{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T19:40:54Z","timestamp":1774726854080,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,7]],"date-time":"2019-03-07T00:00:00Z","timestamp":1551916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&amp;D Projects of Ningxia Hui Autonomous Region","award":["2017BY080"],"award-info":[{"award-number":["2017BY080"]}]},{"name":"National Natural Science Foundation of China (31860477) and the Open Fund of Key Laboratory of Integrated Management of Harmful Crop Vermin in China North-western Oasis\uff0cMinistry of Agriculture\uff0cP. R. China","award":["KFJJ20180108"],"award-info":[{"award-number":["KFJJ20180108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The rapid, recent development of image recognition technologies has led to the widespread use of convolutional neural networks (CNNs) in automated image classification and in the recognition of plant diseases. Aims: The aim of the present study was to develop a deep CNNs to identify tea plant disease types from leaf images. Materials: A CNNs model named LeafNet was developed with different sized feature extractor filters that automatically extract the features of tea plant diseases from images. DSIFT (dense scale-invariant feature transform) features are also extracted and used to construct a bag of visual words (BOVW) model that is then used to classify diseases via support vector machine(SVM) and multi-layer perceptron(MLP) classifiers. The performance of the three classifiers in disease recognition were then individually evaluated. Results: The LeafNet algorithm identified tea leaf diseases most accurately, with an average classification accuracy of 90.16%, while that of the SVM algorithm was 60.62% and that of the MLP algorithm was 70.77%. Conclusions: The LeafNet was clearly superior in the recognition of tea leaf diseases compared to the MLP and SVM algorithms. Consequently, the LeafNet can be used in future applications to improve the efficiency and accuracy of disease diagnoses in tea plants.<\/jats:p>","DOI":"10.3390\/sym11030343","type":"journal-article","created":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T04:58:35Z","timestamp":1552021115000},"page":"343","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":169,"title":["Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0521-9709","authenticated-orcid":false,"given":"Jing","family":"Chen","sequence":"first","affiliation":[{"name":"College of Plant Protection, China Agricultural University, Beijing 100193, China"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Agronomy, Xinjiang Agricultural University, Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests, Urumqi 830052, China"}]},{"given":"Lingwang","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Plant Protection, China Agricultural University, Beijing 100193, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, N., Yuan, M.F., Wang, P., Zhang, R.B., Sun, J., and Mao, H.P. (2019). Tea Diseases Detection Based on Fast Infrared Thermal Image Processing Technology. J. Sci. Food Agric.","DOI":"10.1002\/jsfa.9564"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/TASE.2017.2770170","article-title":"Computational Deep Intelligence Vision Sensing for Nutrient Content Estimation in Agricultural Automation","volume":"15","author":"Sulistyo","year":"2018","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/TII.2016.2628439","article-title":"Regularized Neural Networks Fusion and Genetic Algorithm Based On-Field Nitrogen Status Estimation of Wheat Plants","volume":"13","author":"Sulistyo","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/MIS.2018.111144506","article-title":"Building a Globally Optimized Computational Intelligent Image Processing Algorithm for On-Site Inference of Nitrogen in Plants","volume":"33","author":"Sulistyo","year":"2018","journal-title":"IEEE Intell. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.biosystemseng.2016.08.024","article-title":"Plant species classification using deepconvolutional neural network","volume":"151","author":"Dyrmann","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"267","DOI":"10.3923\/itj.2011.267.275","article-title":"Detection and Classification of Leaf Diseases using K-means-based Segmentation and Neural-networks-based Classification","volume":"10","author":"Bashish","year":"2011","journal-title":"Inf. Technol. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.compag.2010.06.009","article-title":"Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance","volume":"74","author":"Rumpf","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3289801","DOI":"10.1155\/2016\/3289801","article-title":"Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification","volume":"2016","author":"Sladojevic","year":"2016","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Abdullah, N.E., Rahim, A.A., Hashim, H., and Kamal, M.M. (2007, January 11\u201312). Classification of Rubber Tree Leaf Diseases Using Multilayer Perceptron Neural Network. Proceedings of the 5tn Student Conference on Research and Development-SCOReD, Shah Alam, Malaysia.","DOI":"10.1109\/SCORED.2007.4451369"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A Tutorial on Support Vector Machines for Pattern Recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_11","unstructured":"Vapnik, V. (1998). Statistical Learning Theory, John Wiley and Sons."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","article-title":"Deep learning models for plant disease detection and diagnosis","volume":"145","author":"Ferentinos","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","first-page":"65","article-title":"Color Transform Based Approach for Disease Spot Detection on Plant Leaf","volume":"3","author":"Chaudhary","year":"2012","journal-title":"Int. J. Comput. Sci. Telecommun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.compag.2016.01.008","article-title":"Detecting Bakanae disease in rice seedlings by machine vision","volume":"121","author":"Chung","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","first-page":"211","article-title":"Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features","volume":"15","author":"Arivazhagan","year":"2013","journal-title":"Agric. Eng. Int. CIGR J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"26647","DOI":"10.1007\/s11042-016-4191-7","article-title":"Soybean plant