Using Deep Convolutional Networks for Species Identification of Xylotheque Samples

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

doi:10.1109/IWOBI.2018.8464216

Using Deep Convolutional Networks for Species Identification of Xylotheque Samples

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

School of Mathematics

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

13 Citas (Scopus)

Resumen

Forest species identification is critical to scientifically support many environmental, commercial, forensic, archaeological, and paleontological actividades. Therefore, it is very important to develop fast and accurate identification systems. We present a deep CNN for automated forest species identification based on macroscopic images of wood cuts. We first implement and study a modified version of the LeNet convolutional network, which is trained from scratch with a database of macroscopic images of 41 forest species of the Brazilian flora. With this network we achieve a top-1 accuracy of 93.6%. Additionally, we fine-tune the Resnet50 model with pre-trained weights on Imagenet to reach a top-1 accuracy of 98.03%, which improves previous published results of research on the same image database.

Idioma original	Inglés
Título de la publicación alojada	2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión impresa)	9781538675069
DOI	https://doi.org/10.1109/IWOBI.2018.8464216
Estado	Publicada - 12 sept 2018
Evento	2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - San Carlos, Costa Rica Duración: 18 jul 2018 → 20 jul 2018

Serie de la publicación

Nombre	2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings

Conferencia

Conferencia	2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018
País/Territorio	Costa Rica
Ciudad	San Carlos
Período	18/07/18 → 20/07/18

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10.1109/IWOBI.2018.8464216

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Figueroa-Mata, G., Mata-Montero, E., Valverde-Otarola, J. C., & Arias-Aguilar, D. (2018). Using Deep Convolutional Networks for Species Identification of Xylotheque Samples. En 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings Artículo 8464216 (2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWOBI.2018.8464216

Figueroa-Mata, Geovanni ; Mata-Montero, Erick ; Valverde-Otarola, Juan Carlos et al. / Using Deep Convolutional Networks for Species Identification of Xylotheque Samples. 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings).

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title = "Using Deep Convolutional Networks for Species Identification of Xylotheque Samples",

abstract = "Forest species identification is critical to scientifically support many environmental, commercial, forensic, archaeological, and paleontological actividades. Therefore, it is very important to develop fast and accurate identification systems. We present a deep CNN for automated forest species identification based on macroscopic images of wood cuts. We first implement and study a modified version of the LeNet convolutional network, which is trained from scratch with a database of macroscopic images of 41 forest species of the Brazilian flora. With this network we achieve a top-1 accuracy of 93.6%. Additionally, we fine-tune the Resnet50 model with pre-trained weights on Imagenet to reach a top-1 accuracy of 98.03%, which improves previous published results of research on the same image database.",

keywords = "Automated plant identification, Biodiversity informatics, Convolutional neural networks, Deep learning, Forest species identification, Image processing",

author = "Geovanni Figueroa-Mata and Erick Mata-Montero and Valverde-Otarola, {Juan Carlos} and Dagoberto Arias-Aguilar",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 ; Conference date: 18-07-2018 Through 20-07-2018",

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doi = "10.1109/IWOBI.2018.8464216",

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Figueroa-Mata, G, Mata-Montero, E, Valverde-Otarola, JC & Arias-Aguilar, D 2018, Using Deep Convolutional Networks for Species Identification of Xylotheque Samples. En 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings., 8464216, 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018, San Carlos, Costa Rica, 18/07/18. https://doi.org/10.1109/IWOBI.2018.8464216

Using Deep Convolutional Networks for Species Identification of Xylotheque Samples. / Figueroa-Mata, Geovanni; Mata-Montero, Erick; Valverde-Otarola, Juan Carlos et al.
2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8464216 (2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings).

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

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T1 - Using Deep Convolutional Networks for Species Identification of Xylotheque Samples

AU - Figueroa-Mata, Geovanni

AU - Mata-Montero, Erick

AU - Valverde-Otarola, Juan Carlos

AU - Arias-Aguilar, Dagoberto

PY - 2018/9/12

Y1 - 2018/9/12

N2 - Forest species identification is critical to scientifically support many environmental, commercial, forensic, archaeological, and paleontological actividades. Therefore, it is very important to develop fast and accurate identification systems. We present a deep CNN for automated forest species identification based on macroscopic images of wood cuts. We first implement and study a modified version of the LeNet convolutional network, which is trained from scratch with a database of macroscopic images of 41 forest species of the Brazilian flora. With this network we achieve a top-1 accuracy of 93.6%. Additionally, we fine-tune the Resnet50 model with pre-trained weights on Imagenet to reach a top-1 accuracy of 98.03%, which improves previous published results of research on the same image database.

AB - Forest species identification is critical to scientifically support many environmental, commercial, forensic, archaeological, and paleontological actividades. Therefore, it is very important to develop fast and accurate identification systems. We present a deep CNN for automated forest species identification based on macroscopic images of wood cuts. We first implement and study a modified version of the LeNet convolutional network, which is trained from scratch with a database of macroscopic images of 41 forest species of the Brazilian flora. With this network we achieve a top-1 accuracy of 93.6%. Additionally, we fine-tune the Resnet50 model with pre-trained weights on Imagenet to reach a top-1 accuracy of 98.03%, which improves previous published results of research on the same image database.

KW - Automated plant identification

KW - Biodiversity informatics

KW - Convolutional neural networks

KW - Deep learning

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KW - Image processing

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Figueroa-Mata G, Mata-Montero E, Valverde-Otarola JC , Arias-Aguilar D. Using Deep Convolutional Networks for Species Identification of Xylotheque Samples. En 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8464216. (2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings). doi: 10.1109/IWOBI.2018.8464216

Using Deep Convolutional Networks for Species Identification of Xylotheque Samples

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