{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T17:26:44Z","timestamp":1783618004394,"version":"3.55.0"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Key Research and Development Program of Shaanxi","award":["2019ZDLNY07-06-01"],"award-info":[{"award-number":["2019ZDLNY07-06-01"]}]},{"name":"Ningxia Smart Agricultural Industry Technology Collaborative Innovation Center","award":["2017DC53"],"award-info":[{"award-number":["2017DC53"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2452019064"],"award-info":[{"award-number":["2452019064"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602388"],"award-info":[{"award-number":["61602388"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902339"],"award-info":[{"award-number":["61902339"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M613216"],"award-info":[{"award-number":["2017M613216"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2017JM6059"],"award-info":[{"award-number":["2017JM6059"]}]},{"DOI":"10.13039\/501100009996","name":"Postdoctoral Science Foundation of Shaanxi Province of China","doi-asserted-by":"publisher","award":["2016BSHEDZZ121"],"award-info":[{"award-number":["2016BSHEDZZ121"]}],"id":[{"id":"10.13039\/501100009996","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data"},{"name":"Yan\u2019an University","award":["IPBED14"],"award-info":[{"award-number":["IPBED14"]}]},{"name":"Doctoral Starting Up Foundation of Yan\u2019an University","award":["YDBK2019-06"],"award-info":[{"award-number":["YDBK2019-06"]}]},{"DOI":"10.13039\/501100007548","name":"Innovation and Entrepreneurship Training Program of Northwest A&F University of China","doi-asserted-by":"publisher","award":["201910712048"],"award-info":[{"award-number":["201910712048"]}],"id":[{"id":"10.13039\/501100007548","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.2998839","type":"journal-article","created":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T21:32:05Z","timestamp":1591047125000},"page":"102188-102198","source":"Crossref","is-referenced-by-count":186,"title":["A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9388-0198","authenticated-orcid":false,"given":"Bin","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8639-923X","authenticated-orcid":false,"given":"Cheng","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinrong","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1","article-title":"Random erasing data augmentation","author":"zhong","year":"2020","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref38","first-page":"1","article-title":"Mixup: Beyond empirical risk minimization","author":"zhang","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref32","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref31","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevskyi sutskever","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","first-page":"6626","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume":"2017","author":"heusel","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref37","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"tan","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2913372"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref28","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Statist"},{"key":"ref27","first-page":"2234","article-title":"Improved techniques for training GANs","author":"salimans","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.07.016"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/plants8110468"},{"key":"ref20","first-page":"1","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radfordl metz","year":"2016","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref22","article-title":"Instance normalization: The missing ingredient for fast stylization","author":"ulyanov","year":"2016","journal-title":"arXiv 1607 08022"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref24","article-title":"On convergence and stability of GANs","author":"kodali","year":"2017","journal-title":"arXiv 1705 07215"},{"key":"ref23","first-page":"456","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref26","first-page":"331","article-title":"Adversarial perturbations of deep neural networks","volume":"311","author":"warde-farley","year":"2016","journal-title":"Perturbations Optim Statist"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.405"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00224"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2977386"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01265-2"},{"key":"ref56","first-page":"5767","article-title":"Improved training of wasserstein GANs","author":"gulrajani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref55","first-page":"214","article-title":"Wasserstein GAN","author":"arjovsky","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2969805"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.06.043"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2979239"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.03.012"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/en12091735"},{"key":"ref40","first-page":"1","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.06.023"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-38966-0"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogsys.2018.04.006"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1094\/PHYTO-08-18-0288-R"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2019.00209"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/sym10010011"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2914929"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.01.009"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2019.04.011"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2019.00941"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2016.01419"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.10.013"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/s17092022"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2019.00611"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01052"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2019.00272"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01033"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2977007"},{"key":"ref47","first-page":"1","article-title":"Progressive growing of GANs for improved quality, stability, and variation","author":"karras","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref41","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00835"},{"key":"ref43","first-page":"1558","article-title":"Autoencoding beyond pixels using a learned similarity metric","author":"larsen","year":"2016","journal-title":"Proc Int Conf Mach Learn"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09104723.pdf?arnumber=9104723","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:52:24Z","timestamp":1639770744000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9104723\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":58,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2998839","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}