{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T22:43:59Z","timestamp":1769640239970,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T00:00:00Z","timestamp":1674950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R &amp; D plan of Heilongjiang Province","award":["GZ20210103"],"award-info":[{"award-number":["GZ20210103"]}]},{"name":"Key R &amp; D plan of Heilongjiang Province","award":["2021ZD0110904"],"award-info":[{"award-number":["2021ZD0110904"]}]},{"name":"Science and technology innovation 2030\u2014\u201cnew generation artificial intelligence\u201d major project","award":["GZ20210103"],"award-info":[{"award-number":["GZ20210103"]}]},{"name":"Science and technology innovation 2030\u2014\u201cnew generation artificial intelligence\u201d major project","award":["2021ZD0110904"],"award-info":[{"award-number":["2021ZD0110904"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Corn diseases are one of the significant constraints to high\u2013quality corn production, and accurate identification of corn diseases is of great importance for precise disease control. Corn anthracnose and brown spot are typical diseases of corn, and the early symptoms of the two diseases are similar, which can be easily misidentified by the naked eye. In this paper, to address the above problems, a three\u2013dimensional\u2013two\u2013dimensional (3D\u20132D) hybrid convolutional neural network (CNN) model combining a band selection module is proposed based on hyperspectral image data, which combines band selection, attention mechanism, spatial\u2013spectral feature extraction, and classification into a unified optimization process. The model first inputs hyperspectral images to both the band selection module and the attention mechanism module and then sums the outputs of the two modules as inputs to a 3D\u20132D hybrid CNN, resulting in a Y\u2013shaped architecture named Y\u2013Net. The results show that the spectral bands selected by the band selection module of Y\u2013Net achieve more reliable classification performance than traditional feature selection methods. Y\u2013Net obtained the best classification accuracy compared to support vector machines, one\u2013dimensional (1D) CNNs, and two\u2013dimensional (2D) CNNs. After the network pruned the trained Y\u2013Net, the model size was reduced to one\u2013third of the original size, and the accuracy rate reached 98.34%. The study results can provide new ideas and references for disease identification of corn and other crops.<\/jats:p>","DOI":"10.3390\/s23031494","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T02:28:34Z","timestamp":1675045714000},"page":"1494","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Y\u2013Net: Identification of Typical Diseases of Corn Leaves Using a 3D\u20132D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module"],"prefix":"10.3390","volume":"23","author":[{"given":"Yinjiang","family":"Jia","sequence":"first","affiliation":[{"name":"College of Electrical and Information, Northeast Agricultural University, Harbin 150006, China"}]},{"given":"Yaoyao","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Electrical and Information, Northeast Agricultural University, Harbin 150006, China"}]},{"given":"Jiaqi","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Electrical and Information, Northeast Agricultural University, Harbin 150006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1842-5210","authenticated-orcid":false,"given":"Hongmin","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Electrical and Information, Northeast Agricultural University, Harbin 150006, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,29]]},"reference":[{"key":"ref_1","first-page":"483","article-title":"Simulink Platform in Video Image Real-time Diagnosis of Maize Disease","volume":"39","author":"Meng","year":"2017","journal-title":"J. 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