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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Nanyang Technological University
2023
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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 |
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Nanyang Technological University |
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NTU Library |
| continent |
Asia |
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Singapore Singapore |
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English |
| topic |
Engineering::Electrical and electronic engineering |
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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 |