{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:45:16Z","timestamp":1773233116155,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T00:00:00Z","timestamp":1698537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Norwegian Partners of the Agricultural Settlement Fund","award":["159205"],"award-info":[{"award-number":["159205"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and \u2018other\u2019 in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. Images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. In this study, we showed the outstanding performance and robustness of a 1Dtransformer model for weed mapping based on UAV imagery for the first time. The model can be used to obtain weed maps in cereals fields known to be infested by perennial weeds. These maps can be used as basis for the generation of prescription maps for SSWM, either pre-harvest, post-harvest, or in the next crop, by applying herbicides or non-chemical measures.<\/jats:p>","DOI":"10.3390\/rs15215165","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T13:20:07Z","timestamp":1698672007000},"page":"5165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Transformer in UAV Image-Based Weed Mapping"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3916-7388","authenticated-orcid":false,"given":"Jiangsan","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Agricultural Technology, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, NO-1431 \u00c5s, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8780-6538","authenticated-orcid":false,"given":"Therese With","family":"Berge","sequence":"additional","affiliation":[{"name":"Department of Invertebrate Pests and Weeds in Forestry, Agriculture and Horticulture, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, NO-1431 \u00c5s, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7171-9501","authenticated-orcid":false,"given":"Jakob","family":"Geipel","sequence":"additional","affiliation":[{"name":"Department of Agricultural Technology, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, NO-1431 \u00c5s, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1111\/j.1365-3180.1996.tb01658.x","article-title":"A Technique for Mapping the Spatial Distribution of Elymus Repots, with Estimates of the Potential Reduction in Herbicide Usage from Patch Spraying","volume":"36","author":"Rew","year":"1996","journal-title":"Weed Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.17221\/599\/2012-PSE","article-title":"Impact of Site-Specific Weed Management on Herbicide Savings and Winter Wheat Yield","volume":"59","author":"Hamouz","year":"2013","journal-title":"Plant Soil. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Blank, L., Rozenberg, G., and Gafni, R. (2023). Spatial and Temporal Aspects of Weeds Distribution within Agricultural Fields\u2013A Review. 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