{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T21:10:27Z","timestamp":1771967427082,"version":"3.50.1"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"name":"This Project \u201cResearch on the interdisciplinary theory, method and application of plant phenotype data management\u201d supported by Shandong Provincial Natural Science Foundation","award":["ZR2022MG047"],"award-info":[{"award-number":["ZR2022MG047"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:p> Soybean leaf disease labeling data are not easy to obtain, and soybean leaf disease model training often needs a lot of data. Due to the limitations of fixed rules such as rotation and clipping, traditional data enhancement cannot generate images with diversity and variability. In view of the above problems, this study proposed a data enhancement method based on generative adversarial network to expand the original soybean leaf disease dataset. This method was based on cyclic confrontation network, and its discriminator uses dense connection strategy to realize feature reuse, so as to reduce the amount of calculation. In the training process, improved transfer learning is used to automatically fine tune the pre-training model. The accuracy of the optimized method in 9 kinds of soybean leaf disease image recognition is 95.84%, which is 0.98% higher than the traditional fine-tuning method. The experimental results show that this method based on generating confrontation network has significant ability in generating soybean leaf disease image, and can expand the existing dataset. In addition, this method also provides an effective data enhancement solution for the expansion of other crop disease image datasets. <\/jats:p>","DOI":"10.1142\/s146902682350030x","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T09:33:28Z","timestamp":1699954408000},"source":"Crossref","is-referenced-by-count":4,"title":["Soybean Leaf Diseases Recognition Based on Generative Adversarial Network and Transfer Learning"],"prefix":"10.1142","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9094-8748","authenticated-orcid":false,"given":"Xiao","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7146-5656","authenticated-orcid":false,"given":"Cong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9542-7271","authenticated-orcid":false,"given":"Qi","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3663-1305","authenticated-orcid":false,"given":"Weihan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7083-4594","authenticated-orcid":false,"given":"Lina","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Business School, Shandong University of Technology, Zibo, 255000, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2023,11,11]]},"reference":[{"key":"S146902682350030XBIB001","first-page":"1952936","volume":"2022","author":"Miao E.","year":"2022","journal-title":"Mob. Inf. Syst."},{"key":"S146902682350030XBIB002","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2021.682230"},{"key":"S146902682350030XBIB003","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-23484-3"},{"key":"S146902682350030XBIB004","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture12081226"},{"key":"S146902682350030XBIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106064"},{"issue":"1","key":"S146902682350030XBIB006","first-page":"27","volume":"8","author":"Ngugi L. C.","year":"2021","journal-title":"Inf. Process. Agric."},{"key":"S146902682350030XBIB007","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-77281-8_26"},{"key":"S146902682350030XBIB008","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2003.1227801"},{"key":"S146902682350030XBIB009","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"S146902682350030XBIB010","doi-asserted-by":"publisher","DOI":"10.20965\/jaciii.2022.p0003"},{"key":"S146902682350030XBIB011","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"S146902682350030XBIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104412"},{"key":"S146902682350030XBIB013","first-page":"1","author":"Kabiraj A.","year":"2022","journal-title":"Multimedia Tools Appl."},{"key":"S146902682350030XBIB014","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2997001"},{"key":"S146902682350030XBIB015","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"S146902682350030XBIB016","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1716999115"},{"key":"S146902682350030XBIB018","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"S146902682350030XBIB019","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"S146902682350030XBIB020","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"S146902682350030XBIB021","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"S146902682350030XBIB022","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"S146902682350030XBIB023","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"S146902682350030XBIB024","first-page":"24","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Bergstra J.","year":"2011"},{"key":"S146902682350030XBIB025","first-page":"32","volume-title":"Adv. Neural Inf. Process. Syst.","author":"Paszke A.","year":"2019"},{"key":"S146902682350030XBIB026","doi-asserted-by":"publisher","DOI":"10.3390\/info11020108"},{"key":"S146902682350030XBIB027","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"S146902682350030XBIB029","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.58"}],"container-title":["International Journal of Computational Intelligence and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S146902682350030X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T07:03:10Z","timestamp":1711090990000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S146902682350030X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,11]]},"references-count":27,"journal-issue":{"issue":"01","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10.1142\/S146902682350030X"],"URL":"https:\/\/doi.org\/10.1142\/s146902682350030x","relation":{},"ISSN":["1469-0268","1757-5885"],"issn-type":[{"value":"1469-0268","type":"print"},{"value":"1757-5885","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,11]]},"article-number":"2350030"}}