{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T13:35:09Z","timestamp":1780320909658,"version":"3.54.1"},"reference-count":51,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,16]],"date-time":"2020-08-16T00:00:00Z","timestamp":1597536000000},"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":["No.61802410"],"award-info":[{"award-number":["No.61802410"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005236","name":"Chinese Universities Scientific Fund","doi-asserted-by":"publisher","award":["(2018XD002 & 2018QC024"],"award-info":[{"award-number":["(2018XD002 & 2018QC024"]}],"id":[{"id":"10.13039\/501100005236","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model training to compensate for the lack of training samples. Then, we test the performance of the generated super-resolution images in crop disease classification task. Extensive experiments show that using the fine-tuned ESRGAN model can recover realistic crop information and improve the accuracy of crop disease classification, compared with the other four image super-resolution methods.<\/jats:p>","DOI":"10.3390\/s20164601","type":"journal-article","created":{"date-parts":[[2020,8,17]],"date-time":"2020-08-17T04:35:51Z","timestamp":1597638951000},"page":"4601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Crop Disease Classification on Inadequate Low-Resolution Target Images"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4199-2988","authenticated-orcid":false,"given":"Juan","family":"Wen","sequence":"first","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangjing","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoshi","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6500-3868","authenticated-orcid":false,"given":"Yiming","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20130089","DOI":"10.1098\/rstb.2013.0089","article-title":"Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar","volume":"369","author":"Harvey","year":"2014","journal-title":"Philos. 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