{"id":234189,"date":"2025-11-27T09:52:12","date_gmt":"2025-11-27T08:52:12","guid":{"rendered":"https:\/\/www.alimentibevande.it\/sponsored-contents\/analisi-comparativa-di-modelli-di-apprendimento-profondo-pre-addestrati-per-la-classificazione-dei-chicchi-di-caffe\/"},"modified":"2026-02-18T11:38:35","modified_gmt":"2026-02-18T10:38:35","slug":"coffee-deep-learning-models-for-classification","status":"publish","type":"post","link":"https:\/\/www.alimentibevande.it\/en\/scientific-studies\/coffee-deep-learning-models-for-classification\/","title":{"rendered":"Coffee, deep learning models for classification"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\"><figure><img fetchpriority=\"high\" decoding=\"async\" width=\"428\" height=\"285\" class=\"alignright wp-image-29487\" src=\"https:\/\/static.tecnichenuove.it\/macchinealimentari\/2025\/11\/Fotolia_69050964_M.jpg\" alt=\"\"><\/figure><\/h4>\n\n\n\n<p>Coffee production can be challenged by fluctuations in global commodity prices, impacting the economic stability of some countries that rely heavily on coffee production.<\/p>\n\n\n\n<p>Therefore, the objective of a recent <strong>study <\/strong>by an Egyptian researcher [<strong>Hassan, 2024<\/strong>] was to evaluate the effectiveness of several <strong>pre-trained deep learning models<\/strong> (including AlexNet, LeNet, HRNet, Google Net, Mobile V2 Net, ResNet (50), VGG, Efficient, Darknet, and DenseNet) in classifying different coffee types.<\/p>\n\n\n\n<p>The <strong>results <\/strong>show that some of these models exhibit higher accuracy and faster convergence than others. Specifically, the <strong>comparative analysis<\/strong> was performed using key evaluation metrics such as sensitivity, specificity, precision, negative predictive value, accuracy, and F1-Score.<\/p>\n\n\n\n<p>According to the author, the strategic use of <strong>transfer learning<\/strong> and <strong>model fine-tuning <\/strong>not only improves the <strong>accuracy of coffee classification<\/strong> but also helps address the <strong>economic challenges<\/strong> associated with global price fluctuations in this industry. Further research is needed, however, to validate the tool directly online, resulting in improved efficiency in coffee bean sorting and categorization.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Riferimenti bibliografici: <\/strong>Hassan, <em>Neural Computing and Applications<\/em>, 36, 2024, 9023\u20139052.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A study evaluated the effectiveness of several pre-trained deep learning models<\/p>\n","protected":false},"author":3806,"featured_media":232223,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[20580],"tags":[20899,20617],"tipologia":[],"coauthors":[17168],"class_list":["post-234189","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-scientific-studies","tag-coffee","tag-macchine-alimentari"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Coffee, deep learning models for classification - Alimentibevande.it<\/title>\n<meta name=\"description\" content=\"A study evaluated the effectiveness of several pre-trained deep learning models\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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