{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T11:58:21Z","timestamp":1772884701573,"version":"3.50.1"},"reference-count":11,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,11,4]]},"abstract":"<jats:p>Despite the significant improvements in the detection and diagnosis of plant diseases at an early stage facilitated by deep learning technology, there are challenges associated with the generalization performance of deep learning models. These problems from the differences between in-field and in-lab data, as well as the heterogeneity of training and prediction data features. In the case of tomato leaf diseases, the PlantVillage dataset is widely used and has already demonstrated accuracy of more than 99%. However, using trained model based on this dataset to predict in-field data results in low accuracy due to domain differences and heterogeneous features. In this paper, we propose a domain adaptation method based on CycleGAN to solve this problem, followed by a preprocessing technique that utilizes both the OpenCV module and a segmentation model based on U-Net for the best generalization performance. The classification accuracy is evaluated by applying the DenseNet121 model trained on the PlantVillage dataset to the images generated by CycleGAN. Our results demonstrate, with an F1-score of 95.6%, that our domain adaptation method between the two domains is effective in mitigating the effect of domain shift.<\/jats:p>","DOI":"10.3233\/jifs-230561","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:11:49Z","timestamp":1694776309000},"page":"8859-8870","source":"Crossref","is-referenced-by-count":5,"title":["Heterogeneous domain adaptation method for tomato leaf disease classification base on CycleGAN"],"prefix":"10.1177","volume":"45","author":[{"given":"Seung-Beom","family":"Cho","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Sungkyunkwan University, Suwon, Republic of Korea"}]},{"given":"Si-Hwa","family":"Jeong","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sungkyunkwan University, Suwon, Republic of Korea"}]},{"given":"Jae-Wook","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sungkyunkwan University, Suwon, Republic of Korea"}]},{"given":"Jae-Boong","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sungkyunkwan University, Suwon, Republic of Korea"}]},{"given":"Moon Ki","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sungkyunkwan University, Suwon, Republic of Korea"},{"name":"SKKU Advanced Institute of Nano Technology, Sungkyunkwan University, Suwon, Republic of Korea"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-230561_ref1","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1007\/s12571-012-0200-5","article-title":"Crop losses due to diseases and their implications for global food production losses and food security","volume":"4.4","author":"Savary Serge","year":"2012","journal-title":"Food Security"},{"key":"10.3233\/JIFS-230561_ref4","first-page":"712","article-title":"A survey of deep learning applications to autonomous vehicle control","volume":"22.2","author":"Kuutti Sampo","year":"2020","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.3233\/JIFS-230561_ref5","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen Dinggang","year":"2017","journal-title":"Annual Review of Biomedical Engineering"},{"key":"10.3233\/JIFS-230561_ref6","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86.11","author":"Lecun Yann","year":"1998","journal-title":"Proceedings of the IEEE"},{"key":"10.3233\/JIFS-230561_ref7","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60.6","author":"Krizhevsky Alex","year":"2017","journal-title":"Communications of the ACM"},{"key":"10.3233\/JIFS-230561_ref15","doi-asserted-by":"crossref","first-page":"18583","DOI":"10.1007\/s11042-021-10599-4","article-title":"Improved crossover-based monarch butterfly optimization for tomato leaf disease classification using convolutional neural network","volume":"80","author":"Nandhini","year":"2021","journal-title":"Multimedia Tools and Applications"},{"key":"10.3233\/JIFS-230561_ref18","first-page":"1258","article-title":"Leafgan: An effective data augmentation method for practical plant disease diagnosis","volume":"19.2","author":"Cap Quan Huu","year":"2020","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"key":"10.3233\/JIFS-230561_ref19","doi-asserted-by":"crossref","first-page":"106279","DOI":"10.1016\/j.compag.2021.106279","article-title":"Tomato plant disease detection using transfer learning with C-GAN synthetic images","volume":"187","author":"Abbas Amreen","year":"2021","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.3233\/JIFS-230561_ref20","doi-asserted-by":"crossref","first-page":"46776","DOI":"10.1109\/ACCESS.2021.3068094","article-title":"ArCycleGAN: Improved CycleGAN for style transferring of fruit images","volume":"9","author":"Chen Hongqian","year":"2021","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-230561_ref32","first-page":"033011","article-title":"New benchmark for image segmentation evaluation","volume":"16.3","author":"Feng","year":"2007","journal-title":"Journal of Electronic Imaging"},{"key":"10.3233\/JIFS-230561_ref35","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1007\/s41348-021-00500-8","article-title":"Artificial intelligence in tomato leaf disease detection: a comprehensive review and discussion","volume":"129.3","author":"Thangaraj Rajasekaran","year":"2022","journal-title":"Journal of Plant Diseases and Protection"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-230561","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T07:26:03Z","timestamp":1769671563000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-230561"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,4]]},"references-count":11,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jifs-230561","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,4]]}}}