# Advanced wood species identification based on multiple anatomical sections
and using deep feature transfer and fusion

Kallil M. Zielinski  Leonardo Scabini  Lucas C. Ribas  Núbia R. da Silva  Hans
Beeckman  Jan Verwaeren  Odemir M. Bruno  Bernard De Baets  São Carlos
Institute of Physics, University of São Paulo, Brazil Institute of
Biosciences, Humanities and Exact Sciences, São Paulo State University, Brazil
Federal University of Catalão, Brazil Royal Museum for Central Africa, Belgium
BIOVISM, Dept. of Data Analysis and Mathematical Modelling, Ghent University,
Belgium KERMIT, Dept. of Data Analysis and Mathematical Modelling, Ghent
University, Belgium

###### Abstract

In recent years, we have seen many advancements in wood species
identification. Methods like DNA analysis, Near Infrared (NIR) spectroscopy,
and Direct Analysis in Real Time (DART) mass spectrometry complement the long-
established wood anatomical assessment of cell and tissue morphology. However,
most of these methods have some limitations such as high costs, the need for
skilled experts for data interpretation, and the lack of good datasets for
professional reference. Therefore, most of these methods, and certainly the
wood anatomical assessment, may benefit from tools based on Artificial
Intelligence. In this paper, we apply two transfer learning techniques with
Convolutional Neural Networks (CNNs) to a multi-view Congolese wood species
dataset including sections from different orientations and viewed at different
microscopic magnifications. We explore two feature extraction methods in
detail, namely Global Average Pooling (GAP) and Random Encoding of Aggregated
Deep Activation Maps (RADAM), for efficient and accurate wood species
identification. Our results indicate superior accuracy on diverse datasets and
anatomical sections, surpassing the results of other methods. Our proposal
represents a significant advancement in wood species identification, offering
a robust tool to support the conservation of forest ecosystems and promote
sustainable forestry practices.

###### keywords:

Wood species identification , Texture analysis , Transfer learning ,
Convolutional Neural Networks , Feature extraction

††journal: Computers and Electronics in Agriculture

##  1 Introduction

Wood is a versatile and renewable resource that can be produced in a
sustainable way. It is widely used in many industries such as construction,
furniture and paper production. The global demand for wood has led to the
emergence of illegal logging and trade, having environmental, social, and
economic repercussions. Illegal wood trade represents a significant portion of
global wood trade, with percentages increasing in regions such as Southeast
Asia, Central Africa, and South America [1]. This illicit trade, worth
billions of dollars annually, also threatens ecosystems due to the over-
exploitation of rare and protected species. To combat this issue, various
protection measures, such as the Convention on International Trade in
Endangered Species of Wild Fauna and Flora (CITES) [2], and policy measures
like the European Union Timber Regulation (EUTR) and the U.S. Lacey Act have
been implemented [3].

The effective implementation of the above policies and regulations also
requires efficient methods for identifying wood species as well as robust
datasets. Currently, wood species identification is primarily done through
wood anatomical analysis, which involves the examination of tissue and cell
diagnostic features using various imaging tools such as hand lenses, light or
electronic microscopes, 2D and 3D scans, among others. Also, the International
Association of Wood Anatomists (IAWA) has developed a list of standardized
microscopic diagnostic features that can be used to identify hardwood species
based on anatomical patterns, such as vessels, rays, parenchyma, and fibers
[4]. Although this approach is widely applied, readily available, and cost-
effective, it can sometimes fail to distinguish between closely related taxa
or determine the exact species [5, 6].

Alternative methods for wood species identification have been gradually
developed, including DNA analysis, Near Infrared spectroscopy, and Direct
Analysis in Real Time (DART) mass spectrometry [7, 8, 9, 10, 11]. These
methods show promising results but are still hindered by factors such as high
costs, the need for skilled experts for data interpretation, and the lack of
reference datasets. Recently, pattern recognition techniques employing machine
vision for automated wood species identification have emerged as a feasible
and attractive solution. This approach is less dependent on expert knowledge
and can leverage existing datasets containing high-quality microscopy images
[12, 13].

