Identification of wood from the Amazon by characteristics of Haralick and Neural Network: image segmentation and polishing of the surface
Giselly Lenise de Souza Vieira (1) , Márcio José Moutinho da Ponte (1-2), Victor Hugo Pereira Moutinho (1), Ricardo Jardim-Gonçalves (2), Celson Pantoja Lima (1-3), Marco Valério de Albuquerque Vinagre (4)
iForest - Biogeosciences and Forestry, Volume 15, Issue 4, Pages 234-239 (2022)
doi: https://doi.org/10.3832/ifor3906-015
Published: Jul 14, 2022 - Copyright © 2022 SISEF
Research Articles
Abstract
The identification of Amazonian timber species is a complex problem due to their great diversity and the lack of leaf material in the post-harvest inspection often hampers a correct recognition of the wood species. In this context, we developed a pattern recognition system of wood images to identify commonly traded species, with the aim of increasing the accuracy and efficiency of current identification methods. We used ten different species with three polishing treatments and twenty images for each wood species. As for the image recognition system, the textural segmentation associated with Haralick characteristics and classified by Artificial Neural Networks was used. We verified that the improvement of sandpaper granulometry increased the accuracy of species recognition. The developed model based on linear regression achieved a recognition rate of 94% in the training phase, and a post-training recognition rate of 65% for wood treated with 120-grit sandpaper mesh. We concluded that the wood pattern recognition model presented has the potential to correctly identify the wood species studied.
Keywords
Wood Identification, Amazon, Technology, Pattern Recognition, Digital Image Processing, Artificial Neural Networks
Authors’ Info
Authors’ address
Márcio José Moutinho da Ponte 0000-0002-0724-3721
Victor Hugo Pereira Moutinho 0000-0001-7770-3087
Celson Pantoja Lima 0000-0002-8074-8566
Graduate Program in Intellectual Property and Information Transfer Technology for Innovation/Federal University of West Pará (Brazil)
Ricardo Jardim-Gonçalves 0000-0002-3703-6854
Universidade Nova de Lisboa (Portugal)
Massachusetts Institute of Technology, MA (United States of America)
Corresponding author
Paper Info
Citation
de Souza Vieira GL, Moutinho da Ponte MJ, Pereira Moutinho VH, Jardim-Gonçalves R, Pantoja Lima C, de Albuquerque Vinagre MV (2022). Identification of wood from the Amazon by characteristics of Haralick and Neural Network: image segmentation and polishing of the surface. iForest 15: 234-239. - doi: 10.3832/ifor3906-015
Academic Editor
Giacomo Goli
Paper history
Received: Jun 19, 2021
Accepted: May 06, 2022
First online: Jul 14, 2022
Publication Date: Aug 31, 2022
Publication Time: 2.30 months
Copyright Information
© SISEF - The Italian Society of Silviculture and Forest Ecology 2022
Open Access
This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Web Metrics
Breakdown by View Type
Article Usage
Total Article Views: 736
(from publication date up to now)
Breakdown by View Type
HTML Page Views: 0
Abstract Page Views: 0
PDF Downloads: 494
Citation/Reference Downloads: 0
XML Downloads: 242
Web Metrics
Days since publication: 825
Overall contacts: 736
Avg. contacts per week: 6.24
Citation Metrics
Article Citations
Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Nov 2020)
(No citations were found up to date. Please come back later)
Publication Metrics
by Dimensions ©
Articles citing this article
List of the papers citing this article based on CrossRef Cited-by.
References
Neural networks for pattern recognition. Oxford University Press, Birmingham, UK, pp. 5-14.
Gscholar
Mapping forest successional stages in the Brazilian Amazon using forest heights derived from TanDEM-X SAR interferometry. Remote Sensing of Environment 232: 111-194.
CrossRef | Gscholar
Digital image processing. Prentice-Hall Inc., Upper Saddle River, NJ, USA, pp. 667.
Gscholar
Reconhecimento de espécies florestais através de imagem macroscópicas [Forest species recognition through macroscopic images]. Tese de doutorado, Programa de Pós-Graduação em Informática do Setor de Ciências Exatas da Universidade Federal do Paraná, Brazil, pp. 169-175. [in Portuguese]
Online | Gscholar
Neural networks and learning machines (3rd edn). Prentice Hall, Hoboken, NJ, USA, pp. 1-2.
Gscholar
Research on color space applicable to wood species recognition. Forestry Machinery and Woodworking Equipment 37: 20-22.
Gscholar
Design of an intelligent wood species recognition system. International Journal of Simulation: Systems, Science & Technology 9 (3): 9-19.
Gscholar
Combining textural descriptors for forest species recognition. In: Proceedings of the “IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society”. Universite du Quebec (Montreal, Canada) 25-28 Oct 2012. IEEExplora, pp. 1483-1488.
CrossRef | Gscholar
Análise de imagens digitais, princípios, algoritmos e aplicações [Analysis of digital images, principles, algorithms and applications]. Editora Thomson Learning, Brazil, pp. 506. [in Portuguese]
Gscholar
A importância da identificação botânica nos inventários florestais: o exemplo do “tauari” (Couratari spp. e Cariniana spp. - Lecythidaceae) em duas áreas manejadas no estado do Pará [The importance of botanical identification in forest inventories: the example of “tauari” (Couratari spp. and Cariniana spp. - Lecythidaceae) in two managed areas in the state of Pará]. Acta Amazonica 38 (1): 31-44. [in Portuguese]
CrossRef | Gscholar
Learning internal representations by error back propagation. In: “Parallel Distributed Processing: Explorations in the Microstructure of Cognition”. MIT Press, Cambridge, MA, USA, vol. 1, pp. 318-369.
Gscholar
Analysis of wood classification using L*a*b* color space. Forestry Machinery and Woodworking Equipment 35 (2007): 28-30.
Gscholar
Research on the classification of wood texture based on gray level co- occurrence matrix. Journal of Harbin Institute of Technology 37: 1667-1670.
Gscholar
A morphological feature extraction method of wood pores based on an improved growing region algorithm. Journal of Beijing Forestry University 33: 64-69.
Gscholar