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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-09, Vol.21 (18), p.6126
Main Authors: Dyrmann, Mads, Mortensen, Anders Krogh, Linneberg, Lars, Høye, Toke Thomas, Bjerge, Kim
Format: Article
Language:English
Subjects:
Algorithms
Biodiversity
Deep learning
Digital cameras
Flowers & plants
high speed acquisition
Image acquisition
Image classification
invasive alien plant species
machine learning
Monitoring
Morphology
Neural networks
Nonnative species
Remote sensing
Roads & highways
roadside
Traffic speed
Unmanned aerial vehicles
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21186126