{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:43:03Z","timestamp":1760236983292,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,2]],"date-time":"2020-02-02T00:00:00Z","timestamp":1580601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The innovation of germplasm resources and the continuous breeding of new varieties of apples (Malus domestica Borkh.) have yielded more than 8000 apple cultivars. The ability to identify apple cultivars with ease and accuracy can solve problems in apple breeding related to property rights protection to promote the healthy development of the global apple industry. However, the existing methods are inconsistent and time-consuming. This paper proposes an efficient and convenient method for the classification of apple cultivars using a deep convolutional neural network with leaf image input, which is the delicate symmetry of a human brain learning. The model was constructed using the TensorFlow framework and trained on a dataset of 12,435 leaf images for the identification of 14 apple cultivars. The proposed method achieved an overall accuracy of 0.9711 and could successfully avoid the over-fitting problem. Tests on an unknown independent testing set resulted in a mean accuracy, mean error, and variance of      \u03bc  a c c   = 0.9685    ,      \u03bc \u03b5  = 0.0315    , and      \u03c3 2  = 1.89025 E \u2212 4    , respectively, indicating that the generalization accuracy and stability of the model were very good. Finally, the classification performance for each cultivar was tested. The results show that model had an accuracy of 1.0000 for Ace, Hongrouyouxi, Jazz, and Honey Crisp cultivars, and only one leaf was incorrectly identified for 2001, Ada Red, Jonagold, and Gold Spur cultivars, with accuracies of 0.9787, 0.9800, 0.9773, and 0.9737, respectively. Jingning1 and Pinova cultivars were classified with the lowest accuracies, with 0.8780 and 0.8864, respectively. The results also show that the genetic relationship between cultivars Shoufu 3 and Yanfu 3 is very high, which is mainly because they were both selected from a red mutation of Fuji and bred in Yantai City, Shandong Province, China. Generally, this study indicates that the proposed deep learning model is a novel and improved solution for apple cultivar identification, with high generalization accuracy, stable convergence, and high specificity.<\/jats:p>","DOI":"10.3390\/sym12020217","type":"journal-article","created":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T01:25:51Z","timestamp":1580693151000},"page":"217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Novel Identification Method for Apple (Malus domestica Borkh.) Cultivars Based on a Deep Convolutional Neural Network with Leaf Image Input"],"prefix":"10.3390","volume":"12","author":[{"given":"Chengzhong","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Sciences and Technology, Gansu Agricultural University, No. 1, Yinmencun Road, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9405-279X","authenticated-orcid":false,"given":"Junying","family":"Han","sequence":"additional","affiliation":[{"name":"College of Information Sciences and Technology, Gansu Agricultural University, No. 1, Yinmencun Road, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6963-1279","authenticated-orcid":false,"given":"Baihong","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Horticulture, Gansu Agricultural University, No. 1, Yinmencun Road, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Mao","sequence":"additional","affiliation":[{"name":"College of Horticulture, Gansu Agricultural University, No. 1, Yinmencun Road, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengxu","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Information Sciences and Technology, Gansu Agricultural University, No. 1, Yinmencun Road, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunqiang","family":"Li","sequence":"additional","affiliation":[{"name":"Research Institute of Pomology of Jingning Country, Jingning 743400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,2]]},"reference":[{"key":"ref_1","first-page":"1217","article-title":"Genetic divergence in respect to qualitative traits and their possible use in precision breeding programme of apple (Malus Domestica)","volume":"83","author":"Srivastava","year":"2013","journal-title":"Indian J. Agric. Sci."},{"key":"ref_2","unstructured":"Peihua, C. (2015). Apple Varieties in China[M], China Agriculture Press."},{"key":"ref_3","unstructured":"Zhengyang, Z. (2015). Fruit Science and Practice in China. Apple[M], Shaanxi Science and Technology Press."},{"key":"ref_4","unstructured":"Sheth, K. (2019, December 25). 25 Countries That Import The Most Apples. WorldAtlas. Available online: https:\/\/www.worldatlas.com\/articles\/the-countries-with-the-most-apple-imports-in-the-world.html."},{"key":"ref_5","unstructured":"(2019, December 12). Sohu News. \u201cWorld Apple Production and Sales in 2018\/2019.\u201d. Available online: https:\/\/www.http:\/\/www.sohu.com\/a\/284046969_120045201."},{"key":"ref_6","first-page":"598","article-title":"Discussion on today\u2019s world apple industry trends and the suggestions on sustainable and efficient development of apple industry in China","volume":"27","author":"Chen","year":"2010","journal-title":"J. Fruit Sci."},{"key":"ref_7","first-page":"971","article-title":"Establishment of SSR fingerprinting database on major apple cultivars","volume":"29","author":"Lixin","year":"2012","journal-title":"J. Fruit Sci."