The state-of-the-art in wood species identification has seen significant
progress through the incorporation of automated classification techniques
based on macroscopic and microscopic images. Several studies [14, 15, 16, 17]
have indeed reported promising results. However, these studies either focus on
a limited number of species or rely on morphological wood features, which are
dependent on segmentation and may consequently yield variable outcomes.

Texture analysis has emerged as a promising technique, as it can describe the
spatial organization of pixels and the variation of patterns in an area on the
surface of the studied object. Filho et al. [18] and Wang et al. [19] used
texture attributes derived from macroscopic images for wood species
identification. Martins et al. [20] and Cavalin et al. [21] employed texture
features to identify wood species from the Brazilian flora using microscopic
transverse cross-sections. These studies made use of Local Phase Quantization
(LPQ), Local Binary Patterns (LBP) and gray-level co-occurence matrices as
feature descriptors.

Various computer vision models have been employed to automate wood species
identification using digital imagery of anatomical sections. These models
typically involve collecting a representative dataset of labeled digital
images, applying a feature extraction procedure, and training a machine
learning classification algorithm. Martins et al. [22] achieved an accuracy of
86% using LBP as a feature descriptor combined with Support Vector Machines
(SVMs). Filho et al. [18] used a strategy of dividing the image into sub-
images, classifying them independently using SVMs, and fusing the class
probabilities through a fusion rule, achieving an accuracy of 97.77% for 41
different species.

Recent studies have also employed deep convolutional neural networks (CNNs)
for wood species identification. Ravindran et al. [23] obtained an accuracy of
87.4% using CNNs on a dataset of 2303 macroscopic images from the Meliaceae
family. Another work [24] proposed a different CNN model to identify 12 common
species in the United States based on macroscopic imagery of transverse
sections reaching an accuracy of 97.7%. Lens et al. [25] proposed yet another
CNN model achieving a similar accuracy of over 98% on 2240 images from 112
species.

Transfer learning techniques have recently emerged as a promising approach to
image classification in various domains, including wood species
identification. Transfer learning enables the application of knowledge gained
from one task to a different but related task. It has been particularly
successful in situations where the target task has only limited labeled data
available, by leveraging pre-trained backbones on larger, more diverse
datasets [26]. In the context of wood species identification, transfer
learning can be used to fine-tune pre-trained backbones, such as CNNs, to
effectively learn the intricate features and patterns of wood species from the
available datasets [27].

The application of transfer learning to wood species identification can
further improve the efficiency and accuracy of classification models,
especially when combined with the aforementioned methods such as LBP, SVMs,
and CNNs. For example, Zhao et al. [28] employed transfer learning with a pre-
trained CNN model for wood species identification, achieving an accuracy of
95.2% on a dataset of 1832 macroscopic images from 32 species. This approach
not only yielded impressive classification results but also reduced the time
and computational resources required for training the model.

Despite the significant advancements in wood species identification, the
African continent, particularly the Congo Basin, remains underrepresented. To
address this gap, we leverage the commercial timber species dataset from the
Democratic Republic of Congo (DRC) provided by the Belgian Royal Museum for
Central Africa (RMCA) [29], focusing on texture features extracted from
distinct microscopic cross-sections, which have shown notable discriminative
potential in wood classification in works by Rosa da Silva et al. [30, 31]. To
further advance the field of wood species identification, our work applies
transfer learning to the timber species dataset from the DRC. This dataset
includes 805 images representing 77 distinct wood species across three
anatomical sections: transversal, tangential, and radial. In their approach,
Rosa da Silva et al. [31] employed the LPQ method [32] on each anatomical
section, followed by a separate classification using a random forest [33]
algorithm, and a subsequent concatenation of the probability matrices to feed
a logistic regression model. Building upon this foundation, we propose two
novel transfer learning approaches that harness the power of pre-trained
backbones and advanced learning techniques, aiming to enhance the
identification efficiency and accuracy of wood species.

The first approach employs a serial activation map fusion using Global Average
Pooling (GAP) on a backbone CNN model that has been pre-trained on ImageNet, a
large-scale image classification dataset containing millions of samples
spanning thousands of object categories [34]. This allows us to benefit from a
richer set of feature representations learned on diverse images.

