{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T09:24:15Z","timestamp":1787736255019,"version":"build-2784847793"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of Maize Multi-Part Disease Recognition Based on Edge Computing","award":["XJAIEC2026K008"],"award-info":[{"award-number":["XJAIEC2026K008"]}]},{"name":"Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region","award":["2022A02011"],"award-info":[{"award-number":["2022A02011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Maize diseases affecting leaves, stalks, and ears can substantially reduce yield and quality; therefore, rapid and accurate recognition in complex field environments is important for intelligent agricultural monitoring. To address the large-scale variation, weak fine-grained texture, and strong background interference associated with multi-part maize diseases, this study proposes YOLOv11-MPD (YOLOv11 for Maize Multi-Part Disease Detection), a maize disease detection algorithm based on YOLOv11n. The method jointly improves spatial position awareness, shallow detail preservation, local-context modeling, key semantic-region enhancement, and lightweight detection-head reconstruction. RFCAConv, C3k2_RFCAConv, and Detect_LSDECD are introduced into the baseline network to strengthen directional texture modeling, multi-scale feature aggregation, and detection-head feature representation. FG-RFCAConv, HGD-C3k2, LCA-C3k2, and GRN-BiAttn are further designed for high-frequency differential gated detail compensation, P3 high-resolution detail enhancement, local-context fusion, and global-response-normalized attention regulation, respectively. Experimental results show that YOLOv11-MPD achieves Precision, Recall, mAP50, and mAP50-95 of 72.3%, 72.8%, 79.5%, and 50.2%, improving YOLOv11n by 2.4, 2.5, 2.9, and 2.4 percentage points, respectively, while reducing parameters from 2.6 M to 2.4 M. These results indicate that, within the scope of the dataset used in this study, YOLOv11-MPD improves multi-part maize disease detection under complex field conditions. However, the current conclusions are limited to the constructed dataset, and further validation using larger multi-region, multi-season, and multi-device datasets is required to evaluate its broader generalization ability.<\/jats:p>","DOI":"10.3390\/s26175383","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T09:05:46Z","timestamp":1787735146000},"page":"5383","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLOv11-MPD: A Multi-Part Maize Disease Detection Algorithm for Complex Field Environments"],"prefix":"10.3390","volume":"26","author":[{"given":"Rui","family":"Dong","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Xinjiang Engineering Technology Research Center for Agricultural Informatization, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longtao","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Xinjiang Engineering Technology Research Center for Agricultural Informatization, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaozhao","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Xinjiang Engineering Technology Research Center for Agricultural Informatization, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"ref_1","first-page":"15","article-title":"Research progress of crop leaf disease detection based on convolutional neural networks","volume":"41","author":"Zhu","year":"2025","journal-title":"Trans. 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