{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:17:34Z","timestamp":1780053454290,"version":"3.54.0"},"reference-count":52,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T00:00:00Z","timestamp":1660262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for Central Non-profit Scientific Institution","award":["1610132021024"],"award-info":[{"award-number":["1610132021024"]}]},{"name":"Fundamental Research Funds for Central Non-profit Scientific Institution","award":["Y2022GH05"],"award-info":[{"award-number":["Y2022GH05"]}]},{"name":"Fundamental Research Funds for Central Non-profit Scientific Institution","award":["2017KCXTD015"],"award-info":[{"award-number":["2017KCXTD015"]}]},{"name":"Central Public-interest Scientific Institution Basal Research Fund","award":["1610132021024"],"award-info":[{"award-number":["1610132021024"]}]},{"name":"Central Public-interest Scientific Institution Basal Research Fund","award":["Y2022GH05"],"award-info":[{"award-number":["Y2022GH05"]}]},{"name":"Central Public-interest Scientific Institution Basal Research Fund","award":["2017KCXTD015"],"award-info":[{"award-number":["2017KCXTD015"]}]},{"name":"Shantou University Team Building Project for Innovative and Strong University","award":["1610132021024"],"award-info":[{"award-number":["1610132021024"]}]},{"name":"Shantou University Team Building Project for Innovative and Strong University","award":["Y2022GH05"],"award-info":[{"award-number":["Y2022GH05"]}]},{"name":"Shantou University Team Building Project for Innovative and Strong University","award":["2017KCXTD015"],"award-info":[{"award-number":["2017KCXTD015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rust is a common disease in wheat that significantly impacts its growth and yield. Stem rust and leaf rust of wheat are difficult to distinguish, and manual detection is time-consuming. With the aim of improving this situation, this study proposes a method for identifying wheat rust based on ensemble learning (WR-EL). The WR-EL method extracts and integrates multiple convolutional neural network (CNN) models, namely VGG, ResNet 101, ResNet 152, DenseNet 169, and DenseNet 201, based on bagging, snapshot ensembling, and the stochastic gradient descent with warm restarts (SGDR) algorithm. The identification results of the WR-EL method were compared to those of five individual CNN models. The results show that the identification accuracy increases by 32%, 19%, 15%, 11%, and 8%. Additionally, we proposed the SGDR-S algorithm, which improved the f1 scores of healthy wheat, stem rust wheat and leaf rust wheat by 2%, 3% and 2% compared to the SGDR algorithm, respectively. This method can more accurately identify wheat rust disease and can be implemented as a timely prevention and control measure, which can not only prevent economic losses caused by the disease, but also improve the yield and quality of wheat.<\/jats:p>","DOI":"10.3390\/s22166047","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T23:44:03Z","timestamp":1660607043000},"page":"6047","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Image Classification of Wheat Rust Based on Ensemble Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Qian","family":"Pan","sequence":"first","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou 515063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9674-6020","authenticated-orcid":false,"given":"Maofang","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs\/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pingbo","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou 515063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwen","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou 515063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8639-591X","authenticated-orcid":false,"given":"Mohamed A. E.","family":"AbdelRahman","sequence":"additional","affiliation":[{"name":"Division of Environmental Studies and Land Use, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s12571-013-0263-y","article-title":"Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security","volume":"5","author":"Shiferaw","year":"2013","journal-title":"Food Secur."},{"key":"ref_2","unstructured":"El Solh, M., Nazari, K., Tadesse, W., and Wellings, C. (2012, January 1\u20134). The growing threat of stripe rust worldwide. Proceedings of the BGRI 2012 Technical Workshop, Beijing, China."},{"key":"ref_3","first-page":"1231","article-title":"Wheat rust research-then and now","volume":"86","author":"Bhardwaj","year":"2016","journal-title":"Indian J. Agric. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1111\/mpp.12618","article-title":"A review of wheat diseases-a field perspective","volume":"19","author":"Figueroa","year":"2018","journal-title":"Mol. Plant Pathol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"115004","DOI":"10.1088\/1748-9326\/ab4034","article-title":"An early warning system to predict and mitigate wheat rust diseases in Ethiopia","volume":"14","author":"Thurston","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.compag.2018.08.001","article-title":"Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review","volume":"153","author":"Patricio","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1094\/Phyto-84-940","article-title":"Use of mixtures of fluorescent pseudomonads to suppress take-all and improve the growth of wheat","volume":"84","author":"Pierson","year":"1994","journal-title":"Phytopathology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13593-014-0246-1","article-title":"Advanced methods of plant disease detection. A review","volume":"35","author":"Martinelli","year":"2015","journal-title":"Agron. Sustain. Dev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.biosystemseng.2016.01.017","article-title":"A review on the main challenges in automatic plant disease identification based on visible range images","volume":"144","author":"Barbedo","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1504\/IJCVR.2014.065566","article-title":"Detection and classification of fungal disease with Radon transform and support vector machine affected on cereals","volume":"4","author":"Pujari","year":"2014","journal-title":"Int. J. Comput. Vis. Robot."},{"key":"ref_11","unstructured":"Xiaoli, Z. (2014). Diagnosis for Main Disease of Winter Wheat Leaf Based on Image Recognition. [Master\u2019s Thesis, Henan Agricultural University]."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.compag.2017.04.013","article-title":"Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case","volume":"138","author":"Johannes","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1016\/j.procs.2017.03.177","article-title":"Automatic Wheat Leaf Rust Detection and Grading Diagnosis via Embedded Image Processing System","volume":"107","author":"Xu","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","unstructured":"Weiwei, H. (2017). Study on Feature Extraction and Recognition Medthod for Wheat Disease Image. [Master\u2019s Thesis, Anhui Agricultural University]."},{"key":"ref_15","first-page":"31","article-title":"Fast and accurate detection and classification of plant diseases","volume":"17","author":"Reyalat","year":"2011","journal-title":"Int. J. Comput. Appl."},{"key":"ref_16","first-page":"137","article-title":"A method of wheat disease identification based on convolutional neural network","volume":"50","author":"Hang","year":"2018","journal-title":"Shandong Agric. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Qiu, R.C., Yang, C., Moghimi, A., Zhang, M., Steffenson, B.J., and Hirsch, C.D. (2019). Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging. Remote Sens., 11.","DOI":"10.20944\/preprints201910.0056.v1"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.compag.2018.04.002","article-title":"Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild","volume":"161","author":"Picon","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","first-page":"174","article-title":"Image recognition of field wheat scab based on multi-way convolutional neural network","volume":"36","author":"Wenxia","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, D.Y., Wang, D.Y., Gu, C.Y., Jin, N., Zhao, H.T., Chen, G., Liang, H.Y., and Liang, D. (2019). Using Neural Network to Identify the Severity of Wheat Fusarium Head Blight in the Field Environment. Remote Sens., 11.","DOI":"10.3390\/rs11202375"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ros, A.S., and Doshi-Velez, F. (2018, January 2\u20137). Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. Proceedings of the AAAI\u201918: AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11504"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tran, T.T., Choi, J.W., Le, T., and Kim, J.W. (2019). A Comparative Study of Deep CNN in Forecasting and Classifying the Macronutrient Deficiencies on Development of Tomato Plant. Appl. Sci., 9.","DOI":"10.3390\/app9081601"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108145","DOI":"10.1016\/j.compeleceng.2022.108145","article-title":"Design of an integrated learning approach to assist real-time deaf application using voice recognition system","volume":"102","author":"Prasath","year":"2022","journal-title":"Comput. Electron. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Genaev, M., Ekaterina, S., and Afonnikov, D. (2020, January 6\u201310). Application of neural networks to image recognition of wheat rust diseases. Proceedings of the 2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB), Novosibirsk, Russia.","DOI":"10.1109\/CSGB51356.2020.9214703"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sood, S., and Singh, H. (2020, January 3\u20135). An implementation and analysis of deep learning models for the detection of wheat rust disease. Proceedings of the 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India.","DOI":"10.1109\/ICISS49785.2020.9316123"},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_30","unstructured":"Huang, G., Li, Y., Pleiss, G., Liu, Z., Hopcroft, J.E., and Weinberger, K.Q. (2017). Snapshot Ensembles: Train 1, get M for free. arXiv."