The second approach involves the use of RADAM (Random encoding of Aggregated
Deep Activation Maps), a feature extractor based on randomized auto-encoders
that has demonstrated state-of-the-art accuracy on texture recognition tasks
[35]. By incorporating RADAM into our wood species identification pipeline, we
aim to further optimize the classification performance by exploiting the
advanced learning capabilities of this approach.

Through the implementation of the above approaches, we seek to improve upon
the results achieved by Rosa da Silva et al. [31] and contribute to the
development of more efficient and more accurate wood species identification
models, ultimately promoting the protection and conservation of forest
ecosystems.

##  2 Materials and Methods

This section presents the materials and methods used in our study, including
the dataset, the feature extraction methods adopted and the parameters of the
experimental configurations employed. The following subsections provide a
detailed overview of these components.

###  2.1 Datasets

Our research uses the timber species dataset from the DRC, which was assembled
by Rosa da Silva et al. [30]. This dataset encompasses a total of 78 different
Congolese timber species, each represented by a set of high-resolution images
characterized by a resolution of $1000\times 1000$ pixels. The uniqueness of
this dataset lies in its anatomical diversity. For every timber species
included, the dataset provides images from three distinct anatomical planes:
tangential, transversal and radial. This approach offers a comprehensive view
of each species, facilitating a more accurate identification process. In
summary, the dataset is composed of 805 images for each anatomical plane,
resulting in a significant volume of data for our method. Figure 1 displays an
illustrative representation of the images from each anatomical section in this
dataset, which shows the different visual characteristics present in each
anatomical plane.

Figure 1: Representation of each anatomical section of a wood sample from the
species Afzelia africana. The arrows point from the respective regions on the
sample (transversal, tangential, and radial) to their corresponding images.
Each image offers unique structural characteristics that are crucial for wood
species identification.

The dataset contains an average of 10 images per class, which is a relatively
low number for machine learning tasks. To overcome this limitation, Rosa da
Silva et al. [31] applied some data augmentation techniques to increase the
number of images per class. Starting from the original dataset consisting of
$1000\times 1000$ pixel images, they created three additional datasets, each
with varying image dimensions and based on different transformations. The
procedures leading to the different sub-datasets are detailed below:

  * 1.

$1000\times 1000$ (Original): This dataset is referred to as the ‘original’,
because it contains the initial images as previously described. It includes
805 images for each of the three anatomical sections, with each image
consisting of $1000\times 1000$ pixels;

  * 2.

$1000\times 500$: This dataset is derived from the original by dividing each
image into two equal halves, effectively doubling the total number of images
compared to the original dataset;

  * 3.

$500\times 500$: Each image in the $1000\times 500$ dataset is divided once
more, resulting in four separate image sections from each original image in
the $1000\times 1000$ dataset;

  * 4.

$500\times 500$-OGRN: This dataset is equivalent to the $500\times 500$
dataset in terms of image subdivision but also includes the results of several
transformations applied to each quarter section of the original images. The
first quarter remains identical to the original (O), the second quarter
undergoes a Gaussian smoothing operation (G), the third one is rotated by 90
degrees (R), and the final one comes with added salt-and-pepper noise with a
density of 0.05 (N).

These additional datasets created from the original set of 1000 $\times$ 1000
images not only increase the quantity of training data but also introduce a
range of variations that improve the robustness of the machine learning models
trained on them. These data augmentation processes are visually demonstrated
in Figure 2, where each subfigure corresponds to a different dataset created
from the original set of 1000 $\times$ 1000 images. This visualization allows
for a better understanding of the transformations applied.

Figure 2: Illustration of the data augmentation techniques employed for each
sub-dataset. (a) 1000 $\times$ 1000 original dataset, (b) 1000 $\times$ 500
dataset with each image halved, (c) 500 $\times$ 500 dataset with each
original image split into four equal parts, and (d) 500 $\times$ 500 - OGRN
dataset demonstrating the applied transformations including original (O),
Gaussian smoothing (G), 90-degree rotation (R), and salt-and-pepper noise
addition (N).

