]}]},{"name":"Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics","award":["KYTZ202149"],"award-info":[{"award-number":["KYTZ202149"]}]},{"name":"Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics","award":["JQZN2022-001"],"award-info":[{"award-number":["JQZN2022-001"]}]},{"name":"Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics","award":["220900882063927"],"award-info":[{"award-number":["220900882063927"]}]},{"name":"Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics","award":["220900882293657"],"award-info":[{"award-number":["220900882293657"]}]},{"name":"Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics","award":["JXZRZH202304"],"award-info":[{"award-number":["JXZRZH202304"]}]},{"name":"Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics","award":["DSWL23-36"],"award-info":[{"award-number":["DSWL23-36"]}]},{"name":"Opening Fund of Sichuan Research Center of Electronic Commerce and Modern Logistics","award":["DSWL23-35"],"award-info":[{"award-number":["DSWL23-35"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the development of intelligent substations, inspection robots are widely used to ensure the safe and stable operation of substations. Due to the prevalence of grass around the substation in the external environment, the inspection robot will be affected by grass when performing the inspection task, which can easily lead to the interruption of the inspection task. At present, inspection robots based on LiDAR sensors regard grass as hard obstacles such as stones, resulting in interruption of inspection tasks and decreased inspection efficiency. Moreover, there are inaccurate multiple object-detection boxes in grass recognition. To address these issues, this paper proposes a new assistance navigation method for substation inspection robots to cross grass areas safely. First, an assistant navigation algorithm is designed to enable the substation inspection robot to recognize grass and to cross the grass obstacles on the route of movement to continue the inspection work. Second, a three-layer convolutional structure of the Faster-RCNN network in the assistant navigation algorithm is improved instead of the original full connection structure for optimizing the object-detection boxes. Finally, compared with several Faster-RCNN networks with different convolutional kernel dimensions, the experimental results show that at the convolutional kernel dimension of 1024, the proposed method in this paper improves the mAP by 4.13% and the mAP is 91.25% at IoU threshold 0.5 in the range of IoU thresholds from 0.5 to 0.9 with respect to the basic network. In addition, the assistant navigation algorithm designed in this paper fuses the ultrasonic radar signals with the object recognition results and then performs the safety judgment to make the inspection robot safely cross the grass area, which improves the inspection efficiency.<\/jats:p>","DOI":"10.3390\/s23229201","type":"journal-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T08:19:43Z","timestamp":1700122783000},"page":"9201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A New Assistance Navigation Method for Substation Inspection Robots to Safely Cross Grass Areas"],"prefix":"10.3390","volume":"23","author":[{"given":"Qiang","family":"Yang","sequence":"first","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Key Laboratory of Natural Disaster Monitoring & Early Warning and Assessment of Jiangxi Province, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8034-0977","authenticated-orcid":false,"given":"Gexiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyi","family":"Xian","sequence":"additional","affiliation":[{"name":"Chengdu HitoAI Automatic Control Technology Co., Ltd., Chengdu 610000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijia","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongyu","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xihua University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"431","DOI":"10.25046\/aj040452","article-title":"Effectiveness and comparison of digital substations over conventional substations","volume":"4","author":"Waleed","year":"2019","journal-title":"Adv. Sci. Technol. Eng. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"125540","DOI":"10.1109\/ACCESS.2021.3104731","article-title":"Detection and location of safety protective wear in power substation operation using wear-enhanced YOLOv3 Algorithm","volume":"9","author":"Zhao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zeynal, H., Eidiani, M., and Yazdanpanah, D. (2014, January 20\u201323). Intelligent Substation Automation Systems for robust operation of smart grids. Proceedings of the 2014 IEEE Innovative Smart Grid Technologies\u2014Asia, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ISGT-Asia.2014.6873893"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, P., Li, C., Yang, Q., Fu, L., Yu, F., Min, L., Guo, D., and Li, X. (2022). Environment Understanding Algorithm for Substation Inspection Robot Based on Improved DeepLab V3+. J. Imaging, 8.","DOI":"10.3390\/jimaging8100257"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1049\/iet-gtd.2017.0096","article-title":"Real-time condition monitoring of substation equipment using thermal cameras","volume":"12","author":"Pal","year":"2018","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1109\/TPWRD.2016.2598572","article-title":"Smart Substation: State of the Art and Future Development","volume":"32","author":"Huang","year":"2017","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1109\/JAS.2017.7510364","article-title":"Mobile Robot for Power Substation Inspection: A Survey","volume":"4","author":"Lu","year":"2017","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lu, P., Sun, W., An, C., Fu, Q., Long, C., and Li, W. (2021, January 26\u201328). Slam and Navigation of Electric Power Intelligent Inspection Robot based on ROS. Proceedings of the 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence, Nanchang, China.","DOI":"10.1109\/ICIBA52610.2021.9687883"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, Y., Li, H., Hao, H., Chen, W., and Zhan, W. (2022). The Navigation System of a Logistics Inspection Robot Based on Multi-Sensor Fusion in a Complex Storage