{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T13:50:44Z","timestamp":1775137844827,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T00:00:00Z","timestamp":1636502400000},"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":["(31860477"],"award-info":[{"award-number":["(31860477"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["M2042001"],"award-info":[{"award-number":["M2042001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China agriculture research system","award":["CARS-29-bc-3"],"award-info":[{"award-number":["CARS-29-bc-3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Due to the benefits of convolutional neural networks (CNNs) in image classification, they have been extensively used in the computerized classification and focus of crop pests. The intention of the current find out about is to advance a deep convolutional neural network to mechanically identify 14 species of tea pests that possess symmetry properties. (1) As there are not enough tea pests images in the network to train the deep convolutional neural network, we proposes to classify tea pests images by fine-tuning the VGGNET-16 deep convolutional neural network. (2) Through comparison with traditional machine learning algorithms Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), the performance of our method is evaluated (3) The three methods can identify tea tree pests well: the proposed convolutional neural network classification has accuracy up to 97.75%, while MLP and SVM have accuracies of 76.07% and 68.81%, respectively. Our proposed method performs the best of the assessed recognition algorithms. The experimental results also show that the fine-tuning method is a very powerful and efficient tool for small datasets in practical problems.<\/jats:p>","DOI":"10.3390\/sym13112140","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T23:07:21Z","timestamp":1636672041000},"page":"2140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0521-9709","authenticated-orcid":false,"given":"Jing","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Plant Pathology, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Department of Plant Biosafety, College of Plant Protection, China Agricultural University, Beijing 100193, China"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China"}]},{"given":"Lingwang","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Plant Biosafety, College of Plant Protection, China Agricultural University, Beijing 100193, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3459","DOI":"10.1002\/jsfa.9564","article-title":"Tea Diseases Detection Based on Fast Infrared Thermal Image Processing Technology","volume":"99","author":"Yang","year":"2019","journal-title":"J. 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