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Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning
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 tr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473160/ https://www.ncbi.nlm.nih.gov/pubmed/34577335 http://dx.doi.org/10.3390/s21186126 |
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author | Dyrmann, Mads Mortensen, Anders Krogh Linneberg, Lars Høye, Toke Thomas Bjerge, Kim |
author_facet | Dyrmann, Mads Mortensen, Anders Krogh Linneberg, Lars Høye, Toke Thomas Bjerge, Kim |
author_sort | Dyrmann, Mads |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8473160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84731602021-09-28 Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning Dyrmann, Mads Mortensen, Anders Krogh Linneberg, Lars Høye, Toke Thomas Bjerge, Kim Sensors (Basel) Article 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. MDPI 2021-09-13 /pmc/articles/PMC8473160/ /pubmed/34577335 http://dx.doi.org/10.3390/s21186126 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dyrmann, Mads Mortensen, Anders Krogh Linneberg, Lars Høye, Toke Thomas Bjerge, Kim Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning |
title | Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning |
title_full | Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning |
title_fullStr | Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning |
title_full_unstemmed | Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning |
title_short | Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning |
title_sort | camera assisted roadside monitoring for invasive alien plant species using deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473160/ https://www.ncbi.nlm.nih.gov/pubmed/34577335 http://dx.doi.org/10.3390/s21186126 |
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