###  2.2 Feature Extraction Methods

Texture analysis using CNNs typically adopts end-to-end models that leverage a
pre-trained backbone, followed by fine-tuning of the architecture specific to
the texture recognition problem at hand. In contrast, our study employs two
approaches that use the backbone only as a feature extractor with a dedicated
classifier applied to the resulting features. Notably, this process eliminates
the need for fine-tuning of the architecture.

####  2.2.1 GAP

The first approach we consider is using GAP computed over the activation maps
produced by the last layer of the backbone, which are subsequently fed to an
SVM classifier [36]. Let us denote the function representing the pre-trained
backbone as $f$, and the input image from each of the anatomical planes
(tangential, transversal, and radial) as $X_{\mathrm{T}}$, $X_{\mathrm{Tr}}$,
and $X_{\mathrm{R}}$, respectively, with
$X_{\mathrm{T}},X_{\mathrm{Tr}},X_{\mathrm{R}}\in\mathbb{R}^{w_{0}\times
h_{0}\times 3}$. We extract features from the last layer of the backbone as
follows:

$Z_{{\mathrm{T}}}=f(X_{\mathrm{T}}),\quad
Z_{{\mathrm{Tr}}}=f(X_{{\mathrm{Tr}}}),\quad
Z_{\mathrm{R}}=f(X_{\mathrm{R}})\,.$ (1)

For each wood sample, we have three feature vectors corresponding to the three
anatomical planes. We can combine these features in two ways:

  1. 1.

Serial Feature Concatenation (SFC): We concatenate the feature vectors end-to-
end. This can be defined mathematically as:

$Z_{s}=[Z_{\mathrm{T}},Z_{{\mathrm{Tr}}},Z_{\mathrm{R}}]\,.$ (2)

  2. 2.

Parallel Feature Merging (PFM): We perform an element-wise summation of the
feature vectors:

$Z_{p}=Z_{\mathrm{T}}\oplus Z_{{\mathrm{Tr}}}\oplus Z_{\mathrm{R}}\,.$ (3)

Finally, we use the combined features ($Z_{s}$ or $Z_{p}$) as input to an SVM
classifier. Let $h$ denote the SVM, then the predicted class label is given
by:

$\hat{y}=h(Z_{s})\quad\text{or}\quad\hat{y}=h(Z_{p})\,.$ (4)

####  2.2.2 RADAM

The second approach we adopt is RADAM [35], which significantly differs from
GAP. Instead of considering only the last layer of a pre-trained backbone,
RADAM takes into account multiple activation maps at different depths in the
architecture. This approach allows to capture various levels of texture
characteristics, from simple to complex features.

In the RADAM method, the features are combined through a specific sequence of
steps, including a Randomized Autoencoder (RAE) [37, 38]. A RAE is a type of
neural network that consists of one hidden layer and aims to reproduce its
input as target output, i.e., $Y=X$. The input weights of the RAE are randomly
generated using a Linear Congruential Generator (LCG) [39]. After training the
RAE using the least-squares method, the output weights are used as features in
our approach, as we describe below.

Let us now describe each step of the RADAM method. We denote the function
representing the pre-trained backbone as $f$, and its intermediate activation
maps at different depths $i$ as $f_{i}$. Each depth level corresponds to the
output of a specific convolutional block in the backbone. For each input image
of the three anatomical planes ($X_{\mathrm{T}}$, $X_{{\mathrm{Tr}}}$ and
$X_{\mathrm{R}}$) and each depth $i$, we extract the corresponding activation
map:

$A_{{\mathrm{T}},i}=f_{i}(X_{\mathrm{T}}),\quad
A_{{\mathrm{Tr}},i}=f_{i}(X_{{\mathrm{Tr}}}),\quad
A_{{\mathrm{R}},i}=f_{i}(X_{\mathrm{R}})\,.$ (5)

Next, we compute the depthwise $2p$-norm of the activation maps. Let us denote
the depthwise norm function as $g$, which takes the activation map as input
and applies the $2p$-norm along the depth (channel) dimension. The computed
norms for the three anatomical planes at depth $i$ are given by:

