{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T04:41:02Z","timestamp":1776746462670,"version":"3.51.2"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":109,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Apple leaf diseases critically affect fruit yield and quality. Rapid and accurate field diagnosis remains challenging due to variable lighting, complex backgrounds, and environmental noise. We propose a robust hybrid deep learning framework that effectively combines convolutional neural networks for fine\u2010grained local feature extraction with vision transformers for capturing global contextual dependencies and further refines them through a dual\u2010stage attention mechanism. It achieved state\u2010of\u2010the\u2010art performance, with 98.94% accuracy on the ALDD, 99.93% accuracy on the plant pathology benchmark, and a mean F1\u2010score of 0.992. Paired\n                    <jats:italic>t<\/jats:italic>\n                    \u2010test analysis confirmed significant improvements over conventional CNN and transformer models. These results demonstrate the framework\u2019s reliability, scalability, and potential for real\u2010time orchard disease monitoring, enabling precision management and sustainable apple production.\n                  <\/jats:p>","DOI":"10.1155\/acis\/9966874","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T04:03:48Z","timestamp":1776744228000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Attention\u2010Enhanced Hybrid CNN\u2013Transformer Approach Using ResNeXt\u201050 and Swin Transformer for Apple Leaf Disease Classification"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7887-7863","authenticated-orcid":false,"given":"Vishal","family":"Thakur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4063-7893","authenticated-orcid":false,"given":"Ravindara","family":"Bhatt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8625-0462","authenticated-orcid":false,"given":"R. 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M.","year":"2025","journal-title":"AgriEngineering"},{"key":"e_1_2_11_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10341-024-01239-w"},{"key":"e_1_2_11_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10341-025-01384-w"},{"key":"e_1_2_11_19_2","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.2655"},{"key":"e_1_2_11_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-10816-9"},{"key":"e_1_2_11_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-10774-2"},{"key":"e_1_2_11_22_2","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy15051246"},{"key":"e_1_2_11_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11104-024-07017-7"},{"key":"e_1_2_11_24_2","unstructured":"DharS. Apple Leaf Disease Classification Dataset 2024 https:\/\/www.kaggle.com\/datasets\/showravdhar\/apple-disease-dataset."},{"key":"e_1_2_11_25_2","volume-title":"AppleLeaf9-Enhanced Edition (AL9EE) Dataset","author":"Zhu Z.","year":"2023"},{"key":"e_1_2_11_26_2","article-title":"A Novel Approach for Classification and Detection of Apple Leaf Disease Using Enhanced RBVT-Net With Transfer Learning and Yolov7","volume":"13","author":"Kumar S.","year":"2025","journal-title":"IEEE Access"},{"key":"e_1_2_11_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2023.07.076"},{"key":"e_1_2_11_28_2","doi-asserted-by":"crossref","unstructured":"ThakurP. S. KhannaP. SheoreyT. andOjhaA. 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