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Updated: Oct 18, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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

Mads Dyrmann¹, Anders Krogh Mortensen², Lars Linneberg³

¹Department of Electrical and Computer Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark.

Sensors (Basel, Switzerland)

|September 28, 2021

Summary

This study presents a high-speed roadside monitoring system for invasive alien plant species (IAPS). Automated detection and mapping of these invasive plants are shown to be feasible, aiding biodiversity conservation efforts.

Keywords:

high speed acquisition invasive alien plant species machine learning remote sensing roadside

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Published on: July 1, 2019

Area of Science:

Ecology and Environmental Science
Computer Science and Artificial Intelligence
Botany and Plant Science

Background:

Invasive alien plant species (IAPS) threaten biodiversity by outcompeting native flora.
Effective monitoring systems are crucial for managing and mitigating the spread of IAPS.
Current roadside monitoring methods may not be efficient for large-scale detection.

Purpose of the Study:

To develop and evaluate a high-speed roadside monitoring system for detecting Invasive Alien Plant Species (IAPS).
To assess the performance of deep convolutional neural networks for IAPS classification and object detection.
To determine the feasibility of automatic detection and mapping of IAPS along roadways.

Main Methods:

A vehicle-mounted camera system captured high-speed images of roadside vegetation on Danish motorways.
Images of seven specific IAPS (Cytisus scoparius, Heracleum, Lupinus polyphyllus, Pastinaca sativa, Reynoutria, Rosa rugosa, and Solidago) were collected.
Three deep convolutional neural networks (ResNet50V2, MobileNetV2 for classification; YOLOv3 for object detection) were trained and evaluated at various image resolutions.

Main Results:

Network performance varied significantly with input image size and the scale of IAPS within images.
Binary classification (IAPS vs. non-IAPS) demonstrated higher performance compared to individual IAPS classification.
The system successfully enabled automatic detection and mapping of invasive plants at high vehicle speeds.

Conclusions:

Automatic, high-speed detection and mapping of roadside Invasive Alien Plant Species (IAPS) is achievable.
Deep learning models show promise for ecological monitoring applications, with performance influenced by image parameters.
The developed system offers a scalable solution for monitoring IAPS and supporting biodiversity management strategies.