},{"key":"ref_31","unstructured":"Loshchilov, I., and Hutter, F. (2017). SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1049\/el.2020.1951","article-title":"BitMix: Data augmentation for image steganalysis","volume":"56","author":"Yu","year":"2020","journal-title":"Electron. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"113588","DOI":"10.1016\/j.eswa.2020.113588","article-title":"Grape detection with convolutional neural networks","volume":"159","author":"Cecotti","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pawara, P., Okafor, E., Schomaker, L., and Wiering, M. (2017, January 18\u201321). Data augmentation for plant classification. Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, Antwerp, Belgium.","DOI":"10.1007\/978-3-319-70353-4_52"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.jocs.2018.12.003","article-title":"Multi-grade brain tumor classification using deep CNN with extensive data augmentation","volume":"30","author":"Sajjad","year":"2019","journal-title":"J. Comput. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/s40537-019-0276-2","article-title":"Deep convolutional neural network based medical image classification for disease diagnosis","volume":"6","author":"Yadav","year":"2019","journal-title":"J. Big Data"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s41066-019-00158-6","article-title":"Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition","volume":"5","author":"Zhao","year":"2020","journal-title":"Granul. Comput."},{"key":"ref_38","unstructured":"Jia, S.J., Wang, P., Jia, P.Y., and Hu, S.P. (2017, January 20\u201322). Research on Data Augmentation for Image Classification Based on Convolution Neural Networks. Proceedings of the 2017 Chinese Automation Congress (CAC), Jinan, China."},{"key":"ref_39","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network In Network. arXiv."},{"key":"ref_40","first-page":"5830766","article-title":"Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things","volume":"2022","author":"Mabrouk","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hekal, A.A., Moustafa, H., and Elnakib, A. (2022). Ensemble deep learning system for early breast cancer detection. Evol. Intell.","DOI":"10.1007\/s12065-022-00719-w"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10107-019-01363-6","article-title":"Gradient descent with random initialization: Fast global convergence for nonconvex phase retrieval","volume":"176","author":"Chen","year":"2019","journal-title":"Math. Program."},{"key":"ref_43","unstructured":"Lee, J.D., Simchowitz, M., Jordan, M.I., and Recht, B. (2016). Gradient Descent Converges to Minimizers. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2012). Stochastic Gradient Descent Tricks. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1023\/A:1017938623079","article-title":"Relative loss bounds for multidimensional regression problems","volume":"45","author":"Kivinen","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s10472-017-9564-8","article-title":"A Bayesian interpretation of the confusion matrix","volume":"81","author":"Caelen","year":"2017","journal-title":"Ann. Math. Artif. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"117407","DOI":"10.1016\/j.eswa.2022.117407","article-title":"Automated accurate fire detection system using ensemble pretrained residual network","volume":"203","author":"Dogan","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","article-title":"A comparative study of fine-tuning deep learning models for plant disease identification","volume":"161","author":"Too","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","first-page":"324","article-title":"Facial Expression Classification Based on Ensemble Convolutional Neural Network","volume":"57","author":"Zhou","year":"2020","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1109\/LGRS.2018.2880136","article-title":"Deep Network Ensembles for Aerial Scene Classification","volume":"16","author":"Dede","year":"2019","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_51","first-page":"4151","article-title":"The Marginal Value of Adaptive Gradient Methods in Machine Learning","volume":"30","author":"Wilson","year":"2017","journal-title":"Adv. Neural Inf. Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1007\/s13042-020-01228-x","article-title":"Class-weighted neural network for monotonic imbalanced classification","volume":"12","author":"Zhu","year":"2021","journal-title":"Int. J. Mach. Learn. Cybern."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6047\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:08:07Z","timestamp":1760141287000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6047"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,12]]},"references-count":52,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22166047"],"URL":"https:\/\/doi.org\/10.3390\/s22166047","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,12]]}}}