{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T18:37:26Z","timestamp":1775759846857,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,10]],"date-time":"2020-02-10T00:00:00Z","timestamp":1581292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["NO. 2015ZCQ- GX-03"],"award-info":[{"award-number":["NO. 2015ZCQ- GX-03"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Diseases from Ginkgo biloba have brought great losses to medicine and the economy. Therefore, if the degree of disease can be automatically identified in Ginkgo biloba leaves, people will take appropriate measures to avoid losses in advance. Deep learning has made great achievements in plant disease identification and classification. For this paper, the convolution neural network model was used to classify the different degrees of ginkgo leaf disease. This study used the VGGNet-16 and Inception V3 models. After preprocessing and training 1322 original images under laboratory conditions and 2408 original images under field conditions, 98.44% accuracy was achieved under laboratory conditions and 92.19% under field conditions with the VGG model. The Inception V3 model achieved 92.3% accuracy under laboratory conditions and 93.2% under field conditions. Thus, the Inception V3 model structure was more suitable for field conditions. To our knowledge, there is very little research on the classification of different degrees of the same plant disease. The success of this study will have a significant impact on the prediction and early prevention of ginkgo leaf blight.<\/jats:p>","DOI":"10.3390\/info11020095","type":"journal-article","created":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T09:25:21Z","timestamp":1581413121000},"page":"95","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5919-7264","authenticated-orcid":false,"given":"Kaizhou","family":"Li","sequence":"first","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhui","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinrong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yandong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1001\/jama.2008.683","article-title":"Ginkgo biloba for prevention of dementia: A randomized controlled trial","volume":"300","author":"Dekosky","year":"2008","journal-title":"JAMA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6577","DOI":"10.1073\/pnas.111126298","article-title":"The in vivo neuromodulatory effects of the herbal medicine ginkgo biloba","volume":"98","author":"Watanabe","year":"2001","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1146\/annurev-phyto-080508-081743","article-title":"Plant disease diagnostic capabilities and networks","volume":"47","author":"Sally","year":"2009","journal-title":"Annu. Rev. Phytopathol."},{"key":"ref_4","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":"Sankaran","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"91","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_6","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1080\/10798587.2011.10643166","article-title":"Multiple Classifier Combination For Recognition Of Wheat Leaf Diseases","volume":"17","author":"Tian","year":"2011","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3305","DOI":"10.1073\/pnas.1524473113","article-title":"Computer vision cracks the leaf code","volume":"113","author":"Wilf","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s11831-016-9206-z","article-title":"Plant species identification using computer vision techniques: A systematic literature review","volume":"25","year":"2018","journal-title":"Arch Comput. Method E."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kumar, N., Belhumeur, P.N., Biswas, A., Jacobs, D.W., and Kress, W.J. (2012). Leafsnap: A computer vision system for automatic plant species identification. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-642-33709-3_36"},{"key":"ref_10","first-page":"5","article-title":"Feature extraction and automatic recognition of plant leaf using artificial neural network","volume":"3","author":"Wu","year":"2007","journal-title":"Adv. Artif. Intell."},{"key":"ref_11","first-page":"429","article-title":"Extraction of Leaf Vein Features Based on Artificial Neural Network-Studies on the Living Plant Identification \u2160","volume":"21","author":"Hong","year":"2004","journal-title":"Chin. Bull. Bot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rastogi, A., Arora, R., and Sharma, S. (2015, January 19\u201320). Leaf disease detection and grading using computer vision technology and fuzzy logic. Proceedings of the 2nd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","DOI":"10.1109\/SPIN.2015.7095350"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mehrotra, K., Mohan, C.K., and Ranka, S. (1997). Elements of Artificial Neural Networks. A Bradford Book, The MIT Press.","DOI":"10.7551\/mitpress\/2687.001.0001"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"15","DOI":"10.5120\/10172-4897","article-title":"Plant recognition from leaf image through artificial neural network","volume":"62","author":"Hati","year":"2013","journal-title":"IJCA"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document Recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_16","first-page":"1097","article-title":"Imagenet Classification with Deep Convolutional Neural Networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","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":"Grinblat","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lee, S.H., Chan, C.S., Wilkin, P., and Remagnino, P. (2015, January 27\u201330). Deep-plant: Plant identification with convolutional neural networks. Proceedings of the IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350839"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Carranza-Rojas, J., Goeau, H., Bonnet, P., Mata-Montero, E., and Joly, A. (2017). Going deeper in the automated identification of Herbarium specimens. BMC Evol. Biol., 17.","DOI":"10.1186\/s12862-017-1014-z"},{"key":"ref_20","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_21","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. Neurosc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","article-title":"Using Deep Learning for Image-Based Plant Disease Detection","volume":"17","author":"Mohanty","year":"2016","journal-title":"Front Plant Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/08839514.2017.1315516","article-title":"Deep learning for tomato diseases: Classification and symptoms visualization","volume":"31","author":"Brahimi","year":"2017","journal-title":"Appl. Artif. Intell."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1021\/ci0342472","article-title":"The problem of overfitting","volume":"35","author":"Hawkins","year":"2004","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 27). Rethinking the inception architecture for computer vision. Proceedings of the Computer Vision and Pattern Recognition 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/95\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:56:24Z","timestamp":1760172984000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/95"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,10]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["info11020095"],"URL":"https:\/\/doi.org\/10.3390\/info11020095","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,10]]}}}