Evaluating the significance of cutting planes of wood samples when training CNNs for forest species identification

Geovanni Figueroa-Mata; Erick Mata-Montero; Juan Carlos Valverde-Otárola; Dagoberto Arias-Aguilar

doi:10.1109/CONCAPAN.2018.8596406

Evaluating the significance of cutting planes of wood samples when training CNNs for forest species identification

Geovanni Figueroa-Mata, Erick Mata-Montero, Juan Carlos Valverde-Otárola, Dagoberto Arias-Aguilar

Costa Rica Institute of Technology

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

3 Citas (Scopus)

Resumen

With the goal of quantifying the importance of each of the cutting planes of wood samples in the training process of a convolutional neural network that identifies forest species based on images of those cutting planes, we propose a convolutional model that is trained from scratch with images of transverse, radial, and tangential sections of Costa Rican forest species wood samples. The best Top1-accuracy achieved is 89.58% when the network is trained with transverse sections only. Because this is more than 20% better than the accuracy achieved when using any of the other two sections individually, we conclude that this is the most significant section of all three. This is consistent with current practice of experts, who prefer this cutting plane when conducting manual identifications based on anatomical features of wood samples.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018
Editores	Manuel N. Cardona
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781538661222
DOI	https://doi.org/10.1109/CONCAPAN.2018.8596406
Estado	Publicada - 28 dic 2018
Evento	2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018 - San Salvador, El Salvador Duración: 7 nov 2018 → 9 nov 2018

Serie de la publicación

Nombre	Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018

Conferencia

Conferencia	2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018
País/Territorio	El Salvador
Ciudad	San Salvador
Período	7/11/18 → 9/11/18

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10.1109/CONCAPAN.2018.8596406

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Figueroa-Mata, G., Mata-Montero, E., Valverde-Otárola, J. C., & Arias-Aguilar, D. (2018). Evaluating the significance of cutting planes of wood samples when training CNNs for forest species identification. En M. N. Cardona (Ed.), Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018 Artículo 8596406 (Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CONCAPAN.2018.8596406

Figueroa-Mata, Geovanni ; Mata-Montero, Erick ; Valverde-Otárola, Juan Carlos et al. / Evaluating the significance of cutting planes of wood samples when training CNNs for forest species identification. Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018. editor / Manuel N. Cardona. Institute of Electrical and Electronics Engineers Inc., 2018. (Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018).

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title = "Evaluating the significance of cutting planes of wood samples when training CNNs for forest species identification",

abstract = "With the goal of quantifying the importance of each of the cutting planes of wood samples in the training process of a convolutional neural network that identifies forest species based on images of those cutting planes, we propose a convolutional model that is trained from scratch with images of transverse, radial, and tangential sections of Costa Rican forest species wood samples. The best Top1-accuracy achieved is 89.58% when the network is trained with transverse sections only. Because this is more than 20% better than the accuracy achieved when using any of the other two sections individually, we conclude that this is the most significant section of all three. This is consistent with current practice of experts, who prefer this cutting plane when conducting manual identifications based on anatomical features of wood samples.",

keywords = "Automated image-based species identification, Convolutional neural network, Costa rican forest species, Cutting planes",

author = "Geovanni Figueroa-Mata and Erick Mata-Montero and Valverde-Ot{\'a}rola, {Juan Carlos} and Dagoberto Arias-Aguilar",

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Figueroa-Mata, G, Mata-Montero, E, Valverde-Otárola, JC & Arias-Aguilar, D 2018, Evaluating the significance of cutting planes of wood samples when training CNNs for forest species identification. En MN Cardona (ed.), Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018., 8596406, Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018, San Salvador, El Salvador, 7/11/18. https://doi.org/10.1109/CONCAPAN.2018.8596406

Evaluating the significance of cutting planes of wood samples when training CNNs for forest species identification. / Figueroa-Mata, Geovanni; Mata-Montero, Erick; Valverde-Otárola, Juan Carlos et al.
Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018. ed. / Manuel N. Cardona. Institute of Electrical and Electronics Engineers Inc., 2018. 8596406 (Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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AU - Figueroa-Mata, Geovanni

AU - Mata-Montero, Erick

AU - Valverde-Otárola, Juan Carlos

AU - Arias-Aguilar, Dagoberto

PY - 2018/12/28

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N2 - With the goal of quantifying the importance of each of the cutting planes of wood samples in the training process of a convolutional neural network that identifies forest species based on images of those cutting planes, we propose a convolutional model that is trained from scratch with images of transverse, radial, and tangential sections of Costa Rican forest species wood samples. The best Top1-accuracy achieved is 89.58% when the network is trained with transverse sections only. Because this is more than 20% better than the accuracy achieved when using any of the other two sections individually, we conclude that this is the most significant section of all three. This is consistent with current practice of experts, who prefer this cutting plane when conducting manual identifications based on anatomical features of wood samples.

AB - With the goal of quantifying the importance of each of the cutting planes of wood samples in the training process of a convolutional neural network that identifies forest species based on images of those cutting planes, we propose a convolutional model that is trained from scratch with images of transverse, radial, and tangential sections of Costa Rican forest species wood samples. The best Top1-accuracy achieved is 89.58% when the network is trained with transverse sections only. Because this is more than 20% better than the accuracy achieved when using any of the other two sections individually, we conclude that this is the most significant section of all three. This is consistent with current practice of experts, who prefer this cutting plane when conducting manual identifications based on anatomical features of wood samples.

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KW - Convolutional neural network

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Figueroa-Mata G, Mata-Montero E, Valverde-Otárola JC , Arias-Aguilar D. Evaluating the significance of cutting planes of wood samples when training CNNs for forest species identification. En Cardona MN, editor, Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8596406. (Proceedings of the 2018 IEEE 38th Central America and Panama Convention, CONCAPAN 2018). doi: 10.1109/CONCAPAN.2018.8596406

Evaluating the significance of cutting planes of wood samples when training CNNs for forest species identification

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