{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T11:31:19Z","timestamp":1770031879496,"version":"3.49.0"},"reference-count":9,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,12,16]]},"abstract":"<jats:p>The identification and classification of plant diseases is of great significance to ecological protection and deep learning methods have made a great of progress in the common plant diseases identification for specific plant. While faced with the same plant disease of other plants, due to the insufficient or low quality training data, current deep learning methods will be difficult to identify the diseases effectively and accurately. Inspired by the advantages of GAN in dataset expansion, we propose the CycleGAN based confusion model in this paper. In this paper, GAN framework is improved by adding noise label and learn together during training stage, which migrates the data of common plant diseases to the plants with insufficient or low quality data. In order to evaluate the quality of the migrated training dataset among different GAN approaches, we introduce the quality indicators of the migration images such as MMD, FID, EMD etc. We compare our model with other GANs model, and the experimental results show that the proposed model obtains better results in the migration process, which make it more effective for the identification of cross species plant diseases.<\/jats:p>","DOI":"10.3233\/jifs-210585","type":"journal-article","created":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T12:05:44Z","timestamp":1629461144000},"page":"6685-6696","source":"Crossref","is-referenced-by-count":6,"title":["CycleGAN based confusion model for cross-species plant disease image migration"],"prefix":"10.1177","volume":"41","author":[{"given":"Xiaohui","family":"Cui","sequence":"first","affiliation":[{"name":"School of Information Science and Technology of Beijing Forestry University, Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration"}]},{"given":"Yongzhi","family":"Ying","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology of Beijing Forestry University, Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration"}]},{"given":"Zhibo","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology of Beijing Forestry University, Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration"}]}],"member":"179","reference":[{"issue":"7","key":"10.3233\/JIFS-210585_ref1","doi-asserted-by":"crossref","first-page":"939","DOI":"10.3390\/sym11070939","article-title":"Solving current limitations of deep learning based approaches for plant disease detection[J]","volume":"11","author":"Arsenovic","year":"2019","journal-title":"Symmetry"},{"key":"10.3233\/JIFS-210585_ref4","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[J]","volume":"145","author":"Ferentinos","year":"2018","journal-title":"Computers and Electronics in Agriculture"},{"issue":"3","key":"10.3233\/JIFS-210585_ref5","doi-asserted-by":"crossref","first-page":"537","DOI":"10.3390\/bios5030537","article-title":"Current and prospective methods for plant disease detection[J]","volume":"5","author":"Fang","year":"2015","journal-title":"Biosensors"},{"issue":"3","key":"10.3233\/JIFS-210585_ref6","doi-asserted-by":"publisher","first-page":"4905","DOI":"10.3233\/JIFS-201691","article-title":"Test Scheduling of Systemon-Chip Using Dragonfly and Ant Lion Optimization Algorithms[J]","volume":"40","author":"Chandrasekaran","year":"2021","journal-title":"Journal of Intelligent and Fuzzy Systems"},{"key":"10.3233\/JIFS-210585_ref7","doi-asserted-by":"crossref","unstructured":"Ronneberger O. , Fischer P. and Brox T. , U-net: Convolutional networks for biomedical image segmentation[C].\/\/International Conference on Medical image computing and computerassisted intervention. Springer, Cham, (2015), 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"1","key":"10.3233\/JIFS-210585_ref17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-52737-x","article-title":"Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks[J]","volume":"9","author":"Sandfort","year":"2019","journal-title":"Scientific Reports"},{"issue":"1","key":"10.3233\/JIFS-210585_ref20","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting[J]","volume":"15","author":"Srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"issue":"1","key":"10.3233\/JIFS-210585_ref21","doi-asserted-by":"crossref","first-page":"167","DOI":"10.32604\/cmc.2018.02356","article-title":"A method for improving CNN-based image recognition using DCGAN[J]","volume":"57","author":"Fang","year":"2018","journal-title":"Computers, Materials and Continua"},{"key":"10.3233\/JIFS-210585_ref26","doi-asserted-by":"publisher","first-page":"5303","DOI":"10.1007\/s00521-019-04039-6","article-title":"Minimization of test time in system on chip using artificial intelligence-based test scheduling techniques[J]","volume":"32","author":"Chandrasekaran","year":"2020","journal-title":"Neural Computing and Applications"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-210585","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:54:01Z","timestamp":1769993641000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-210585"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,16]]},"references-count":9,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-210585","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,16]]}}}