{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T18:58:03Z","timestamp":1782327483334,"version":"3.54.5"},"reference-count":106,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T00:00:00Z","timestamp":1627344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovation Fund of Postgraduate, Xihua University","award":["YCJJ2020041"],"award-info":[{"award-number":["YCJJ2020041"]}]},{"name":"Sichuan Science and Technology Program","award":["2021YFN0020"],"award-info":[{"award-number":["2021YFN0020"]}]},{"name":"Sichuan Science and Technology Program","award":["2019YFN0106"],"award-info":[{"award-number":["2019YFN0106"]}]},{"name":"Key Project of Xihua University","award":["DC1900007141"],"award-info":[{"award-number":["DC1900007141"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31870347"],"award-info":[{"award-number":["31870347"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Agriculture"],"abstract":"<jats:p>Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.<\/jats:p>","DOI":"10.3390\/agriculture11080707","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T12:18:31Z","timestamp":1627388311000},"page":"707","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":447,"title":["Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5182-7508","authenticated-orcid":false,"given":"Jinzhu","family":"Lu","sequence":"first","affiliation":[{"name":"Modern Agricultural Equipment Research Institute, Xihua University, Chengdu 610039, China"},{"name":"School of Mechanical Engineering, Xihua University, Chengdu 610039, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4039-779X","authenticated-orcid":false,"given":"Lijuan","family":"Tan","sequence":"additional","affiliation":[{"name":"Modern Agricultural Equipment Research Institute, Xihua University, Chengdu 610039, China"},{"name":"School of Mechanical Engineering, Xihua University, Chengdu 610039, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6814-7477","authenticated-orcid":false,"given":"Huanyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mukti, I.Z., and Biswas, D. 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