foliar disease detection using image retrieval approaches","volume":"76","author":"Shrivastava","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.compag.2006.01.004","article-title":"Identification of citrus disease using color texture features and discriminant analysis","volume":"52","author":"Pydipati","year":"2006","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.compag.2017.01.014","article-title":"Leaf image based cucumber disease recognition using sparse representation classification","volume":"134","author":"Zhang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","first-page":"60","article-title":"Feature extraction of diseased leaf images","volume":"3","author":"Patil","year":"2012","journal-title":"J. Signal Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.compag.2017.04.008","article-title":"Symptom based automated detection of citrus diseases using color histogram and textural descriptors","volume":"138","author":"Ali","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.compag.2016.04.032","article-title":"Local descriptors for soybean disease recognition","volume":"125","author":"Pires","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.compag.2017.06.016","article-title":"Fusion of superpixel, expectation maximization and PHOG for recognizing cucumber diseases","volume":"140","author":"Zhang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s11263-006-9794-4","article-title":"Local features and kernels for classification of texture and object categories: A comprehensive study","volume":"73","author":"Zhang","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_26","first-page":"27","article-title":"Tea Leaf Diseases Recognition using Neural Network Ensemble","volume":"114","author":"Karmokar","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, G., Ma, Z.H., and Li, X.L. (2012, January 29\u201331). Image recognition of plant diseases based on principal component analysis and neural networks. Proceedings of the 2012 8th International Conference on Natural Computation, Chongqing, China.","DOI":"10.1109\/ICNC.2012.6234701"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hossain, M.S., Mou, R.M., Hasan, M.M., Chakraborty, S., and Razzak, M.A. (2018, January 9\u201310). Recognition and Detection of Tea Leaf\u2019s Diseases Using Support Vector Machine. Proceedings of the 14th International Colloquium on Signal Processing & Its Applications (CSPA 2018), Penang, Malaysia.","DOI":"10.1109\/CSPA.2018.8368703"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yao, Q., Guan, Z.X., Zhou, Y.F., Tang, J., Hu, Y., and Yang, B.J. (2009, January 2\u20133). Application of Support Vector Machine for Detecting Rice Diseases Using Shape and Color Texture Features. Proceedings of the International Conference on Engineering Computation, Hong Kong, China.","DOI":"10.1109\/ICEC.2009.73"},{"key":"ref_30","unstructured":"Pichayoot, O. (2017, January 15\u201318). Corn Disease Identification from Leaf Images Using Convolutional Neural Networks. Proceedings of the 21st International Computer Science and Engineering Conference (ICSEC), Bangkok, Thailand."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.compag.2018.08.048","article-title":"A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network","volume":"154","author":"Ma","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, B., Zhang, Y., He, D.J., and Li, Y. (2018). Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks. Symmetry, 10.","DOI":"10.3390\/sym10010011"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"30370","DOI":"10.1109\/ACCESS.2018.2844405","article-title":"Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_34","unstructured":"Prajwala, T.M., Pranathi, A., Ashritha, K.S., Chittaragi, N.B., and Koolagudi, S.G. (2018, January 2\u20134). Tomato Leaf Disease Detection using Convolutional Neural Networks. Proceedings of the 2018 Eleventh International Conference on Contemporary Computing (IC3), Noida, India."},{"key":"ref_35","first-page":"13","article-title":"Diseases and Pests of Tea: Overview and Possibilities of Integrated Pest and Disease Management","volume":"101","year":"2000","journal-title":"J. Agric. Rural Dev. Trop. Subtrop."},{"key":"ref_36","first-page":"1","article-title":"Identification Guide for Diseases of Tea (Camellia sinensis)","volume":"2006","author":"Keith","year":"2006","journal-title":"Plant Dis."},{"key":"ref_37","first-page":"2073076","article-title":"Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project","volume":"2016","author":"Cascio","year":"2016","journal-title":"Biomed Res. Int."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kim, P. (2017). MATLAB Deep Learning With Machine Learning, Neural Networks and Artificial Intelligence, Apress.","DOI":"10.1007\/978-1-4842-2845-6_1"},{"key":"ref_39","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","volume":"28","author":"Sutskever","year":"2013","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"20","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_41","first-page":"143","article-title":"A Comparison of SIFT, PCA-SIFT and SURF","volume":"3","author":"Juan","year":"2009","journal-title":"Int. J. Image Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1109\/TPAMI.2010.147","article-title":"SIFT Flow: Dense Correspondence across Scenes and its Applications","volume":"33","author":"Liu","year":"2011","journal-title":"Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sivic, J., and Zisserman, A. (2003, January 13\u201316). Video Google: A text retrieval approach to object matching in videos. Proceedings of the 9th IEEE International Conference on Computer Vision, Nice, France.","DOI":"10.1109\/ICCV.2003.1238663"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.jvcir.2016.05.022","article-title":"A novel method for image classification based on bag of visual words","volume":"40","author":"Wang","year":"2016","journal-title":"J. Vis. Commun. Image Represent."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/3\/343\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:37:02Z","timestamp":1760186222000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/3\/343"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,7]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["sym11030343"],"URL":"https:\/\/doi.org\/10.3390\/sym11030343","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,7]]}}}