{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T02:16:51Z","timestamp":1777861011026,"version":"3.51.4"},"reference-count":34,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["25NSFSC0456"],"award-info":[{"award-number":["25NSFSC0456"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>This paper proposes ShiftingNet, a lightweight classification model based on the improved EfficientNetV2 architecture, which embeds the Channel\u2010wise Feature Shifting (CFS) operation. Designed as an efficient diagnostic component for agricultural expert systems, ShiftingNet aims to automate classification performance for diverse crop leaf diseases under challenging agricultural conditions. To address the limitations in balancing global and local feature representations and cross\u2010environment generalisation, we design two novel modules: Channel\u2010wise Feature Shifting Convolution (CFSConv) and Fused Channel\u2010wise Feature Shifting Convolution (Fused\u2010CFSConv). These modules integrate the CFS residual connection and DropPath regularisation into the original MBConv and Fused\u2010MBConv, while introducing the Squeeze\u2010and\u2010Excitation (SE) and Coordinate Attention (CA) mechanisms, respectively. We construct a multi\u2010source corn dataset, MixCorn, which fuses PlantVillage laboratory images, PlantDoc network images, and CD&amp;S field samples, covering different illumination conditions, backgrounds, and disease scales. Experiments show that ShiftingNet achieves classification accuracies of 99.84% and 99.08% on the PlantVillage and MixCorn datasets, respectively, with only 9.92\u2009M parameters. This demonstrates advantages in knowledge acquisition efficiency and computational cost, providing a theoretical foundation for constructing resource\u2010constrained mobile expert systems. Robustness evaluation under common perturbations and Grad\u2010CAM\u2010based interpretability analysis further validate the model's reliability for automated decision\u2010making. Ablation studies further confirm that the CFS operation improves feature representation and classification performance.<\/jats:p>","DOI":"10.1111\/exsy.70242","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T10:20:47Z","timestamp":1773915647000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>ShiftingNet<\/scp>\n                    : Lightweight Crop Leaf Disease Classification Model With Channel\u2010Wise Feature Shifting"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3927-7616","authenticated-orcid":false,"given":"Dongen","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer and Software, Nanyang Institute of Technology  Nanyang China"}]},{"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanyang Institute of Technology  Nanyang China"}]},{"given":"Linbo","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanyang Institute of Technology  Nanyang China"}]},{"given":"Penghua","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanyang Institute of Technology  Nanyang China"}]},{"given":"Ziqi","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanyang Institute of Technology  Nanyang China"}]},{"given":"Liming","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science, China West Normal University  Nanchong China"}]}],"member":"311","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"e_1_2_11_2_1","article-title":"Cd&s Dataset: Handheld Imagery Dataset Acquired Under Field Conditions for Corn Disease Identification and Severity Estimation","author":"Ahmad A.","year":"2021","journal-title":"arXiv Preprint arXiv:2110.12084"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-021-00536-1"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inpa.2024.03.002"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2025.100813"},{"key":"e_1_2_11_6_1","doi-asserted-by":"crossref","unstructured":"Chen W. D.Xie Y.Zhang andS.Pu.2019.All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification. In Proceedings of the Ieee\/Cvf Conference on Computer Vision and Pattern Recognition (pp. 7241\u20137250).","DOI":"10.1109\/CVPR.2019.00741"},{"key":"e_1_2_11_7_1","doi-asserted-by":"crossref","unstructured":"Chollet F.2017.Xception: Deep Learning With Depthwise Separable Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1251\u20131258).","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3216198"},{"key":"e_1_2_11_9_1","article-title":"An Image Is Worth 16\u00d716 Words: Transformers for Image Recognition at Scale","author":"Dosovitskiy A.","year":"2020","journal-title":"arXiv Preprint arXiv:2010.11929"},{"key":"e_1_2_11_10_1","doi-asserted-by":"crossref","unstructured":"Fan L. 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