Environment. Sensors, 22.","DOI":"10.3390\/s22207794"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.compag.2016.11.021","article-title":"Automatic crop detection under field conditions using the HSV colour space and morphological operations","volume":"133","author":"Hamuda","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107809","DOI":"10.1016\/j.patcog.2020.107809","article-title":"Plant leaf recognition by integrating shape and texture features","volume":"112","author":"Yang","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105520","DOI":"10.1016\/j.compag.2020.105520","article-title":"Graph weeds net: A graph-based deep learning method for weed recognition","volume":"174","author":"Hu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","first-page":"628","article-title":"Weed recognition using deep learning techniques on class-imbalanced imagery","volume":"74","author":"Sohel","year":"2023","journal-title":"Crop Pasture Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"669","DOI":"10.32604\/iasc.2021.015225","article-title":"Weed recognition for depthwise separable network based on transfer learning","volume":"27","author":"Xu","year":"2021","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ding, H., Junling, W., Jing, W., ZiYin, M., Xu, Z., and DePeng, D. (2019, January 3\u20135). Study on Identification for the Typical Pasture Based on Image Texture Features. Proceedings of the 2019 Chinese Control and Decision Conference (CCDC), Nanchang, China.","DOI":"10.1109\/CCDC.2019.8832691"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dinc, S., and Parra, L.A.C. (2021, January 15\u201317). A three layer spatial-spectral hyperspectral image classification model using guided median filters. Proceedings of the 2021 ACMSE Conference\u2014ACMSE 2021: The Annual ACM Southeast Conference, Virtual.","DOI":"10.1145\/3409334.3452045"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105684","DOI":"10.1016\/j.compag.2020.105684","article-title":"Edge detection for weed recognition in lawns","volume":"176","author":"Parra","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kounalakis, T., Triantafyllidis, G.A., and Nalpantidis, L. (2016, January 4\u20136). Weed recognition framework for robotic precision farming. Proceedings of the IST 2016\u20142016 IEEE International Conference on Imaging Systems and Techniques, Chania, Greece.","DOI":"10.1109\/IST.2016.7738271"},{"key":"ref_19","unstructured":"Michaels, A., Haug, S., and Albert, A. (October, January 28). Vision-based high-speed manipulation for robotic ultra-precise weed control. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Hamburg, Germany."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_21","first-page":"355","article-title":"Multi-type feature fusion technique for weed identification in cotton fields","volume":"9","author":"Lin","year":"2016","journal-title":"Int. J. Signal Process. Image Process. Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107921","DOI":"10.1016\/j.compag.2023.107921","article-title":"AGHRNet: An attention ghost-HRNet for confirmation of catch-and-shake locations in jujube fruits vibration harvesting","volume":"210","author":"Zheng","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107049","DOI":"10.1016\/j.compag.2022.107049","article-title":"AFFU-Net: Attention feature fusion U-Net with hybrid loss for winter jujube crack detection","volume":"198","author":"Zheng","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","first-page":"437","article-title":"VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning","volume":"Volume 12966 LNCS","author":"Lian","year":"2021","journal-title":"Machine Learning in Medical Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.compag.2017.10.027","article-title":"Weed detection in soybean crops using ConvNets","volume":"143","author":"Pistori","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106067","DOI":"10.1016\/j.compag.2021.106067","article-title":"A survey of deep learning techniques for weed detection from images","volume":"184","author":"Hasan","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105450","DOI":"10.1016\/j.compag.2020.105450","article-title":"CNN feature based graph convolutional network for weed and crop recognition in smart farming","volume":"174","author":"Jiang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s13007-022-00869-z","article-title":"A hybrid CNN\u2013SVM classifier for weed recognition in winter rape field","volume":"18","author":"Tao","year":"2022","journal-title":"Plant Methods"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ayhan, B., and Kwan, C. (2020). Tree, Shrub, and Grass Classification Using Only RGB Images. Remote Sens., 12.","DOI":"10.3390\/rs12081333"},{"key":"ref_30","unstructured":"Olaniyi, O.M., Daniya, E., Abdullahi, I.M., Bala, J.A., and Olanrewaju, E.A. (2021). Artificial Intelligence and Industrial Applications: Smart Operation Management, Springer."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","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 Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_36","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS 2015), Montreal, QC, Canada."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Thanh Le, V.N., Truong, G., and Alameh, K. (2021, January 13\u201315). Detecting weeds from crops under complex field environments based on Faster RCNN. Proceedings of the 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), Phu Quoc Island, Vietnam.","DOI":"10.1109\/ICCE48956.2021.9352073"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chang, C.L., and Chung, S.C. (2020, January 20\u201322). Improved Deep Learning-based Approach for Real-time Plant Species Recognition on the Farm. Proceedings of the 2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Porto, Portugal.","DOI":"10.1109\/CSNDSP49049.2020.9249558"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9201\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:23:37Z","timestamp":1760131417000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"references-count":38,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23229201"],"URL":"https:\/\/doi.org\/10.3390\/s23229201","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,15]]}}}