{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:21:05Z","timestamp":1792794065139,"version":"4.5.0"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:00:00Z","timestamp":1775865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Shandong Province Natural Science Foundation","doi-asserted-by":"crossref","award":["ZR2023MF048"],"award-info":[{"award-number":["ZR2023MF048"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Province Natural Science Foundation","doi-asserted-by":"crossref","award":["ZR2023QC116"],"award-info":[{"award-number":["ZR2023QC116"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Province Natural Science Foundation","doi-asserted-by":"crossref","award":["ZR2021QC173"],"award-info":[{"award-number":["ZR2021QC173"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key R&D Program of Shandong Province, China","award":["2024RZB0206"],"award-info":[{"award-number":["2024RZB0206"]}]},{"name":"Disciplinary Construction Funds of Weifang University of Science and Technology","award":["2018RC002"],"award-info":[{"award-number":["2018RC002"]}]},{"name":"Weifang Soft Science Project","award":["2023RKX184"],"award-info":[{"award-number":["2023RKX184"]}]},{"name":"Weifang City Science and Technology Development Plan Project","award":["2023GX051"],"award-info":[{"award-number":["2023GX051"]}]},{"name":"Weifang City Science and Technology Development Plan Project","award":["2023JH14"],"award-info":[{"award-number":["2023JH14"]}]},{"name":"Weifang City Science and Technology Development Plan Project","award":["2024GX033"],"award-info":[{"award-number":["2024GX033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>Accurate vegetable disease detection in complex cultivation environments remains challenging because early lesions are often small, low-contrast, and easily confounded by cluttered backgrounds. To address this issue, we propose VDD-Net, a feature-enhanced detection network based on YOLOv10 for robust vegetable disease detection in protected agriculture. The proposed framework integrates three modules: a receptive field enhancement (RFE) module to improve local perception of small lesions, an adaptive channel fusion (ACF) module to strengthen multi-scale feature aggregation and suppress background interference, and a global context attention (GCA) module to capture long-range dependencies and improve contextual discrimination. Experiments on a custom vegetable disease dataset showed that VDD-Net achieved an mAP@0.5 of 95.2% with only 7.78 M parameters. To further evaluate robustness, zero-shot cross-domain testing was conducted on the PlantDoc dataset, where VDD-Net achieved an mAP@0.5 of 76.5%, outperforming the baseline and showing improved generalization to natural scenes. In addition, after TensorRT optimization and FP16 quantization, the model maintained real-time inference on edge platforms, reaching 89.3 FPS on Jetson AGX Orin and 24.2 FPS on Jetson Nano. These results indicate that VDD-Net provides a practical balance among detection accuracy, cross-domain robustness, and deployment efficiency for intelligent disease monitoring in modern agriculture.<\/jats:p>","DOI":"10.3390\/plants15081182","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T07:29:29Z","timestamp":1776065369000},"page":"1182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Feature-Enhanced Network for Vegetable Disease Detection in Complex Environments"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3155-4623","authenticated-orcid":false,"given":"Xuewei","family":"Wang","sequence":"first","affiliation":[{"name":"Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang 262700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8769-5981","authenticated-orcid":false,"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang 262700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109672","DOI":"10.1016\/j.compag.2024.109672","article-title":"Sustainable smart system for vegetables plant disease detection: Four vegetable case studies","volume":"227","author":"Ali","year":"2024","journal-title":"Comput. 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