Potential of Texture Analysis for Charcoal Classification

Andrade, Bruno Geike de; Vital, Benedito Rocha; Carneiro, Angélica de Cássia Oliveira; Basso, Vanessa Maria; Pinto, Francisco de Assis de Carvalho

doi:10.1590/2179-8087.124117

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Machines can be easily misled by factors such as the source of images, which can be acquired in the field using mobile phones [89] or in a laboratory-controlled environment [70,113], and variables including different thicknesses, orientation, staining, digital artefacts and other variations, which is why many thin sections from historical wood collections are useful only to the trained human eye [10].…”

Section: Image Processingmentioning

confidence: 99%

Computer Vision-Based Wood Identification: A Review

Silva

Bordalo

Pissarra

et al. 2022

Forests

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Wood identification is an important tool in many areas, from biology to cultural heritage. In the fight against illegal logging, it has a more necessary and impactful application. Identifying a wood sample to genus or species level is difficult, expensive and time-consuming, even when using the most recent methods, resulting in a growing need for a readily accessible and field-applicable method for scientific wood identification. Providing fast results and ease of use, computer vision-based technology is an economically accessible option currently applied to meet the demand for automated wood identification. However, despite the promising characteristics and accurate results of this method, it remains a niche research area in wood sciences and is little known in other fields of application such as cultural heritage. To share the results and applicability of computer vision-based wood identification, this paper reviews the most frequently cited and relevant published research based on computer vision and machine learning techniques, aiming to facilitate and promote the use of this technology in research and encourage its application among end-users who need quick and reliable results.

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Section: Image Processingmentioning

confidence: 99%

Computer Vision-Based Wood Identification: A Review

Silva

Bordalo

Pissarra

et al. 2022

Forests

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“…from charcoal of native species. Andrade et al (2019) proposed a system of classification of the origin of the charcoal using analysis of texture in digital images of the cross-section plane. For this, a database was produced containing 900 images of 18 species, 12 native and 6 of the genus Eucalyptus sp.…”

Section: Introductionmentioning

confidence: 99%

Automatic identification of charcoal origin based on deep learning

Neto¹,

Rodrigues²,

Mari³

et al. 2021

Maderas, Cienc. tecnol.

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The differentiation between the charcoal produced from (Eucalyptus) plantations and native forests is essential to control, commercialization, and supervision of its production in Brazil. The main contribution of this study is to identify the charcoal origin using macroscopic images and Deep Learning Algorithm.We applied a Convolutional Neural Network (CNN) using VGG-16 architecture, with preprocessing based on contrast enhancement and data augmentation with rotation over the training set images. on the performance of the CNN with fine-tuning using 360 macroscopic charcoal images from the plantation and native forests. The results pointed out that our method provides new perspectives to identify the charcoal origin, achieving results upper 95 % of mean accuracy to classify charcoal from native forests for all compared preprocessing strategies.

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“…GT is mainly applied to achieve the frequency eigenvector of wood material images, which can be used to describe the effect of recognizing wood surface defects [9,10]. Regarding the application of GLCM, there are 14 texture characteristic parameters in GLCM applications and some scholars screened for the best combination of characteristic parameters by different methods to identify wood texture and wood species [11,12]. In addition, others have also used different combinations of parameters of GLCM to identify wood surface defects [11,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Testing of Mechanical Properties of Bamboo–Wood Composite Container Floor by Image Processing

Jiang

Liang

et al. 2021

Forests

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The bamboo–wood composite container floor (BWCCF) has been wildly utilized in transportation in recent years. However, most of the common approaches of mechanics detection are conducted in a time-consuming and resource wasting way. Therefore, this paper aims to provide a frugal and highly efficient method to predict the short-span shear stress, the modulus of rupture (MOR) and the modulus of elasticity (MOE) of the BWCCF. Artificial neural network (ANN) models were developed and support vector machine (SVM) models were constructed for comparative study by taking the characteristic parameters of image processing as input and the mechanical properties as output. The results show that the SVM models can output better values than the ANN models. In a prediction of the three mechanical properties by SVMs, the correlation coefficients (R) were determined as 0.899, 0.926, and 0.949, and the mean absolute percentage errors (MAPE) were obtained, 6.983%, 5.873%, and 4.474%, respectively. The performance measures show the strong generalization of the SVM models. The discoveries in this work provide new perspectives on the study of mechanical properties of the BWCCF combining machine learning and image processing.

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Potential of Texture Analysis for Charcoal Classification

Cited by 4 publications

References 20 publications

Computer Vision-Based Wood Identification: A Review

Computer Vision-Based Wood Identification: A Review

Automatic identification of charcoal origin based on deep learning

Nondestructive Testing of Mechanical Properties of Bamboo–Wood Composite Container Floor by Image Processing

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