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UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands
The monitoring of invasive grasses and vegetation in remote areas is challenging, costly, and on the ground sometimes dangerous. Satellite and manned aircraft surveys can assist but their use may be limited due to the ground sampling resolution or cloud cover. Straightforward and accurate surveillan...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856113/ https://www.ncbi.nlm.nih.gov/pubmed/29462912 http://dx.doi.org/10.3390/s18020605 |
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author | Sandino, Juan Gonzalez, Felipe Mengersen, Kerrie Gaston, Kevin J. |
author_facet | Sandino, Juan Gonzalez, Felipe Mengersen, Kerrie Gaston, Kevin J. |
author_sort | Sandino, Juan |
collection | PubMed |
description | The monitoring of invasive grasses and vegetation in remote areas is challenging, costly, and on the ground sometimes dangerous. Satellite and manned aircraft surveys can assist but their use may be limited due to the ground sampling resolution or cloud cover. Straightforward and accurate surveillance methods are needed to quantify rates of grass invasion, offer appropriate vegetation tracking reports, and apply optimal control methods. This paper presents a pipeline process to detect and generate a pixel-wise segmentation of invasive grasses, using buffel grass (Cenchrus ciliaris) and spinifex (Triodia sp.) as examples. The process integrates unmanned aerial vehicles (UAVs) also commonly known as drones, high-resolution red, green, blue colour model (RGB) cameras, and a data processing approach based on machine learning algorithms. The methods are illustrated with data acquired in Cape Range National Park, Western Australia (WA), Australia, orthorectified in Agisoft Photoscan Pro, and processed in Python programming language, scikit-learn, and eXtreme Gradient Boosting (XGBoost) libraries. In total, 342,626 samples were extracted from the obtained data set and labelled into six classes. Segmentation results provided an individual detection rate of 97% for buffel grass and 96% for spinifex, with a global multiclass pixel-wise detection rate of 97%. Obtained results were robust against illumination changes, object rotation, occlusion, background cluttering, and floral density variation. |
format | Online Article Text |
id | pubmed-5856113 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-58561132018-03-20 UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands Sandino, Juan Gonzalez, Felipe Mengersen, Kerrie Gaston, Kevin J. Sensors (Basel) Article The monitoring of invasive grasses and vegetation in remote areas is challenging, costly, and on the ground sometimes dangerous. Satellite and manned aircraft surveys can assist but their use may be limited due to the ground sampling resolution or cloud cover. Straightforward and accurate surveillance methods are needed to quantify rates of grass invasion, offer appropriate vegetation tracking reports, and apply optimal control methods. This paper presents a pipeline process to detect and generate a pixel-wise segmentation of invasive grasses, using buffel grass (Cenchrus ciliaris) and spinifex (Triodia sp.) as examples. The process integrates unmanned aerial vehicles (UAVs) also commonly known as drones, high-resolution red, green, blue colour model (RGB) cameras, and a data processing approach based on machine learning algorithms. The methods are illustrated with data acquired in Cape Range National Park, Western Australia (WA), Australia, orthorectified in Agisoft Photoscan Pro, and processed in Python programming language, scikit-learn, and eXtreme Gradient Boosting (XGBoost) libraries. In total, 342,626 samples were extracted from the obtained data set and labelled into six classes. Segmentation results provided an individual detection rate of 97% for buffel grass and 96% for spinifex, with a global multiclass pixel-wise detection rate of 97%. Obtained results were robust against illumination changes, object rotation, occlusion, background cluttering, and floral density variation. MDPI 2018-02-16 /pmc/articles/PMC5856113/ /pubmed/29462912 http://dx.doi.org/10.3390/s18020605 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sandino, Juan Gonzalez, Felipe Mengersen, Kerrie Gaston, Kevin J. UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands |
title | UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands |
title_full | UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands |
title_fullStr | UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands |
title_full_unstemmed | UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands |
title_short | UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands |
title_sort | uavs and machine learning revolutionising invasive grass and vegetation surveys in remote arid lands |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856113/ https://www.ncbi.nlm.nih.gov/pubmed/29462912 http://dx.doi.org/10.3390/s18020605 |
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