{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T09:56:48Z","timestamp":1781690208803,"version":"3.54.5"},"reference-count":48,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"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":["Expert Systems"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Accurate and interpretable intelligent expert systems are essential for early plant disease diagnosis and precision agricultural management. However, existing diagnostic systems struggle to identify subtle lesions and lack sufficient interpretability, often functioning as \u201cblack\u2010box\u201d classifiers rather than integrated computational cores. This paper proposes PCSAM\u2010YOLO, a lightweight visual inference architecture based on YOLO11, designed as the computational engine for intelligent plant disease diagnostic systems. Unlike conventional attention\u2010based models that treat spatial and channel features in isolation, PCSAM\u2010YOLO integrates Pinwheel Convolution (PConv) and a Channel Similarity Attention Mechanism (CSAM) to achieve synergistic dual\u2010domain feature refinement. Specifically, PConv effectively expands the receptive field to aggregate directional spatial context, providing a robust structural foundation for feature extraction. Building upon this, CSAM implements second\u2010order channel interaction modelling to capture fine\u2010grained inter\u2010channel correlations, significantly enhancing the model's sensitivity to subtle pathological features. Evaluated on the WCG and Paddy Doctor datasets, the proposed architecture achieves state\u2010of\u2010the\u2010art performance (97.29% Accuracy, 96.84% Precision, 95.91% Recall, and 96.35% F1\u2010Score). Visual analysis confirms that the integration of PConv and CSAM enables the model to accurately focus on lesion regions, aligning with the diagnostic logic of agricultural experts. Rather than serving only as a standalone classifier, PCSAM\u2010YOLO functions as a high\u2010performance inference core capable of supporting downstream decision\u2010making and automated management recommendations in complex agricultural IoT environments.<\/jats:p>","DOI":"10.1111\/exsy.70326","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T13:19:41Z","timestamp":1780665581000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Lightweight Intelligent Vision Architecture With Receptive Field Enhancement and Channel Similarity Attention for Plant Disease Diagnosis"],"prefix":"10.1111","volume":"43","author":[{"given":"Xinjian","family":"Xiang","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering, Zhejiang University of Science and Technology  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3781-6291","authenticated-orcid":false,"given":"Haoyu","family":"Pei","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Zhejiang University of Science and Technology  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongping","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Zhejiang University of Science and Technology  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dianzheng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Zhejiang University of Science and Technology  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-021-09054-2"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.3923\/itj.2011.267.275"},{"issue":"1","key":"e_1_2_11_4_1","first-page":"211","article-title":"Detection of Unhealthy Region of Plant Leaves and Classification of Plant Leaf Diseases Using Texture Features","volume":"15","author":"Arivazhagan S.","year":"2013","journal-title":"Agricultural Engineering International: CIGR Journal"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystemseng.2018.05.013"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-15163-0"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/08839514.2017.1315516"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11060951"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3609839"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.18699\/VJGB-22-25"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/1426\/1\/012003"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41348-024-00915-z"},{"key":"e_1_2_11_13_1","unstructured":"Dosovitskiy A.2020.An Image Is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv Preprint arXiv:2010.11929."},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.01.009"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1712\/1\/012012"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2016.07.003"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12501"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40031-024-01062-7"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.02.016"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-27863-6_59"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3505244"},{"key":"e_1_2_11_22_1","unstructured":"Khanam R. andM.Hussain.2024.Yolov11: An Overview of the Key Architectural Enhancements. arXiv Preprint arXiv:2410.17725."},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_11_24_1","article-title":"Pmvt: A Lightweight Vision Transformer for Plant Disease Identification on Mobile Devices, Frontiers","volume":"14","author":"Li G.","year":"2023","journal-title":"Plant Science"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.70059"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.08.048"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3054561"},{"key":"e_1_2_11_30_1","doi-asserted-by":"crossref","unstructured":"Mehdipour S. 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