{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:01:36Z","timestamp":1777042896461,"version":"3.51.4"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976063"],"award-info":[{"award-number":["61976063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11042-023-14755-w","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T19:58:00Z","timestamp":1677009480000},"page":"23655-23672","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A grape disease identification and severity estimation system"],"prefix":"10.1007","volume":"82","author":[{"given":"Haiping","family":"Shu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9790-1571","authenticated-orcid":false,"given":"Junxiu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Hua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunsheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuling","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"issue":"12","key":"14755_CR1","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"14755_CR2","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1080\/07352681003617285","volume":"29","author":"CH Bock","year":"2010","unstructured":"Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. CRC Crit Rev Plant Sci 29(2):59\u2013107","journal-title":"CRC Crit Rev Plant Sci"},{"issue":"11","key":"14755_CR3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.compag.2018.12.028","volume":"157","author":"A Cruz","year":"2019","unstructured":"Cruz A et al (2019) Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence. Comput Electron Agric 157(11):63\u201376","journal-title":"Comput Electron Agric"},{"issue":"1","key":"14755_CR4","first-page":"1","volume":"169","author":"JGM Esgario","year":"2020","unstructured":"Esgario JGM, Krohling RA, Ventura JA (2020) Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput Electron Agric 169(1):1\u20139","journal-title":"Comput Electron Agric"},{"issue":"8","key":"14755_CR5","first-page":"1","volume":"96","author":"M Fern\u00e1ndez-D\u00edaz","year":"2020","unstructured":"Fern\u00e1ndez-D\u00edaz M, Gallardo-Antol\u00edn A (2020) An attention long short-term memory based system for automatic classification of speech intelligibility. Eng Appl Artif Intell 96(8):1\u20138","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"14755_CR6","first-page":"1","volume":"90","author":"G Hu","year":"2021","unstructured":"Hu G, Wang H, Zhang Y, Wan M (2021) Detection and severity analysis of tea leaf blight based on deep learning. Comput Electr Eng 90(1):1\u201315","journal-title":"Comput Electr Eng"},{"key":"14755_CR7","unstructured":"Hughes DP, Salathe M (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv, pp 1\u201313"},{"issue":"4","key":"14755_CR8","first-page":"328","volume":"8","author":"SB Jadhav","year":"2019","unstructured":"Jadhav SB (2019) Convolutional neural networks for leaf image-based plant disease classification. IAES Int J Artif Intell 8(4):328\u2013341","journal-title":"IAES Int J Artif Intell"},{"key":"14755_CR9","unstructured":"Kim TH, Sajjadi MSM, Hirsch M, Sch B (2018) Encoder-Decoder with atrous separable convolution for semantic image segmentation. In: European Conference on Computer Vision(ECCV), pp 111\u2013127"},{"key":"14755_CR10","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. In: In 3rd international conference on learning representations, pp 1\u201315"},{"key":"14755_CR11","doi-asserted-by":"crossref","unstructured":"Kranz J (1988) Measuring plant disease. In: Experimental techniques in plant disease epidemiology. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 35\u201350","DOI":"10.1007\/978-3-642-95534-1_4"},{"issue":"1","key":"14755_CR12","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/JBHI.2016.2635663","volume":"21","author":"A Kumar","year":"2017","unstructured":"Kumar A, Kim J, Lyndon D, Fulham M, Feng D (2017) An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J Biomed Health Inform 21(1):31\u201340","journal-title":"IEEE J Biomed Health Inform"},{"issue":"12","key":"14755_CR13","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1016\/j.compag.2019.01.034","volume":"157","author":"Q Liang","year":"2019","unstructured":"Liang Q, Xiang S, Hu Y, Coppola G, Zhang D, Sun W (2019) PD 2 SE-Net: computer-assisted plant disease diagnosis and severity estimation network. Comput Electron Agric 157(12):518\u2013529","journal-title":"Comput Electron Agric"},{"key":"14755_CR14","doi-asserted-by":"crossref","unstructured":"Liang X, B JL, Luo Y, Wu G (2020) Automatic segmentation and diagnosis of intervertebral discs based on deep neural networks. In: International Conference on Neural Information Processing (ICONIP), pp 168\u2013175","DOI":"10.1007\/978-3-030-63820-7_19"},{"issue":"7","key":"14755_CR15","first-page":"1","volume":"11","author":"B Liu","year":"2020","unstructured":"Liu B, Ding Z, Tian L, He D, Li S, Wang H (2020) Grape leaf disease identification using improved deep convolutional neural networks. Front Plant Sci 11(7):1\u201314","journal-title":"Front Plant Sci"},{"issue":"5","key":"14755_CR16","first-page":"1","volume":"203","author":"J Liu","year":"2021","unstructured":"Liu J, Li M, Luo Y, Yang S, Li W, Bi Y (2021) Alzheimer\u2019s