Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning

Mads Dyrmann*, Anders Krogh Mortensen, Lars Linneberg, Toke Thomas Høye, Kim Bjerge

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review


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.

Original languageEnglish
Article number6126
JournalSensors (Switzerland)
Number of pages21
Publication statusPublished - Sept 2021


  • High speed acquisition
  • Invasive alien plant species
  • Machine learning
  • Remote sensing
  • Roadside


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