$N_{{\mathrm{T}},i}=g(A_{{\mathrm{T}},i}),\quad
N_{{\mathrm{Tr}},i}=g(A_{{\mathrm{Tr}},i}),\quad
N_{{\mathrm{R}},i}=g(A_{{\mathrm{R}},i})\,.$ (6)

The computed norms are then concatenated along the third dimension (channel
dimension $z_{i}$). However, since the spatial dimensions ($w_{i}$ and
$h_{i}$) of the activation maps might differ across different depths, we
resize the spatial dimension of all activation maps to the same spatial size
using bilinear interpolation. The concatenations of the normalized activation
maps for the three anatomical planes are denoted as:

$C_{\mathrm{T}}=\bigoplus_{i=1}^{n}N_{{\mathrm{T}},i},\quad
C_{{\mathrm{Tr}}}=\bigoplus_{i=1}^{n}N_{{\mathrm{Tr}},i},\quad
C_{\mathrm{R}}=\bigoplus_{i=1}^{n}N_{{\mathrm{R}},i}\,,$ (7)

where $\bigoplus$ denotes the concatenation operation along the third
dimension for each computed anatomical section norm, with $i$ varying from $1$
to $n$, and $n$ representing the number of depths considered in the RADAM
approach.

Next, we apply a set of $m$ RAEs to the concatenated activation maps
$C_{{\mathrm{T}}}$, $C_{{\mathrm{Tr}}}$ and $C_{{\mathrm{R}}}$. The projection
obtained by the output weights of each RAE is considered as the encoded
representation of the activation map. This representation is denoted as
$r_{j}$ for the $j$-th RAE:

$Z_{{\mathrm{T}},i}=r_{j}(C_{{\mathrm{T}}}),\quad
Z_{{\mathrm{Tr}},j}=r_{j}(C_{{\mathrm{Tr}}}),\quad
Z_{{\mathrm{R}},j}=r_{j}(C_{{\mathrm{R}}})\,.$ (8)

After repeating this process for $m$ RAEs, the representations are combined
using the PFM strategy, summating the features for each anatomical section
image:

$Z_{{\mathrm{T}}}=\sum_{j=1}^{m}Z_{{\mathrm{T}},j},\quad
Z_{{\mathrm{Tr}}}=\sum_{j=1}^{m}Z_{{\mathrm{Tr}},j},\quad
Z_{{\mathrm{R}}}=\sum_{j=1}^{m}Z_{{\mathrm{R}},j}\,.$ (9)

In a last step before inputting these features into a classifier, we combine
the features of all three anatomical sections using the SFC or PFM strategy
just as for GAP (see Eqs. (2)) and (3)). Finally, these combined features
serve as input to an SVM classifier, which performs the final classification
task.

In order to facilitate the understanding of the overall process, Figure 3
shows the methodology adopted in this paper. The individual anatomical
sections (radial, tangential and transversal) are first input through the pre-
trained backbone, resulting in their respective feature blocks. These features
are then combined through SFC or PFM, forming a unified feature
representation. This feature representation is then passed through an SVM
classifier for final wood species classification. The output of the model is
the predicted wood species, derived from the combined and classified features
of the three anatomical sections.

Figure 3: Methodological process employed in this study. Anatomical sections
are input to a pre-trained backbone to extract feature blocks. These blocks
are then fused and classified using an SVM to produce the final wood species
prediction.

###  2.3 Experimental Configuration

This section describes the experimental setup, including the libraries,
parameters of the methods and classification algorithms used in this study.

####  2.3.1 Libraries

Our feature extraction methods were implemented using PyTorch [40], a popular
open-source machine learning library. This platform is especially beneficial
due to its application for training and manipulating deep learning models. To
investigate the performance of some backbones on our dataset, we employed
Pytorch Image Models (TIMM) [41], which includes a wide-range of pre-trained
computer vision backbones, facilitating the implementation of different
architectures. The classification process was realized through the use of the
SVM implementation in the Scikit-learn [42] library.

####  2.3.2 Backbones

We experimented with four different pre-trained backbones for our feature
extraction methods. Specifically, we used ResNets [43] (18 and 50), both of
which provide five distinct depths for feature extraction. In addition to
ResNets, we also considered two variants of the ConvNext architecture [44]:
ConvNext-Large-In22k and ConvNext-Xlarge-In22k. Both are pre-trained on the
ImageNet-22k dataset, and have four different feature extraction depths. To
further detail the complexity of each backbone, Table 1 outlines their number
of parameters and GFLOPs.

