{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:06:07Z","timestamp":1776276367593,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,9,18]],"date-time":"2022-09-18T00:00:00Z","timestamp":1663459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Anhui Province, China","award":["1808085MF183"],"award-info":[{"award-number":["1808085MF183"]}]},{"name":"Natural Science Foundation of Anhui Province, China","award":["gxbjZD2020079"],"award-info":[{"award-number":["gxbjZD2020079"]}]},{"name":"Anhui University Top-notch Talents Academic Funding Project","award":["1808085MF183"],"award-info":[{"award-number":["1808085MF183"]}]},{"name":"Anhui University Top-notch Talents Academic Funding Project","award":["gxbjZD2020079"],"award-info":[{"award-number":["gxbjZD2020079"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>To extract more accurate and abundant features of corn disease and solve the problems of rough classification and low recognition accuracy, the attention mechanism is introduced into the field of corn disease recognition. The corn disease recognition model (AT-AlexNet) is proposed based on an attention mechanism. The network was based on AlexNet, and the new down-sampling attention module was constructed to enhance the foreground response of the disease; the Mish activation function was introduced to improve the nonlinear expression of the network; the new module of the full connection layer was designed to reduce the network parameters. In the experiment of the enhanced corn disease datasets, the average recognition accuracy of the attention-based network model AT-AlexNet is 99.35%. The recognition accuracy of using the Mish activation function is 0.65% higher than that of the ReLu activation function. The experiments show that compared with other identification methods, the proposed method has better classification performance for corn diseases.<\/jats:p>","DOI":"10.3390\/axioms11090480","type":"journal-article","created":{"date-parts":[[2022,9,18]],"date-time":"2022-09-18T22:12:43Z","timestamp":1663539163000},"page":"480","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Corn Disease Recognition Based on Attention Mechanism Network"],"prefix":"10.3390","volume":"11","author":[{"given":"Yingying","family":"Wang","sequence":"first","affiliation":[{"name":"College of Electrical and Electronic Engineering, Anhui Science and Technology University, Bengbu 233030, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1066-1809","authenticated-orcid":false,"given":"Jin","family":"Tao","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin 300350, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3192-5183","authenticated-orcid":false,"given":"Haitao","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Electrical and Electronic Engineering, Anhui Science and Technology University, Bengbu 233030, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104414","DOI":"10.1016\/j.dib.2019.104414","article-title":"RoCoLe: A robusta coffee leaf images dataset for evaluation of machine learning based methods in plant diseases recognition","volume":"25","author":"Cusme","year":"2019","journal-title":"Data Brief"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"542","DOI":"10.3390\/agriengineering3030035","article-title":"Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods","volume":"3","author":"Tan","year":"2021","journal-title":"AgriEngineering"},{"key":"ref_3","first-page":"3257","article-title":"Bridge apparent damage detection based on the improved YOLO v3 in complex background","volume":"18","author":"Zou","year":"2021","journal-title":"J. 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