Cargando…

Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

Forests are indispensable links in the ecological chain and important ecosystems in nature. The destruction of forests seriously influences the ecological environment of the Earth. Forest protection plays an important role in human sustainable development, and the most important aspect of forest pro...

Descripción completa

Detalles Bibliográficos
Autores principales: Zheng, Shaoxiong, Gao, Peng, Zou, Xiangjun, Wang, Weixing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618655/
https://www.ncbi.nlm.nih.gov/pubmed/36325548
http://dx.doi.org/10.3389/fpls.2022.954757
_version_ 1784821098855006208
author Zheng, Shaoxiong
Gao, Peng
Zou, Xiangjun
Wang, Weixing
author_facet Zheng, Shaoxiong
Gao, Peng
Zou, Xiangjun
Wang, Weixing
author_sort Zheng, Shaoxiong
collection PubMed
description Forests are indispensable links in the ecological chain and important ecosystems in nature. The destruction of forests seriously influences the ecological environment of the Earth. Forest protection plays an important role in human sustainable development, and the most important aspect of forest protection is preventing forest fires. Fire affects the structure and dynamics of forests and also climate and geochemical cycles. Using various technologies to monitor the occurrence of forest fires, quickly finding the source of forest fires, and conducting early intervention are of great significance to reducing the damage caused by forest fires. An improved forest fire risk identification algorithm is established based on a deep learning algorithm to accurately identify forest fire risk in a complex natural environment. First, image enhancement and morphological preprocessing are performed on a forest fire risk image. Second, the suspected forest fire area is segmented. The color segmentation results are compared using the HAF and MCC methods, and the suspected forest fire area features are extracted. Finally, the forest fire risk image recognition processing is conducted. A forest fire risk dataset is constructed to compare different classification methods to predict the occurrence of forest fire risk to improve the backpropagation (BP) neural network forest fire identification algorithm. An improved machine learning algorithm is used to evaluate the classification accuracy. The results reveal that the algorithm changes the learning rate between 0.1 and 0.8, consistent with the cross-index verification of the 10x sampling algorithm. In the combined improved BP neural network and support vector machine (SVM) classifier, forest fire risk is recognized based on feature extraction and the BP network. In total, 1,450 images are used as the training set. The experimental results reveal that in image preprocessing, image enhancement technology using the frequency and spatial domain methods can enhance the useful information of the image and improve its clarity. In the image segmentation stage, MCC is used to evaluate the segmentationresults. The accuracy of this algorithm is high compared with other algorithms, up to 92.73%. Therefore, the improved forest fire risk identification algorithm can accurately identify forest fire risk in the natural environment and contribute to forest protection.
format Online
Article
Text
id pubmed-9618655
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-96186552022-11-01 Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm Zheng, Shaoxiong Gao, Peng Zou, Xiangjun Wang, Weixing Front Plant Sci Plant Science Forests are indispensable links in the ecological chain and important ecosystems in nature. The destruction of forests seriously influences the ecological environment of the Earth. Forest protection plays an important role in human sustainable development, and the most important aspect of forest protection is preventing forest fires. Fire affects the structure and dynamics of forests and also climate and geochemical cycles. Using various technologies to monitor the occurrence of forest fires, quickly finding the source of forest fires, and conducting early intervention are of great significance to reducing the damage caused by forest fires. An improved forest fire risk identification algorithm is established based on a deep learning algorithm to accurately identify forest fire risk in a complex natural environment. First, image enhancement and morphological preprocessing are performed on a forest fire risk image. Second, the suspected forest fire area is segmented. The color segmentation results are compared using the HAF and MCC methods, and the suspected forest fire area features are extracted. Finally, the forest fire risk image recognition processing is conducted. A forest fire risk dataset is constructed to compare different classification methods to predict the occurrence of forest fire risk to improve the backpropagation (BP) neural network forest fire identification algorithm. An improved machine learning algorithm is used to evaluate the classification accuracy. The results reveal that the algorithm changes the learning rate between 0.1 and 0.8, consistent with the cross-index verification of the 10x sampling algorithm. In the combined improved BP neural network and support vector machine (SVM) classifier, forest fire risk is recognized based on feature extraction and the BP network. In total, 1,450 images are used as the training set. The experimental results reveal that in image preprocessing, image enhancement technology using the frequency and spatial domain methods can enhance the useful information of the image and improve its clarity. In the image segmentation stage, MCC is used to evaluate the segmentationresults. The accuracy of this algorithm is high compared with other algorithms, up to 92.73%. Therefore, the improved forest fire risk identification algorithm can accurately identify forest fire risk in the natural environment and contribute to forest protection. Frontiers Media S.A. 2022-10-17 /pmc/articles/PMC9618655/ /pubmed/36325548 http://dx.doi.org/10.3389/fpls.2022.954757 Text en Copyright © 2022 Zheng, Gao, Zou and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Zheng, Shaoxiong
Gao, Peng
Zou, Xiangjun
Wang, Weixing
Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm
title Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm
title_full Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm
title_fullStr Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm
title_full_unstemmed Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm
title_short Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm
title_sort forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618655/
https://www.ncbi.nlm.nih.gov/pubmed/36325548
http://dx.doi.org/10.3389/fpls.2022.954757
work_keys_str_mv AT zhengshaoxiong forestfiremonitoringviauncrewedaerialvehicleimageprocessingbasedonamodifiedmachinelearningalgorithm
AT gaopeng forestfiremonitoringviauncrewedaerialvehicleimageprocessingbasedonamodifiedmachinelearningalgorithm
AT zouxiangjun forestfiremonitoringviauncrewedaerialvehicleimageprocessingbasedonamodifiedmachinelearningalgorithm
AT wangweixing forestfiremonitoringviauncrewedaerialvehicleimageprocessingbasedonamodifiedmachinelearningalgorithm