Standing tree health assessment using contact–ultrasonic testing and machine learning

Mousavi, M; Taskhiri, MS; Gandomi, AH

Standing tree health assessment using contact–ultrasonic testing and machine learning

Mousavi, M

Taskhiri, MS Gandomi, AH

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Computers and Electronics in Agriculture, 2023, 209
Issue Date:: 2023-06-01

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Field	Value	Language
dc.contributor.author	Mousavi, M https://orcid.org/0000-0002-0436-5176
dc.contributor.author	Taskhiri, MS
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-05-10T11:14:54Z
dc.date.available	2024-05-10T11:14:54Z
dc.date.issued	2023-06-01
dc.identifier.citation	Computers and Electronics in Agriculture, 2023, 209
dc.identifier.issn	0168-1699
dc.identifier.issn	1872-7107
dc.identifier.uri	http://hdl.handle.net/10453/178818
dc.description.abstract	The problem of hole-defect detection in standing trees is solved. An ultrasonic device (Pundit PL-200) was employed to collect ultrasonic signals from various wood specimens both in the lab and field. The collected ultrasonic signals were then processed through the Variational Mode Decomposition algorithm to derive effective features. In order to solve the classification problem more efficiently, the obtained characteristics were then analyzed through PCA to determine the most useful features. Several machine learning algorithms and a one-dimensional convolutional neural network (1D-CNN) were employed to solve a set of classification problems based on data collected from (1) specimens with artificial defects in the lab and (2) billets with natural defects selected from trees harvested at sites in the two states of WA and NSW, Australia. The results demonstrate the effectiveness of the proposed method for classifying wood materials based on their health state, where testing accuracy results of 100% in the lab and at least 92.2% in fields were achieved. The Fine Gaussian SVM was found to perform most effectively on data derived from specimens in the lab and fields. It was also shown that 1D-CNN results were more reliable for generalizing the solution to the classification problem of standing trees in fields.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Computers and Electronics in Agriculture
dc.relation.isbasedon	10.1016/j.compag.2023.107816
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	07 Agricultural and Veterinary Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Agronomy & Agriculture
dc.subject.classification	30 Agricultural, veterinary and food sciences
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Standing tree health assessment using contact–ultrasonic testing and machine learning
dc.type	Journal Article
utslib.citation.volume	209
utslib.for	07 Agricultural and Veterinary Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2024-05-10T11:14:53Z
pubs.publication-status	Published
pubs.volume	209

Abstract:

The problem of hole-defect detection in standing trees is solved. An ultrasonic device (Pundit PL-200) was employed to collect ultrasonic signals from various wood specimens both in the lab and field. The collected ultrasonic signals were then processed through the Variational Mode Decomposition algorithm to derive effective features. In order to solve the classification problem more efficiently, the obtained characteristics were then analyzed through PCA to determine the most useful features. Several machine learning algorithms and a one-dimensional convolutional neural network (1D-CNN) were employed to solve a set of classification problems based on data collected from (1) specimens with artificial defects in the lab and (2) billets with natural defects selected from trees harvested at sites in the two states of WA and NSW, Australia. The results demonstrate the effectiveness of the proposed method for classifying wood materials based on their health state, where testing accuracy results of 100% in the lab and at least 92.2% in fields were achieved. The Fine Gaussian SVM was found to perform most effectively on data derived from specimens in the lab and fields. It was also shown that 1D-CNN results were more reliable for generalizing the solution to the classification problem of standing trees in fields.

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http://hdl.handle.net/10453/178818