{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T12:42:20Z","timestamp":1776688940962,"version":"3.51.2"},"reference-count":34,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Invasive alien plant species (IAPS) pose a threat to biodiversity as they propagate and outcompete natural vegetation. In this study, a system for monitoring IAPS on the roadside is presented. The system consists of a camera that acquires images at high speed mounted on a vehicle that follows the traffic. Images of seven IAPS (Cytisus scoparius, Heracleum, Lupinus polyphyllus, Pastinaca sativa, Reynoutria, Rosa rugosa, and Solidago) were collected on Danish motorways. Three deep convolutional neural networks for classification (ResNet50V2 and MobileNetV2) and object detection (YOLOv3) were trained and evaluated at different image sizes. The results showed that the performance of the networks varied with the input image size and also the size of the IAPS in the images. Binary classification of IAPS vs. non-IAPS showed an increased performance, compared to the classification of individual IAPS. This study shows that automatic detection and mapping of invasive plants along the roadside is possible at high speeds.<\/jats:p>","DOI":"10.3390\/s21186126","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T03:46:14Z","timestamp":1631591174000},"page":"6126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6510-1609","authenticated-orcid":false,"given":"Mads","family":"Dyrmann","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8773-635X","authenticated-orcid":false,"given":"Anders Krogh","family":"Mortensen","sequence":"additional","affiliation":[{"name":"Department of Agroecology, Aarhus University, Fors\u00f8gsvej 1, 4200 Slagelse, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0659-8934","authenticated-orcid":false,"given":"Lars","family":"Linneberg","sequence":"additional","affiliation":[{"name":"Danish Road Directorate, Thomas Helsteds Vej 11, 8660 Skanderborg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5387-3284","authenticated-orcid":false,"given":"Toke Thomas","family":"H\u00f8ye","sequence":"additional","affiliation":[{"name":"Department of Bioscience and Arctic Research Centre, Aarhus University, Gren\u00e5vej 14, 8440 R\u00f8nde, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6742-9504","authenticated-orcid":false,"given":"Kim","family":"Bjerge","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106020","DOI":"10.1016\/j.ecolind.2019.106020","article-title":"Invasive alien plant species: Their impact on environment, ecosystem services and human health","volume":"111","author":"Singh","year":"2020","journal-title":"Ecol. 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