{"id":9736,"date":"2026-04-02T13:05:27","date_gmt":"2026-04-02T12:05:27","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=9736"},"modified":"2026-06-15T12:16:14","modified_gmt":"2026-06-15T11:16:14","slug":"25-5-4-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/25-5-4-en\/","title":{"rendered":"25.5.4 En"},"content":{"rendered":"\n<h1 class=\"wp-block-heading has-lora-font-family\" style=\"font-size:22px\"><strong>The use of convolutional neural networks for automatic diseases detection tasks<\/strong><\/h1>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-4810ec25784c525ea37514c76d57af1f wp-block-paragraph\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\"><strong>M.A. Shereuzheva, M.A. Shereuzhev, Z.M. Albekova<\/strong><\/p>\n\n\n\n<div class=\"wp-block-group is-vertical is-content-justification-left is-nowrap is-layout-flex wp-container-core-group-is-layout-20193d73 wp-block-group-is-layout-flex\" style=\"border-style:none;border-width:0px;margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\">\n<p class=\"has-text-color has-link-color has-lora-font-family wp-elements-1934a6274ad67c2dd8d97ce7471e2bbb wp-block-paragraph\" style=\"color:#5b1919;font-size:12px;text-decoration:underline\"><\/p>\n\n\n\n<div class=\"wp-block-group is-horizontal is-layout-flex wp-container-core-group-is-layout-9076828a wp-block-group-is-layout-flex\" style=\"min-height:0px;margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\">\n<div class=\"wp-block-buttons is-content-justification-left is-layout-flex wp-container-core-buttons-is-layout-856cf56e wp-block-buttons-is-layout-flex\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-100 is-style-outline is-style-outline--1\"><a class=\"wp-block-button__link has-background-background-color has-text-color has-background has-link-color has-border-color has-custom-font-size wp-element-button\" href=\"http:\/\/izvestiyakbncran.ru\/wp-content\/uploads\/2026\/06\/4-sher.pdf\" style=\"border-color:#5b1919;border-style:solid;border-width:2px;border-radius:8px;color:#5b1919;padding-top:0.4rem;padding-right:var(--wp--preset--spacing--40);padding-bottom:0.4rem;padding-left:var(--wp--preset--spacing--40);font-size:12px\">PDF<\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:0px;width:0px\" aria-hidden=\"true\" class=\"wp-block-spacer wp-container-content-273e683f\"><\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-100 is-style-outline is-style-outline--2\"><a class=\"wp-block-button__link has-background-background-color has-text-color has-background has-link-color has-border-color has-text-align-center has-custom-font-size wp-element-button\" href=\"http:\/\/izvestiyakbncran.ru\/wp-content\/uploads\/2026\/06\/4.-sher.xml\" style=\"border-color:#5b1919;border-width:2px;border-top-left-radius:8px;border-top-right-radius:8px;border-bottom-left-radius:8px;border-bottom-right-radius:8px;color:#5b1919;padding-top:0.4rem;padding-right:var(--wp--preset--spacing--40);padding-bottom:0.4rem;padding-left:var(--wp--preset--spacing--40);font-size:12px\">JATS XML<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<p class=\"has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-41ee428b6a5740c7f514a7432ff786a3 wp-block-paragraph\" style=\"border-style:none;border-width:0px;border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-left-radius:0px;border-bottom-right-radius:0px;color:#5b1919;margin-top:0;margin-right:0;margin-bottom:0;margin-left:0;padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\"><\/p>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:var(--wp--preset--spacing--20)\"\/>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-a6626a83fc0142021cf58722c8180a4e wp-block-paragraph\" style=\"line-height:1.4\"><em><strong><strong>Abstract<\/strong>:<\/strong> <\/em>This article provides an overview of existing convolutional neural network architectures and their application in the classification task for detecting diseases of fruits and plants. Diseases of plants and fruits are a serious problem in agriculture and horticulture, and their early detection can help in taking timely measures to prevent the spread and minimize damage. The results of the study can be useful for the development of automated systems for detecting diseases of fruits and plants, which helps to increase yields.<\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-df03325adc83c022634de6b79d43432f wp-block-paragraph\" style=\"line-height:1.4\"><\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-c6210d57bf329e354467734b2e304f82 wp-block-paragraph\" style=\"line-height:1.4\"><strong><em><strong>Keywords<\/strong>:<\/em><\/strong> neural networks, machine learning, convolutional network architecture, computer vision, image classification<\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-df03325adc83c022634de6b79d43432f wp-block-paragraph\" style=\"line-height:1.4\"><\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family wp-elements-eddd45c97653a0a1e05193424fce8191 wp-block-paragraph\" style=\"font-size:12px;line-height:1.4\"><strong><strong>For citation<\/strong>.<\/strong> Shereuzheva M.A., Shereuzhev M.A., Albekova Z.M. The use of convolutional neural networks for automatic diseases detection tasks. News of the Kabardino-Balkarian Scientific Center of RAS. 2023. No. 5(115). Pp. 41\u201351. DOI: 10.35330\/1991-6639-2023-5-115-41-51<\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family wp-elements-17c3333a0b1db39b7fa4a3e1a1572bc4 wp-block-paragraph\" style=\"font-size:12px;line-height:1.4\"><\/p>\n\n\n\n<details class=\"wp-block-details has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-abef0f104856141a84e166710e98b47b is-layout-flow wp-container-core-details-is-layout-f488f964 wp-block-details-is-layout-flow\" style=\"font-style:normal;font-weight:700;line-height:1.5\"><summary><strong>R<\/strong>eferences<\/summary>\n<ol style=\"margin-top:0;margin-bottom:0\" class=\"wp-block-list\">\n<li style=\"font-style:normal;font-weight:400\">Digital agriculture [\u042d\u043b\u0435\u043a\u0442\u0440\u043e\u043d\u043d\u044b\u0439 \u0440\u0435\u0441\u0443\u0440\u0441]. URL: https:\/\/en.wikipedia.org\/wiki\/Digital_agriculture.