{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:15:05Z","timestamp":1769519705036,"version":"3.49.0"},"reference-count":42,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T00:00:00Z","timestamp":1649721600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Key Project of Henan Province","award":["212102210520"],"award-info":[{"award-number":["212102210520"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2022,4,12]]},"abstract":"<jats:p>Identifying crop disease fast, intelligently and accurately, plays a vital role in agricultural informatization development, while existing methods are almost performed manually, which depends on expert experience, and thus the identifying result is inevitably influenced by personal preferences. To address these issues, an improved crop disease identification method based on convolutional neural network is proposed to process images of crops for identifying diseases. Firstly, the original crop images were cut and normalized, and the irrelevant noises were removed by image data enhancement to improve the generalization ability and recognition accuracy of the training network. Then a neural network with nine convolutional layers is built to work on crop images, the first stage of training loads data samples, and divide the training set and verification set, and then set the learning rate, image intensifier, and optimizer and compile the training convolution model. Finally, it saves the loss and accuracy data during the training process and evaluates the accuracy of the model. In order to improve the training learning rate, Adam optimizer combining momentum algorithm and RMSprop algorithm is used to dynamically adjust the learning rate; the combination of the two algorithms makes the loss function converge to the lowest point faster. Then the feature map after each convolution is obtained by using transferred revolution, and the model is adjusted according to the feature map to further improve the effect of model recognition. Finally, validations were carried out by PlantVillage dataset, which consists of images of about 38 kinds of crops. The experiment result shows that the validation accuracy and the test accuracy are 95.7% and 94.3%, respectively; in addition, the recognition accuracy of apple, corn, grape, and other single classes is about 97%, which proves that the convolutional neural network in this paper has faster training speed and higher accuracy. In addition, the proposed method is less time consuming, which is of great significance to promote the development of smart agriculture and precision agriculture.<\/jats:p>","DOI":"10.1155\/2022\/6342357","type":"journal-article","created":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T18:06:22Z","timestamp":1649786782000},"page":"1-16","source":"Crossref","is-referenced-by-count":16,"title":["An Improved Crop Disease Identification Method Based on Lightweight Convolutional Neural Network"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5204-9365","authenticated-orcid":true,"given":"Tingzhong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Technology, Luoyang Normal University, Luoyang 471934, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Honghao","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Technology, Luoyang Normal University, Luoyang 471934, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yudong","family":"Hai","sequence":"additional","affiliation":[{"name":"School of Information Technology, Luoyang Normal University, Luoyang 471934, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutian","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Information Technology, Luoyang Normal University, Luoyang 471934, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Technology, Luoyang Normal University, Luoyang 471934, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"issue":"20","key":"1","first-page":"135","article-title":"Early prediction of antioxidant enzyme value of rice blast based on hyper-spectral image[J]","volume":"29","author":"Y. Yang","year":"2013","journal-title":"Transactions of the Chinese Society of Agricultural Engineering"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1038\/srep27790"},{"issue":"4","key":"3","first-page":"1","article-title":"Automatic detection of diseased tomato plants using thermal and stereo visible light images","volume":"10","author":"G. Prince","year":"2015","journal-title":"PLoS One"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2017.2675902"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0123262"},{"key":"6","first-page":"1","article-title":"Detection of potato diseases using image segmentation and multiclass support vector machine","author":"M. Islam"},{"issue":"3","key":"7","first-page":"134","article-title":"Application of support vector machine in plant lesion shape recognition","volume":"20","author":"Y. Tian","year":"2004","journal-title":"Journal of agricultural engineering"},{"key":"8","volume-title":"Classification of Apple Leaf Diseases Based on Image Processing and Support Vector Machine [D]","author":"S. Sun","year":"2017"},{"issue":"20","key":"9","first-page":"169","article-title":"Detection method of rice ear blast based on deep convolution neural network","author":"S. Huang","year":"2017","journal-title":"Journal of agricultural engineering"},{"key":"10","volume-title":"Extraction of Potato Spatial Distribution Based on BP Neural Network [D]","author":"Y. Zhou","year":"2018"},{"issue":"10","key":"11","article-title":"Rice leaf roll recognition based on BP neural network","volume":"49","author":"R. Zhao","year":"2018","journal-title":"Journal of southern agriculture"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"13","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"A. Krizhevsky"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.3390\/rs12213547"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105933"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105803"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.08.048"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.07.070"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1094\/phyto-08-18-0288-r"},{"key":"20","article-title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems","author":"A. Mart\u00edn","year":"2015"},{"key":"21","article-title":"TensorFlow: a system for large-scale machine learning","author":"J. Dean"},{"key":"22","article-title":"Visualizing and understanding convolutional networks","author":"M. D. Zeiler"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"25","volume-title":"Deep Learning","author":"I. Goodfellow","year":"2016"},{"key":"26","article-title":"Recent advances in convolutional neural networks","author":"J. Gu","year":"2015"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1007\/bf00344251"},{"issue":"4","key":"28","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwitten zip code recognition","volume":"1","author":"Y. Lecun","year":"2014","journal-title":"Neural Computation"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"30","first-page":"79","article-title":"A deep learning-based approach for banana leaf diseases classification","author":"J. Amara"},{"issue":"18","key":"31","article-title":"Mage recognition of Camellia oleifera disease based on convolutional neural network and transfer learning","volume":"34","author":"M. Long","year":"2018","journal-title":"Transactions of the Chinese Society of Agricultural Engineering"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-27863-6_59"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2019.00272"},{"key":"34","article-title":"Very deep convolutional networks for large-scale image recognition","author":"K. Simonyan"},{"key":"35","first-page":"770","article-title":"Deep residual learning for image recognition","author":"K. M. He"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/3289801"},{"key":"37","first-page":"1","article-title":"Going deeper with convolutions","author":"C. Szegedy"},{"issue":"1","key":"38","first-page":"90","article-title":"Image recognition of rice sheath blight based on convolution neural network","author":"T. Liu","year":"2019","journal-title":"China rice science"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.12133\/j.smartag.2019.1.2.201812-SA007"},{"issue":"3","key":"40","first-page":"137","article-title":"A wheat disease identification method based on convolutional neural network","volume":"50","author":"H. Zhang","year":"2018","journal-title":"Shandong Agricultural Science"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106367"},{"key":"42","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3057659"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2022\/6342357.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2022\/6342357.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2022\/6342357.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T18:06:39Z","timestamp":1649786799000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jece\/2022\/6342357\/"}},"subtitle":[],"editor":[{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,4,12]]},"references-count":42,"alternative-id":["6342357","6342357"],"URL":"https:\/\/doi.org\/10.1155\/2022\/6342357","relation":{},"ISSN":["2090-0155","2090-0147"],"issn-type":[{"value":"2090-0155","type":"electronic"},{"value":"2090-0147","type":"print"}],"subject":[],"published":{"date-parts":[[2022,4,12]]}}}