Table 1: Number of parameters for each backbone and respective GFLOPs used in this paper. Backbone | N. of Params (millions) | GFLOPs
---|---|---
ResNet-18 |  11.7 | 1.8
ResNet-50 |  25.5 | 4.1
ConvNext Large |  230 | 34.4
ConvNext Xlarge |  392 | 60.9

####  2.3.3 RADAM and Feature Extraction Configuration

The feature extraction methods in this study are applied to images with a
$224\times 224$ resolution to reduce the computational cost. In addition, the
RADAM method incorporates the use of 4 RAEs, following the configuration
suggested by the authors [35], which provides a balance between performance
and computational cost.

The performance of the different backbones was evaluated across three
combinations of anatomical sections: Transversal only, Transversal +
Tangential and Transversal + Tangential + Radial. Moreover, for each of these
section combinations, we also compared the performance of both feature fusion
strategies described in Section 2.2, i.e., Serial Feature Concatenation (SFC)
and Parallel Feature Merging (PFM).

####  2.3.4 Classification Process

The SVM was used as the primary classification algorithm for this study. We
use a linear kernel and $C=1$, and no hyperparameter tuning was done. For each
of the datasets detailed in Section 2.1, we adopted a 10-fold cross-validation
strategy and repeated the classification process 10 times to ensure a
comprehensive evaluation of the SVM’s performance across datasets.
Furthermore, we computed the average accuracy and its standard deviation over
the 10 repetitions. These were the key metrics for assessing the performance
of our proposed models. This provides a balanced view of the model’s
performance, taking into account both the model’s correctness and consistency
across different runs.

##  3 Results

In this section, we present and discuss the results obtained with each of the
two feature extraction models.

###  3.1 GAP Results

In the first phase of our investigation with GAP, we explored our approach by
analyzing the accuracies achieved for each individual anatomical section, for
all datasets and all backbones. This examination aims to provide detailed
insights into the strength and performance of GAP when applied to separate
anatomical sections. It further aids in identifying the contribution of each
section towards the overall accuracy in species identification. We present the
results for each anatomical section (Transversal (Tr), tangential (T) and
radial (R)) in Table 2, across all four datasets: Original ($1000\times
1000$), $1000\times 500$, $500\times 500$ and $500\times 500$-OGRN and for
each backbone in Table 1.

Table 2: Accuracies (expressed as percentages) and standard deviations of SVM
using GAP features for each individual anatomical section, dataset and
backbone.

|  | Backbone
---|---|---
Dataset | Section | Resnet18 | Resnet50 | Convnext Large | Convnext XLarge
Original | Tr | 67.00$\pm{0.90}$ | 61.70$\pm{0.50}$ | 79.90$\pm{1.10}$ | 82.20$\pm{1.10}$
T | 74.50$\pm{0.60}$ | 67.30$\pm{0.80}$ | 87.30$\pm{0.80}$ | 88.20$\pm{0.60}$
R | 57.30$\pm{0.90}$ | 52.40$\pm{0.70}$ | 75.40$\pm{1.20}$ | 77.60$\pm{0.80}$
$1000\times 500$ | Tr | 85.70$\pm{0.50}$ | 82.70$\pm{0.40}$ | 96.10$\pm{0.30}$ | 96.30$\pm{0.40}$
T | 82.60$\pm{0.40}$ | 82.20$\pm{0.20}$ | 95.00$\pm{0.60}$ | 95.40$\pm{0.40}$
R | 75.30$\pm{0.50}$ | 73.30$\pm{0.40}$ | 91.00$\pm{0.40}$ | 91.50$\pm{0.40}$
$500\times 500$ | Tr | 91.00$\pm{0.30}$ | 90.20$\pm{0.40}$ | 98.10$\pm{0.10}$ | 98.70$\pm{0.20}$
T | 88.30$\pm{0.40}$ | 89.60$\pm{0.30}$ | 97.40$\pm{0.20}$ | 97.50$\pm{0.10}$
R | 76.90$\pm{0.30}$ | 79.00$\pm{0.20}$ | 93.10$\pm{0.20}$ | 92.90$\pm{0.30}$
$500\times 500$-OGRN | Tr | 78.40$\pm{0.50}$ | 74.90$\pm{0.80}$ | 91.50$\pm{0.40}$ | 92.60$\pm{0.50}$
T | 79.60$\pm{0.70}$ | 78.50$\pm{0.30}$ | 91.50$\pm{0.50}$ | 93.50$\pm{0.40}$
R | 60.60$\pm{0.50}$ | 61.70$\pm{0.50}$ | 82.80$\pm{0.50}$ | 83.70$\pm{0.50}$

