{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T07:20:31Z","timestamp":1774596031461,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T00:00:00Z","timestamp":1569369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672035, 61472282 and 61872004"],"award-info":[{"award-number":["61672035, 61472282 and 61872004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Plant leaf diseases are closely related to people\u2019s daily life. Due to the wide variety of diseases, it is not only time-consuming and labor-intensive to identify and classify diseases by artificial eyes, but also easy to be misidentified with having a high error rate. Therefore, we proposed a deep learning-based method to identify and classify plant leaf diseases. The proposed method can take the advantages of the neural network to extract the characteristics of diseased parts, and thus to classify target disease areas. To address the issues of long training convergence time and too-large model parameters, the traditional convolutional neural network was improved by combining a structure of inception module, a squeeze-and-excitation (SE) module and a global pooling layer to identify diseases. Through the Inception structure, the feature data of the convolutional layer were fused in multi-scales to improve the accuracy on the leaf disease dataset. Finally, the global average pooling layer was used instead of the fully connected layer to reduce the number of model parameters. Compared with some traditional convolutional neural networks, our model yielded better performance and achieved an accuracy of 91.7% on the test data set. At the same time, the number of model parameters and training time have also been greatly reduced. The experimental classification on plant leaf diseases indicated that our method is feasible and effective.<\/jats:p>","DOI":"10.3390\/s19194161","type":"journal-article","created":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T03:06:51Z","timestamp":1569467211000},"page":"4161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":138,"title":["Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network"],"prefix":"10.3390","volume":"19","author":[{"given":"Jie","family":"Hang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China"}]},{"given":"Dexiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5810-8159","authenticated-orcid":false,"given":"Peng","family":"Chen","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China"},{"name":"School of Electrical and Information Engineering, Anhui University of Technology, Ma\u2019anshan 243032, China"}]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4945-7725","authenticated-orcid":false,"given":"Bing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Anhui University of Technology, Ma\u2019anshan 243032, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Es-Saady, Y., Massi, I.E., Yassa, M.E., Mammass, D., and Benazoun, A. (2016, January 2\u20134). Automatic Recognition of Plant Leaves Diseases Based on Serial Combination of Two SVM Classifiers. Proceedings of the 2nd International Conference on Electrical and Information Technologies, Xi\u2019an, China.","DOI":"10.1109\/EITech.2016.7519661"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10","DOI":"10.9790\/0661-16151016","article-title":"An Overview of the Research on Plant Leaves Disease Detection Using Image Processing Techniques","volume":"16","author":"Gavhale","year":"2014","journal-title":"IOSR J. Comput. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, G., Sun, Y., and Wang, J.X. (2017). Automatic Image Based Plant Disease Severity Estimation Using Deep Learning. Comput. Intell. Neurosci., 1\u20138.","DOI":"10.1155\/2017\/2917536"},{"key":"ref_4","first-page":"41","article-title":"The Method of Recognition of Damage by Disease and Insect Based on Laminae","volume":"6","author":"Tan","year":"2009","journal-title":"J. Agric. Mech. Res."},{"key":"ref_5","first-page":"175","article-title":"Method for Recognition of Grape Disease Based on Support Vector Machine","volume":"23","author":"Tian","year":"2007","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_6","first-page":"148","article-title":"Recognition of Cucumber Diseases Based on Leaf Image and Environmental Information","volume":"30","author":"Wang","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_7","first-page":"42","article-title":"Plant Disease Recognition Based on Plant Leaf Image","volume":"25","author":"Zhang","year":"2015","journal-title":"J. Anim. Plant Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1002\/rob.21730","article-title":"Human-robot collaborative site-specific sprayer","volume":"34","author":"Ron","year":"2017","journal-title":"J. Field Robot."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3390\/jimaging3010009","article-title":"3D Imaging with a Sonar Sensor and an Automated 3-Axes Frame for Selective Spraying in Controlled Conditions","volume":"3","author":"David","year":"2017","journal-title":"J. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.compag.2018.07.014","article-title":"Multi-level learning features for automatic classification of field crop insects","volume":"152","author":"Xie","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2010.02.007","article-title":"A review of advanced techniques for detecting plant diseases","volume":"72","author":"Sindhuja","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7024","DOI":"10.1038\/s41598-019-43171-0","article-title":"Automatic Localization and Count of Agricultural Crop Pests Based on an Improved Deep Learning Pipeline","volume":"9","author":"Li","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.neucom.2017.06.023","article-title":"Identification of Rice Diseases Using Deep Convolutional Neural Networks","volume":"267","author":"Yang","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xia, D., Chen, P., Wang, B., Zhang, J., and Xie, C. (2018). Insect detection and classification based on improved convolutional neural network. Sensors, 18.","DOI":"10.3390\/s18124169"},{"key":"ref_15","first-page":"209","article-title":"Identification of Leaf Diseases of Various Plants Based on Improved Convolutional Neural Network","volume":"19","author":"Sun","year":"2017","journal-title":"Agric. Eng. Newsp."},{"key":"ref_16","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":"Mark","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A Large Scale Hierarchical Image Database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2012","journal-title":"Int. J. Comput. Vis."},{"key":"ref_19","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2019, September 25). Imagenet Classification with Deep Convolutional Neural Networks. Available online: http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf."},{"key":"ref_20","unstructured":"Simonyan, K., Zisserman, A., Bengio, Y., and LeCun, Y. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201312). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2017). Squeeze-and-Excitation Networks. arXiv.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_24","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in network. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014). Caffe: Convolutional Architecture for Fast Feature Embedding, Cornell University.","DOI":"10.1145\/2647868.2654889"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4161\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:24:17Z","timestamp":1760189057000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4161"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,25]]},"references-count":26,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194161"],"URL":"https:\/\/doi.org\/10.3390\/s19194161","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,25]]}}}