{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:14:27Z","timestamp":1792790067713,"version":"4.5.0"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,12,29]],"date-time":"2017-12-29T00:00:00Z","timestamp":1514505600000},"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":["61602388"],"award-info":[{"award-number":["61602388"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2017JM6059"],"award-info":[{"award-number":["2017JM6059"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M613216"],"award-info":[{"award-number":["2017M613216"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Science Foundation of Shaanxi Province of China","award":["2016BSHEDZZ121"],"award-info":[{"award-number":["2016BSHEDZZ121"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2452015194,2452016081"],"award-info":[{"award-number":["2452015194,2452016081"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.<\/jats:p>","DOI":"10.3390\/sym10010011","type":"journal-article","created":{"date-parts":[[2017,12,29]],"date-time":"2017-12-29T10:58:47Z","timestamp":1514545127000},"page":"11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":671,"title":["Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9388-0198","authenticated-orcid":false,"given":"Bin","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Engineering, NorthWest A&F University, No. 22, Xinong Road, Yangling 712100, China"},{"name":"Key Laboratory of Agricultural Internet of Things (NorthWest A&F University), Ministry of Agriculture, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, NorthWest A&F University, No. 22, Xinong Road, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"DongJian","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Internet of Things (NorthWest A&F University), Ministry of Agriculture, Yangling 712100, China"},{"name":"College of Mechanical and Electronic Engineering, NorthWest A&F University, No. 22, Xinong Road, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Technology, Henan University of Science and Technology, No. 263, Kaiyuan Avenue, Luoyang 471023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.postharvbio.2013.04.003","article-title":"Predicting the spread of postharvest disease in stored fruit, with application to apples","volume":"85","author":"Dutot","year":"2013","journal-title":"Postharvest Biol. 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