{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T21:20:16Z","timestamp":1793395216418,"version":"4.5.0"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Semantic segmentation in deep learning is a crucial area of research within computer vision, aimed at assigning specific labels to each pixel in an image. The segmentation of crops, plants, and weeds has significantly advanced the application of deep learning in precision agriculture, leading to the development of sophisticated architectures based on convolutional neural networks (CNNs). This study proposes a segmentation algorithm for identifying plants and weeds using broadband multispectral images. In the first part of this algorithm, we utilize the PIF-Net model for feature extraction and fusion. The resulting feature map is then employed to enhance an optimized U-Net model for semantic segmentation within a broadband system. Our investigation focuses specifically on scenes from the CAVIAR dataset of multispectral images. The proposed algorithm has enabled us to effectively capture complex details while regulating the learning process, achieving an impressive overall accuracy of 98.2%. The results demonstrate that our approach to semantic segmentation and the differentiation between plants and weeds yields accurate and compelling outcomes.<\/jats:p>","DOI":"10.3390\/jimaging11030085","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T04:34:43Z","timestamp":1742272483000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Automatic Segmentation of Plants and Weeds in Wide-Band Multispectral Imaging (WMI)"],"prefix":"10.3390","volume":"11","author":[{"given":"Sovi Guillaume","family":"Sodjinou","sequence":"first","affiliation":[{"name":"ImViA, UFR Sciences et Techniques, Universit\u00e9 de Bourgogne Europe, 21078 Dijon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amadou Tidjani Sanda","family":"Mahama","sequence":"additional","affiliation":[{"name":"Institute of Mathematics and Physical Sciences, University of Aboney Calavi, Dangbo 01 BP 613, Benin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre","family":"Gouton","sequence":"additional","affiliation":[{"name":"ImViA, UFR Sciences et Techniques, Universit\u00e9 de Bourgogne Europe, 21078 Dijon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1080\/07038992.2021.1956277","article-title":"Special Issue on Large-Scale Deep Learning for Sensor-Driven Mapping","volume":"47","author":"Wang","year":"2021","journal-title":"Can. 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