{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T15:27:58Z","timestamp":1771342078352,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Australian Department of Agriculture, Fisheries and Forestry"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hawkweeds (Pilosella spp.) have become a severe and rapidly invading weed in pasture lands and forest meadows of New Zealand. Detection of hawkweed infestations is essential for eradication and resource management at private and government levels. This study explores the potential of machine learning (ML) algorithms for detecting mouse-ear hawkweed (Pilosella officinarum) foliage and flowers from Unmanned Aerial Vehicle (UAV)-acquired multispectral (MS) images at various spatial resolutions. The performances of different ML algorithms, namely eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbours (KNN), were analysed in their capacity to detect hawkweed foliage and flowers using MS imagery. The imagery was obtained at numerous spatial resolutions from a highly infested study site located in the McKenzie Region of the South Island of New Zealand in January 2021. The spatial resolution of 0.65 cm\/pixel (acquired at a flying height of 15 m above ground level) produced the highest overall testing and validation accuracy of 100% using the RF, KNN, and XGB models for detecting hawkweed flowers. In hawkweed foliage detection at the same resolution, the RF and XGB models achieved highest testing accuracy of 97%, while other models (KNN and SVM) achieved an overall model testing accuracy of 96% and 72%, respectively. The XGB model achieved the highest overall validation accuracy of 98%, while the other models (RF, KNN, and SVM) produced validation accuracies of 97%, 97%, and 80%, respectively. This proposed methodology may facilitate non-invasive detection efforts of mouse-ear hawkweed flowers and foliage in other naturalised areas, enabling land managers to optimise the use of UAV remote sensing technologies for better resource allocation.<\/jats:p>","DOI":"10.3390\/rs15061633","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T03:09:37Z","timestamp":1679281777000},"page":"1633","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Autonomous Detection of Mouse-Ear Hawkweed Using Drones, Multispectral Imagery and Supervised Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7793-2680","authenticated-orcid":false,"given":"Narmilan","family":"Amarasingam","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology (QUT), 2 George Street, Brisbane City, QLD 4000, Australia"},{"name":"QUT Centre for Robotics, Queensland University of Technology, 2 George Street, Brisbane City, QLD 4000, Australia"},{"name":"Department of Biosystems Technology, Faculty of Technology, South Eastern University of Sri Lanka, University Park, Oluvil 32360, Sri Lanka"}]},{"given":"Mark","family":"Hamilton","sequence":"additional","affiliation":[{"name":"NSW Department of Planning and Environment, 12 Darcy Street, Parramatta, NSW 2150, Australia"}]},{"given":"Jane E.","family":"Kelly","sequence":"additional","affiliation":[{"name":"Gulbali Institute for Agriculture Water and Environment, Charles Sturt University, Boorooma Street, Wagga Wagga, NSW 2678, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5728-4356","authenticated-orcid":false,"given":"Lihong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Gulbali Institute for Agriculture Water and Environment, Charles Sturt University, Boorooma Street, Wagga Wagga, NSW 2678, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6780-2425","authenticated-orcid":false,"given":"Juan","family":"Sandino","sequence":"additional","affiliation":[{"name":"QUT Centre for Robotics, Queensland University of Technology, 2 George Street, Brisbane City, QLD 4000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4342-3682","authenticated-orcid":false,"given":"Felipe","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology (QUT), 2 George Street, Brisbane City, QLD 4000, Australia"},{"name":"QUT Centre for Robotics, Queensland University of Technology, 2 George Street, Brisbane City, QLD 4000, Australia"}]},{"given":"Remy L.","family":"Dehaan","sequence":"additional","affiliation":[{"name":"Gulbali Institute for Agriculture Water and Environment, Charles Sturt University, Boorooma Street, Wagga Wagga, NSW 2678, Australia"}]},{"given":"Hillary","family":"Cherry","sequence":"additional","affiliation":[{"name":"NSW Department of Planning and Environment, 12 Darcy Street, Parramatta, NSW 2150, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Luna, I.M., Fern\u00e1ndez-Quintanilla, C., and Dorado, J. 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