{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T07:16:26Z","timestamp":1788419786324,"version":"build-2803163510"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>Achieving high-precision monitoring of wheat nitrogen levels is essential for boosting yield and grain quality, yet the remote sensing community has largely confined itself to single-modal data sources, missing the synergistic benefits of multimodal fusion. To address this, we propose a multimodal feature fusion network that simultaneously utilizes RGB, multispectral, and vegetation index data. The network employs dual-branch encoders to extract modality\u2011specific features from fused images composed of NDVI, green, and near\u2011infrared bands; a deformable bidirectional cross\u2011attention module aligns pixel\u2011level features and enhances cross\u2011modal interactions; a modality\u2011aware pyramid attention feature pyramid network fuses semantic information across scales and modalities; and a dual\u2011path decoder separates background from wheat regions, with a foreground decoder guided by vegetation attention maps to classify nitrogen status into four levels: Enrichment, Optimum Level, Moderate Nitrogen Deficiency, and Severe Nitrogen Deficiency. Experimental results demonstrate that our method outperforms state\u2011of\u2011the\u2011art models such as DeepLabV3+, U\u2011Net, U\u2011Net++, and Attention UNet. Modality contribution analysis confirms the complementary roles of RGB and MS, while ablation studies validate the effectiveness of each key module. These findings confirm the practicality and efficiency of the proposed approach for precise nitrogen monitoring in wheat, highlighting its potential for optimizing fertilization strategies in precision agriculture.<\/jats:p>","DOI":"10.3389\/fpls.2026.1909856","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:42:09Z","timestamp":1788417729000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DMP-UNet: a multimodal fusion network for high-precision nitrogen status monitoring in wheat from UAV multispectral images"],"prefix":"10.3389","volume":"17","author":[{"given":"Qingqing","family":"Hong","sequence":"first","affiliation":[{"name":"College of Information and Artificial Intelligence, Yangzhou University","place":["Yangzhou, China"]},{"name":"Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqi","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Information and Artificial Intelligence, Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bohan","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Information and Artificial Intelligence, Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changwei","family":"Tan","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Key Laboratory of Crop Cultivation and Physiology\/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Agricultural College of Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenghua","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information and Artificial Intelligence, Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Artificial Intelligence, Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information and Artificial Intelligence, Yangzhou University","place":["Yangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1117\/1.jmi.6.1.014006","article-title":"Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation","author":"Alom","year":"2018","journal-title":"ArXiv."},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2025.110356","article-title":"Appropriate vegetation indices and data analysis methods for orchards monitoring using uav-based remote sensing: A comprehensive research","volume":"235","author":"Bagheri","year":"2025","journal-title":"Comput. 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