disease detection using depthwise separable convolutional neural networks. Comput Methods Programs Biomed 203(5):1\u201310","journal-title":"Comput Methods Programs Biomed"},{"key":"14755_CR17","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer International Publishing, Cham, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"4","key":"14755_CR18","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.ins.2020.11.026","volume":"569","author":"J Shen","year":"2021","unstructured":"Shen J, Robertson N (2021) Towards large scale effective ensemble adversarial attacks against deep neural network learning. Inf Sci (Ny) 569(4):469\u2013478","journal-title":"Inf Sci (Ny)"},{"issue":"4","key":"14755_CR19","doi-asserted-by":"publisher","first-page":"4376","DOI":"10.1109\/TMM.2020.3042068","volume":"23","author":"Y Shi","year":"2021","unstructured":"Shi Y, Wei Z, Ling H, Wang Z, Shen J, Li P (2021) Person retrieval in surveillance videos via deep attribute mining and reasoning. IEEE Trans Multimed 23(4):4376\u20134387","journal-title":"IEEE Trans Multimed"},{"issue":"1","key":"14755_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/3289801","volume":"2016","author":"S Sladojevic","year":"2016","unstructured":"Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci 2016(1):1\u201311","journal-title":"Comput Intell Neurosci"},{"issue":"10","key":"14755_CR21","first-page":"1","volume":"178","author":"Z Tang","year":"2020","unstructured":"Tang Z, Yang J, Li Z, Qi F (2020) Grape disease image classification based on lightweight convolution neural networks and channelwise attention. Comput Electron Agric 178(10):1\u20139","journal-title":"Comput Electron Agric"},{"key":"14755_CR22","doi-asserted-by":"crossref","unstructured":"Waghmare H, Kokare R, Dandawate Y (2016) Detection and classification of diseases of grape plant using opposite colour local binary pattern feature and machine learning for automated decision support system. In: 3rd International Conference on Signal Processing and Integrated Networks (SPIN), pp 513\u2013518","DOI":"10.1109\/SPIN.2016.7566749"},{"issue":"1","key":"14755_CR23","first-page":"1","volume":"2017","author":"G Wang","year":"2017","unstructured":"Wang G, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci 2017(1):1\u20138","journal-title":"Comput Intell Neurosci"},{"issue":"7","key":"14755_CR24","doi-asserted-by":"publisher","first-page":"3330","DOI":"10.1109\/TCYB.2019.2894498","volume":"50","author":"L Wang","year":"2020","unstructured":"Wang L, Qian X, Zhang Y, Shen J, Cao X (2020) Enhancing sketch-based image retrieval by CNN semantic re-ranking. IEEE Trans Cybern 50(7):3330\u20133342","journal-title":"IEEE Trans Cybern"},{"key":"14755_CR25","doi-asserted-by":"crossref","unstructured":"Wang C, Du P, Wu H, Li J, Zhao C, Zhu H (2021) A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net. Comput Electron Agric 189(4):1\u201313","DOI":"10.1016\/j.compag.2021.106373"},{"key":"14755_CR26","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. In: European Conference on Computer Vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"14755_CR27","doi-asserted-by":"crossref","unstructured":"Wu Y, Wu J, Hu G (2020) MMFS:a grape disease recognition method based on multi-feature fusion and SVM. In: Proceedings of the 2020 4th International Conference on Cloud and Big Data Computing, pp 27\u201331","DOI":"10.1145\/3416921.3416927"},{"issue":"3","key":"14755_CR28","first-page":"1","volume":"172","author":"W Zeng","year":"2020","unstructured":"Zeng W, Li M (2020) Crop leaf disease recognition based on self-attention convolutional neural network. Comput Electron Agric 172(3):1\u20137","journal-title":"Comput Electron Agric"},{"issue":"9","key":"14755_CR29","doi-asserted-by":"publisher","first-page":"172882","DOI":"10.1109\/ACCESS.2020.3025196","volume":"8","author":"Q Zeng","year":"2020","unstructured":"Zeng Q, Ma X, Cheng B, Zhou E, Pang W (2020) GANs-based data augmentation for citrus disease severity detection using deep learning. IEEE Access 8(9):172882\u2013172891","journal-title":"IEEE Access"},{"key":"14755_CR30","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 6230\u20136239","DOI":"10.1109\/CVPR.2017.660"},{"key":"14755_CR31","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s11042-018-7092-0","volume":"79","author":"J Zhu","year":"2020","unstructured":"Zhu J, Wu A, Wang X, Zhang H (2020) Identification of grape diseases using image analysis and BP neural networks. Multimed Tools Appl 79:21\u201322","journal-title":"Multimed Tools Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14755-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-14755-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14755-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T09:36:48Z","timestamp":1685439408000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-14755-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,21]]},"references-count":31,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["14755"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-14755-w","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,21]]},"assertion":[{"value":"25 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}