{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:56:08Z","timestamp":1773806168717,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,13]],"date-time":"2023-08-13T00:00:00Z","timestamp":1691884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Plan Project of Tianjin","award":["18ZXYENC00100"],"award-info":[{"award-number":["18ZXYENC00100"]}]},{"name":"Science and Technology Plan Project of Tianjin","award":["22ZYCGSN00190"],"award-info":[{"award-number":["22ZYCGSN00190"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Convolutional Neural Network (CNN) is one of the widely used deep learning models that offers the chance to boost farming productivity through autonomous inference of field conditions. In this paper, CNN is connected to a Support Vector Machine (SVM) to form a new model CNN-SVM; the CNN models chosen are ResNet-50 and VGG16 and the CNN-SVM models formed are ResNet-50-SVM and VGG16-SVM. The method consists of two parts: ResNet-50 and VGG16 for feature extraction and SVM for classification. This paper uses the public multi-class weeds dataset DeepWeeds for training and testing. The proposed ResNet-50-SVM and VGG16-SVM approaches achieved 97.6% and 95.9% recognition accuracies on the DeepWeeds dataset, respectively. The state-of-the-art networks (VGG16, ResNet-50, GoogLeNet, Densenet-121, and PSO-CNN) with the same dataset are accurate at 93.2%, 96.1%, 93.6%, 94.3%, and 96.9%, respectively. In comparison, the accuracy of the proposed methods has been improved by 1.5% and 2.7%, respectively. The proposed ResNet-50-SVM and the VGG16-SVM weed classification approaches are effective and can achieve high recognition accuracy.<\/jats:p>","DOI":"10.3390\/s23167153","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T11:07:10Z","timestamp":1692011230000},"page":"7153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier"],"prefix":"10.3390","volume":"23","author":[{"given":"Yanjuan","family":"Wu","sequence":"first","affiliation":[{"name":"Tianjin Key Laboratory of Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7304-8900","authenticated-orcid":false,"given":"Yuzhe","family":"He","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China"}]},{"given":"Yunliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.compag.2018.12.048","article-title":"Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence","volume":"157","author":"Partel","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8720","DOI":"10.1021\/cr500077e","article-title":"Electrochemically Assisted Remediation of Pesticides in Soils and Water: A Review","volume":"114","author":"Rodrigo","year":"2014","journal-title":"Chem. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3310","DOI":"10.1109\/LRA.2023.3262417","article-title":"Towards Domain Generalization in Crop and Weed Segmentation for Precision Farming Robots","volume":"8","author":"Weyler","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1002\/ps.3647","article-title":"Using genetically modified tomato crop plants with purple leaves for absolute weed\/crop classification","volume":"70","author":"Lati","year":"2014","journal-title":"Pest Manag. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.compag.2017.07.028","article-title":"Maize and weed classification using color indices with support vector data description in outdoor fields","volume":"141","author":"Zheng","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2017.02.002","article-title":"Weed segmentation using texture features extracted from wavelet sub-images","volume":"157","author":"Bakhshipour","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.compag.2017.12.032","article-title":"Evaluation of support vector machine and artificial neural networks in weed detection using shape features","volume":"145","author":"Bakhshipour","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.compag.2015.08.023","article-title":"Exploiting affine invariant regions and leaf edge shapes for weed detection","volume":"118","author":"Kazmi","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Che\u2019Ya, N.N., Dunwoody, E., and Gupta, M. (2021). Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery. Agronomy, 11.","DOI":"10.3390\/agronomy11071435"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"611622","DOI":"10.3389\/fpls.2020.611622","article-title":"Identification of Weeds Based on Hyperspectral Imaging and Machine Learning","volume":"11","author":"Li","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"105590","DOI":"10.1016\/j.compag.2020.105590","article-title":"Weed density classification in rice crop using computer vision","volume":"175","author":"Ashraf","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wu, Z., Zhao, B., Fan, C., and Shi, S. (2020). Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine. Sensors, 21.","DOI":"10.3390\/s21010212"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Islam, N., Rashid, M.M., Wibowo, S., Xu, C.Y., Morshed, A., Wasimi, S.A., Moore, S., and Rahman, S.M. (2021). Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm. Agriculture, 11.","DOI":"10.3390\/agriculture11050387"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.biosystemseng.2020.03.022","article-title":"Real-time robotic weed knife control system for tomato and lettuce based on geometric appearance of plant labels","volume":"194","author":"Raja","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.biosystemseng.2020.02.002","article-title":"Real-time weed-crop classification and localisation technique for robotic weed control in lettuce","volume":"192","author":"Raja","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9567","DOI":"10.1007\/s11042-017-5337-y","article-title":"Image-based recognition framework for robotic weed control systems","volume":"77","author":"Kounalakis","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"481","DOI":"10.5589\/m14-001","article-title":"Weed and crop discrimination using hyperspectral image data and reduced bandsets","volume":"39","author":"Eddy","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.asoc.2015.08.027","article-title":"A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method","volume":"37","author":"Pena","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.compag.2017.01.001","article-title":"Weed identification based on K-means feature learning combined with convolutional neural network","volume":"135","author":"Tang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1017\/S2040470017000206","article-title":"RoboWeedSupport\u2014Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network","volume":"8","author":"Dyrmann","year":"2017","journal-title":"Adv. Anim. Biosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1038\/s41598-018-38343-3","article-title":"DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning","volume":"9","author":"Olsen","year":"2019","journal-title":"Sci. Rep."},{"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":"Freitas","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","unstructured":"Mortensen, A.K., Dyrmann, M., Karstoft, H., J\u00f8rgensen, R.N., and Gislum, R. (2016, January 26\u201329). Semantic Segmentation of Mixed Crops using Deep Convolutional Neural Network. Proceedings of the CIGR-AgEng Conference, Aarhus, Denmark."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"837","DOI":"10.32604\/csse.2022.023016","article-title":"CNN Based Automated Weed Detection System Using UAV Imagery","volume":"42","author":"Haq","year":"2022","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"913","DOI":"10.32604\/csse.2023.025434","article-title":"Weed Classification Using Particle Swarm Optimization and Deep Learning Models","volume":"44","author":"Manikandakumar","year":"2023","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_29","first-page":"100759","article-title":"Deep learning-based precision agriculture through weed recognition in sugar beet fields","volume":"35","author":"Nasiri","year":"2022","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.engappai.2018.04.024","article-title":"Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification","volume":"72","author":"Vogado","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"110358","DOI":"10.1109\/ACCESS.2019.2933670","article-title":"Ensemble Learners of Multiple Deep CNNs for Pulmonary Nodules Classification Using CT Images","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Devnath, L., Fan, Z., Luo, S., Summons, P., and Wang, D. (2022). Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph191811193"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"034501","DOI":"10.1117\/1.JMI.3.3.034501","article-title":"Digital mammographic tumor classification using transfer learning from deep convolutional neural networks","volume":"3","author":"Huynh","year":"2016","journal-title":"J. Med. Imaging"},{"key":"ref_34","unstructured":"Arzhaeva, Y., Wang, D., Devnath, L., Amirgholipour, S.K., McBean, R., Hillhouse, J., Luo, S., Meredith, D., and Newbigin, K. (2023, August 10). Development of Automated Diagnostic Tools for Pneumoconiosis Detection from Chest X-ray Radiographs. Available online: https:\/\/www.coalservices.com.au\/wp-content\/uploads\/2017\/11\/Project-No.-20647-Final-Report.pdf."},{"key":"ref_35","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). PyTorch: An imperative style, high-performance deep learning library. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104958","DOI":"10.1016\/j.compag.2019.104958","article-title":"Cattle segmentation and contour extraction based on Mask R-CNN for precision livestock farming","volume":"165","author":"Qiao","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA."},{"key":"ref_38","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, H., Qi, F., and Wang, S. (2005, January 9\u201312). A Comparison of Model Selection Methods for Multi-class Support Vector Machines. Proceedings of the Computational Science and Its Applications\u2014ICCSA 2005, Singapore.","DOI":"10.1007\/11424925_119"},{"key":"ref_40","unstructured":"Osuna, E.E. (1998). Support Vector Machines: Training and Applications. [Ph.D. Thesis, Massachusetts Institute of Technology]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7153\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:32:48Z","timestamp":1760128368000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7153"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,13]]},"references-count":40,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167153"],"URL":"https:\/\/doi.org\/10.3390\/s23167153","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,13]]}}}