Imaged based identification of colombian timbers using the xylotron: a proof of concept international partnership

Field deployable computer vision wood identification systems can play a key role in combating illegal logging in the real world. This work used 764 xylarium specimens from 84 taxa to develop an image data set to train a classifier to identify 14 commercial Colombian timbers. We imaged specimens from...
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Autor*in:	Rafael E. Arévalo B. [verfasserIn] Esperanza N. Pulido R. [verfasserIn] Juan F. Solórzano G. [verfasserIn] Richard Soares [verfasserIn] Flavio Ruffinatto [verfasserIn] Prabu Ravindran [verfasserIn] Alex C. Wiedenhoeft [verfasserIn]

Format:	E-Artikel
Sprache:	Spanisch

Erschienen:	2021

Schlagwörter:	wood identification forensic wood anatomy deep learning transfer learning machine learning

Übergeordnetes Werk:	In: Colombia Forestal - Universidad Distrital Francisco Jose de Caldas, 2013, 24(2021), 1
Übergeordnetes Werk:	volume:24 ; year:2021 ; number:1

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.14483/2256201X.16700

Katalog-ID:	DOAJ072776420

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authorswithroles_txt_mv	Rafael E. Arévalo B. @@aut@@ Esperanza N. Pulido R. @@aut@@ Juan F. Solórzano G. @@aut@@ Richard Soares @@aut@@ Flavio Ruffinatto @@aut@@ Prabu Ravindran @@aut@@ Alex C. Wiedenhoeft @@aut@@
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author	Rafael E. Arévalo B.
spellingShingle	Rafael E. Arévalo B. misc SD1-669.5 misc wood identification misc forensic wood anatomy misc deep learning misc transfer learning misc machine learning misc Agriculture misc S misc Forestry Imaged based identification of colombian timbers using the xylotron: a proof of concept international partnership
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topic_title	SD1-669.5 Imaged based identification of colombian timbers using the xylotron: a proof of concept international partnership wood identification forensic wood anatomy deep learning transfer learning machine learning
topic	misc SD1-669.5 misc wood identification misc forensic wood anatomy misc deep learning misc transfer learning misc machine learning misc Agriculture misc S misc Forestry
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title	Imaged based identification of colombian timbers using the xylotron: a proof of concept international partnership
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title_sort	imaged based identification of colombian timbers using the xylotron: a proof of concept international partnership
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title_auth	Imaged based identification of colombian timbers using the xylotron: a proof of concept international partnership
abstract	Field deployable computer vision wood identification systems can play a key role in combating illegal logging in the real world. This work used 764 xylarium specimens from 84 taxa to develop an image data set to train a classifier to identify 14 commercial Colombian timbers. We imaged specimens from various xylaria outside Colombia, trained and evaluated an initial identification model, then collected additional images from a Colombian xylarium (BOFw), and incorporated those images to refine and produce a final model. The specimen classification accuracy of this final model was ~ 97%, demonstrating that including local specimens can augment the accuracy and reliability of the XyloTron system. Our study demonstrates the first deployable computer vision model for wood identification in Colombia, developed on a timescale of months rather than years by leveraging international cooperation. We conclude that field testing and advanced forensic and machine learning training are the next logical steps.
abstractGer	Field deployable computer vision wood identification systems can play a key role in combating illegal logging in the real world. This work used 764 xylarium specimens from 84 taxa to develop an image data set to train a classifier to identify 14 commercial Colombian timbers. We imaged specimens from various xylaria outside Colombia, trained and evaluated an initial identification model, then collected additional images from a Colombian xylarium (BOFw), and incorporated those images to refine and produce a final model. The specimen classification accuracy of this final model was ~ 97%, demonstrating that including local specimens can augment the accuracy and reliability of the XyloTron system. Our study demonstrates the first deployable computer vision model for wood identification in Colombia, developed on a timescale of months rather than years by leveraging international cooperation. We conclude that field testing and advanced forensic and machine learning training are the next logical steps.
abstract_unstemmed	Field deployable computer vision wood identification systems can play a key role in combating illegal logging in the real world. This work used 764 xylarium specimens from 84 taxa to develop an image data set to train a classifier to identify 14 commercial Colombian timbers. We imaged specimens from various xylaria outside Colombia, trained and evaluated an initial identification model, then collected additional images from a Colombian xylarium (BOFw), and incorporated those images to refine and produce a final model. The specimen classification accuracy of this final model was ~ 97%, demonstrating that including local specimens can augment the accuracy and reliability of the XyloTron system. Our study demonstrates the first deployable computer vision model for wood identification in Colombia, developed on a timescale of months rather than years by leveraging international cooperation. We conclude that field testing and advanced forensic and machine learning training are the next logical steps.
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title_short	Imaged based identification of colombian timbers using the xylotron: a proof of concept international partnership
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