Deep learning algorithm for tree defect characterization

This study explores the potential of using Deep Learning (DL) to develop a non-invasive method for assessing the health of trees, with a focus on characterizing defects in tree trunks and estimating their size using Ground Penetrating Radar (GPR) images. The aim is to reduce the number of treefall i...

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Bibliographic Details
Main Author:	Grandhi, Dhanush Chandra Krishna Sai
Other Authors:	Abdulkadir C. Yucel
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2023
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/167679
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-1676792023-07-07T16:03:22Z Deep learning algorithm for tree defect characterization Grandhi, Dhanush Chandra Krishna Sai Abdulkadir C. Yucel School of Electrical and Electronic Engineering acyucel@ntu.edu.sg Engineering::Electrical and electronic engineering This study explores the potential of using Deep Learning (DL) to develop a non-invasive method for assessing the health of trees, with a focus on characterizing defects in tree trunks and estimating their size using Ground Penetrating Radar (GPR) images. The aim is to reduce the number of treefall incidents and improve tree management, especially in cities like Singapore, which have very high tree population. A successful DL-based model could help arborists assess trees quickly and accurately, improving the efficiency of tree health assessment and reducing the risk of accidents and fatalities. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-30T07:40:47Z 2023-05-30T07:40:47Z 2023 Final Year Project (FYP) Grandhi, D. C. K. S. (2023). Deep learning algorithm for tree defect characterization. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167679 https://hdl.handle.net/10356/167679 en B3031-221 application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering
spellingShingle	Engineering::Electrical and electronic engineering Grandhi, Dhanush Chandra Krishna Sai Deep learning algorithm for tree defect characterization
description	This study explores the potential of using Deep Learning (DL) to develop a non-invasive method for assessing the health of trees, with a focus on characterizing defects in tree trunks and estimating their size using Ground Penetrating Radar (GPR) images. The aim is to reduce the number of treefall incidents and improve tree management, especially in cities like Singapore, which have very high tree population. A successful DL-based model could help arborists assess trees quickly and accurately, improving the efficiency of tree health assessment and reducing the risk of accidents and fatalities.
author2	Abdulkadir C. Yucel
author_facet	Abdulkadir C. Yucel Grandhi, Dhanush Chandra Krishna Sai
format	Final Year Project
author	Grandhi, Dhanush Chandra Krishna Sai
author_sort	Grandhi, Dhanush Chandra Krishna Sai
title	Deep learning algorithm for tree defect characterization
title_short	Deep learning algorithm for tree defect characterization
title_full	Deep learning algorithm for tree defect characterization
title_fullStr	Deep learning algorithm for tree defect characterization
title_full_unstemmed	Deep learning algorithm for tree defect characterization
title_sort	deep learning algorithm for tree defect characterization
publisher	Nanyang Technological University
publishDate	2023
url	https://hdl.handle.net/10356/167679
_version_	1772827169945288704

Deep learning algorithm for tree defect characterization

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