{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:55:49Z","timestamp":1783490149532,"version":"3.55.0"},"reference-count":50,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976150"],"award-info":[{"award-number":["61976150"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["201801D121135, 201901D111091"],"award-info":[{"award-number":["201801D121135, 201901D111091"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]},{"name":"key R &amp; D projects of  Jinzhong City","award":["Y192006"],"award-info":[{"award-number":["Y192006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Accurate recognition of tomato diseases is of great significance for agricultural production. Sufficient and insufficient training data of supervised recognition neural network training are symmetry problems. A high precision neural network needs a large number of labeled data, and the difficulty of data sample acquisition is the main challenge to improving the performance of disease recognition. The tomato leaf data augmented by the traditional data augmentation methods based on geometric transformation usually contain less information, and the generalization is not strong. Therefore, a new data augmentation method, RAHC_GAN, based on generative adversarial networks is proposed in this paper, which is used to expand tomato leaf data and identify diseases. In this method, continuous hidden variables are added at the input of the generator, and the purpose is to continuously control the size of the generated disease area and to supplement the intra class information of the same disease. Additionally, the residual attention block is added to the generator to make it pay more attention to the disease region in the leaf image; a multi-scale discriminator is also used to enrich the detailed texture of the generated image and finally generate leaves with obvious disease features. Then, we use the images generated by RAHC_GAN and the original training images to build an expanded data set, which is used to train four kinds of recognition networks, AlexNet, VGGNet, GoogLeNet, and ResNet, and the performance is evaluated through the test set. Experimental results show that RAHC_GAN can generate leaves with obvious disease features, and the generated expanded data set can significantly improve the recognition performance of the classifier. Furthermore, the results of the apple, grape, and corn data set show that RAHC_GAN can also be used as a method to solve the problem of insufficient data in other plant research tasks.<\/jats:p>","DOI":"10.3390\/sym13091597","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:58:15Z","timestamp":1630450695000},"page":"1597","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9738-1310","authenticated-orcid":false,"given":"Hongxia","family":"Deng","sequence":"first","affiliation":[{"name":"Department of Information and Computer, Taiyuan University of Technology, TaiYuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2753-0058","authenticated-orcid":false,"given":"Dongsheng","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Information and Computer, Taiyuan University of Technology, TaiYuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangwei","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Information and Computer, Taiyuan University of Technology, TaiYuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Information and Computer, Taiyuan University of Technology, TaiYuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Information and Computer, Taiyuan University of Technology, TaiYuan 030024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1093\/jnci\/91.4.317","article-title":"Tomatoes, Tomato-Based Products, Lycopene, and Cancer: Review of the Epidemiologic Literature","volume":"91","author":"Giovannucci","year":"1999","journal-title":"J. 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