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Shereuzheva M.A., Shereuzhev M.A. Development of expert systems to improve the efficiency of growing plants in agriculture. <em>News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. 2022. No. 5(109). Pp. 93\u2013104. DOI: 10.35330\/1991-6639-2022-5-109-93-104. (In Russian)<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Nagoev Z.V., Shuganov V.M., Bzhikhatlov K.Ch. et al. Prospects for increasing the productivity and efficiency of agricultural production with the use of an intelligent integrated environment. <em>News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. 2021. No. 6(104). Pp. 155\u2013165. DOI: 10.35330\/1991-6639-2021-6-104-155-165. (In Russian)<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Krizhevsky A., Sutskever I., Hinton G.E. ImageNet classification with deep convolutional neural networks. <em>Communications of the ACM<\/em>. 2012. 60(6). Pp. 84\u201390. <\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Pushkarev A., Yakubailik O. A web application for visualization, analysis, and processing of agricultural monitoring spatial-temporal data. <em>CEUR Workshop Proceedings<\/em>. 2021. Vol. 3006. Pp. 231\u2013237. URL: http:\/\/ceur-ws.org\/Vol-006\/27_short_paper.pdf<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Skvortsov E.A., Skvortsova E.G., Sandu I.S. et al. Transition of Agriculture to Digital, Intellectual and Robotics Technologies. <em>Ekonomika regiona<\/em> [Economy of Region]. 2018. Vol. 14. No. 3. Pp. 1014\u20131028. (In Russian)<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">He K. et al. Deep residual learning for image recognition. <em>Proceedings of the IEEE conference on computer vision and pattern recognition<\/em>. 2016. Pp. 770\u2013778.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Iandola F. et al. Densenet. Implementing efficient convnet descriptor pyramids. arXiv preprint arXiv. 2014. Pp. 1404\u20131869.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Huang G., Liu Z., Van Der Maaten et al. \u0421onnected convolutional networks. <em>Proceedings of the IEEE conference on computer vision and pattern recognition<\/em>. 2017. Pp. 4700\u20134708.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Sandler M., Howard A., Zhu M. et al. MobileNetV2. Inverted residuals and linear bottlenecks. <em>Proceedings of the IEEE conference on computer vision and pattern recognition<\/em>. Pp. 4510\u20134520.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Szegedy C. et al. Inception-v4, inception-resnet and the impact of residual connections on learning. <em>Thirty-first AAAI conference on artificial intelligence<\/em>. 2017.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Tan M., Le Q. Efficientnet. Rethinking model scaling for convolutional neural networks. <em>International conference on machine learning. PMLR<\/em>. 2019. Pp. 6105\u20136114.<\/li>\n<\/ol>\n<\/details>\n\n\n\n<details class=\"wp-block-details has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-4e8794aca9488aff76668d770f2b42b3 is-layout-flow wp-container-core-details-is-layout-9ff6af70 wp-block-details-is-layout-flow\" style=\"font-style:normal;font-weight:700;line-height:1.5\"><summary><strong>Information about the authors<\/strong><\/summary>\n<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-1c18c512 wp-block-group-is-layout-flex\" style=\"min-height:0px;margin-top:0;margin-bottom:0;padding-top:var(--wp--preset--spacing--20);padding-right:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--20);padding-left:var(--wp--preset--spacing--40)\">\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><strong>Shereuzheva Milana Arturovna<\/strong>, master, Department \u201cInformation technologies and computing systems\u201d, Moscow Technical University \u201cSTANKIN\u201d;<br>127055, Russia, Moscow, 1 Vadkovsky lane;<br>trainee researcher of the Laboratory \u201cIntellectual Habitats\u201d of the Institute of Computer Science and Problems of Regional Management \u2013 branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;<br>360000, Russia, Nalchik, 37-a I. Armand street;<br>milana.shereuzheva@mail.ru, ORCID: https:\/\/orcid.org\/0000-0002-6668-4703<br><strong>Shereuzhev Madin Arturovich<\/strong>, senior lecturer, Department \u201cRobotic systems and mechatronics\u201d, Moscow State Technical University named after N.E. Bauman;<br>105005, Russia, Moscow, build. 5 corps 1 Baumanskaya street;<br>shereuzhev@bmstu.ru, ORCID: https:\/\/orcid.org\/0000-0003-2352-992\u0425<br><strong>Albekova Zamira Mukhamedalievna<\/strong>, Candidate of Pedagogical Sciences, Associate Professor, Department of Information Systems and Technologies, Institute of Digital Development, North Caucasus Federal University;<br>350029, Russia, Stavropol, 2 Kulakov avenue;<br>zalbekova@ncfu.ru, ORCID: https:\/\/orcid.org\/0000-0002-7214-8114<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-style:normal;font-weight:400\"><\/p>\n<\/div>\n<\/details>\n","protected":false},"excerpt":{"rendered":"<p>The use of convolutional neural networks for automatic diseases detection tasks M.A. Shereuzheva, M.A. Shereuzhev, Z.M. Albekova Abstract: This article provides an overview of existing convolutional neural network architectures and their application in the classification task for detecting diseases of fruits and plants. Diseases of plants and fruits are a serious problem in agriculture and [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-9736","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>25.5.4 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/25-5-4-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"25.5.4 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"The use of convolutional neural networks for automatic diseases detection tasks M.A. Shereuzheva, M.A. Shereuzhev, Z.M. 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