},{"key":"ref_8","first-page":"305","article-title":"Rapid Identification of Apple Varieties Based on Hyperspectral Imaging","volume":"48","author":"Huiling","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_9","first-page":"123","article-title":"Genetic Relationship Analysis of Apple Cultivars Using SSR and SRAP Makers","volume":"39","author":"Ba","year":"2011","journal-title":"J. Northwest A F Univ. -Nat. Sci. Ed."},{"key":"ref_10","unstructured":"Hongjie, S. (2016). Identification of Grape Varieties Based on Leaves Image Analysis, Northwest A&F University."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Satapathy, S., Bhateja, V., and Das, S. (2019). Leaf Recognition and Classification Using Chebyshev Moments. Smart Intelligent Computing and Applications, Springer. Smart Innovation, Systems and Technologies.","DOI":"10.1007\/978-981-13-1927-3"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hall, D., McCool, C., Dayoub, F., Sunderhauf, N., and Upcroft, B. (2015, January 5\u20139). Evaluation of Features for Leaf Classification in Challenging Conditions. Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision, WACV, Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2015.111"},{"key":"ref_13","unstructured":"S\u00f6derkvist, O. (2001). Computer Vision Classification of Leaves from Swedish Trees. [Master\u2019s Thesis, Teknik Och Teknologier]."},{"key":"ref_14","first-page":"12","article-title":"Study on plant leaf classification based on image processing and SVM","volume":"5","author":"Lei","year":"2013","journal-title":"J. Agric. Mech. Res."},{"key":"ref_15","first-page":"162","article-title":"Orthogonal global-locally discriminant projection for plant leaf classification","volume":"26","author":"Zhang","year":"2010","journal-title":"Nongye Gongcheng Xuebao\/Trans. Chin. Soc. Agric. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.biosystemseng.2016.08.024","article-title":"Plant species classification using deep convolutional neural network","volume":"151","author":"Dyrmann","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yalcin, H., and Razavi, S. (2016, January 18\u201320). Plant classification using convolutional neural networks. Proceedings of the 5th International Conference on Agro-geoinformatics, Tianjin, China.","DOI":"10.1109\/Agro-Geoinformatics.2016.7577698"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lee, S.H., Chan, C.S., and Remagnino, P. (2018). Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks. IEEE Trans. Image Process.","DOI":"10.1109\/TIP.2018.2836321"},{"key":"ref_19","unstructured":"(2019, September 16). ImageCLEF \/ LifeCLEF\u2014Multimedia Retrieval in CLE. Available online: https:\/\/www.imageclef.org\/2015."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patcog.2017.05.015","article-title":"How deep learning extracts and learns leaf features for plant classification","volume":"71","author":"Sue","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.compag.2016.07.003","article-title":"Deep learning for plant identification using vein morphological patterns","volume":"127","author":"Guillermo","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/j.compag.2017.11.021","article-title":"A leaf-based back propagation neural network for oleander (Nerium oleander L.) cultivar identification","volume":"142","author":"Baldi","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","unstructured":"(2019, August 20). Weather China. Available online: https:\/\/www.weather.com.cn."},{"key":"ref_24","unstructured":"(2019, November 10). MATLAB for Artificial Intelligence. Available online: www.mathworks.com."},{"key":"ref_25","first-page":"5324","article-title":"Plant leaf disease detection using deep learning and convolutional neural network","volume":"7","author":"Hanson","year":"2017","journal-title":"Int. J. Eng. Sci. Comput."},{"key":"ref_26","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":"Lu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kawasaki, Y., Uga, H., Kagiwada, S., and Iyatomi, H. (2015, January 12\u201314). Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. Proceedings of the 12th International Symposium on Visual Computing, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-319-27863-6_59"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fpls.2016.01419","article-title":"Using deep learning for image-based plant disease detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fuentes, A., Yoon, S., Kim, S.C., and Park, D.S. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17.","DOI":"10.3390\/s17092022"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., and Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci., 2016.","DOI":"10.1155\/2016\/3289801"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, B., Zhang, Y., He, D., and Li, Y. (2018). Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks. Symmetry, 10.","DOI":"10.3390\/sym10010011"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/2\/217\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:53:53Z","timestamp":1760172833000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/2\/217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,2]]},"references-count":31,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["sym12020217"],"URL":"https:\/\/doi.org\/10.3390\/sym12020217","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2020,2,2]]}}}