Our results indicate that the Tr and T anatomical sections provide more
discriminative power for GAP in comparison to the radial (R) section. This
could be attributed to the unique cellular arrangement and structures visible
in the transversal and tangential sections, which may provide more distinctive
features for the identification process. The radial section, while still
providing valuable insights, seems to exhibit a less discriminative
performance, possibly due to its inherent similarity among various species.

There are also some interesting insights that can be derived from the
classification results of the individual anatomical sections. Figure 4
displays the confusion matrix of the results for the transversal anatomical
section. Class b) Chrysophyllum africanum tends to be misidentified as d)
Pentaclethra eetveldeana, which is also misclassified with c) Mammea africana.
This highlights the challenge of distinguishing between these species in the
transversal plane, suggesting a need for more discriminative features for
these species. Many features commonly identified as diagnostic are usually
observed on a radial section, often requiring higher microscopic magnification
for clear visibility. These characteristics are not easily distinguishable in
the available images captured at lower magnifications.

Figure 4: Confusion matrix for the GAP results on the transversal anatomical
section in the original dataset. Class b) Chrysophyllum africanum is often
misclassified with d) Pentaclethra eetveldeana, which is also confused with c)
Mammea africana class.

Proceeding to the results for the tangential anatomical section shown in
Figure 5, one can note that the model frequently confuses b) Klainedoxa
gabonensis and c) Lophira alata. Another class, d) Pericopsis elata, often
finds itself misclassified across various species.

Figure 5: Confusion matrix for the GAP results on the tangential anatomical
section in the original dataset. The model often misclassifies c) Lophira
alata with b) Klainedoxa gabonensis, which is in general correctly classified.
Also, class d) Pericopsis elata is often confused with other several species.

Finally, for the radial anatomical section as represented by Figure 6, b)
Leplaea laurentii generally tends to be correctly classified, indicating
effective differentiation by the model in this case. However, d) Pericopsis
elata is frequently misidentified as Leplaea laurentii, indicating a gap in
the model’s discriminatory power between these two classes. Also, c) Newtonia
leucocarpa has a significantly low classification accuracy, reflecting its
challenging identification in the radial plane.

Figure 6: Confusion matrix for the GAP results on the radial anatomical
section in the original dataset. Class b) Leplaea laurentii is in general
correctly classified, but d) Pericopsis elata is frequently confused with
Leplaea laurentii. Also, d) Newtonia leucocarpa has a very low classification
accuracy.

Building upon these observations, we further analyze the use of combinations
of anatomical sections, specifically transversal with tangential (Tr + T) and
also all sections combined (Tr + T + R). Our goal is to investigate whether
the complementary information from multiple sections can enhance the accuracy,
and if so, to what extent. Incorporating more than one anatomical section
allows the model to harness a broader spectrum of wood features, potentially
improving the overall classification performance. The corresponding results
are listed in Table 3.

Table 3: Accuracies (expressed as percentages) and standard deviations of SVM
using GAP features for different combinations of anatomical sections, and each
fusion strategy, dataset and backbone.

|  |  | Backbone
---|---|---|---
Dataset | Combination | Fusion Strategy | Resnet18 | Resnet50 | Convnext Large | Convnext XLarge
Original | Tr+T | SFC | 85.70$\pm{0.50}$ | 76.90$\pm{0.60}$ | 91.20$\pm{0.70}$ | 91.80$\pm{0.70}$
| PFM | 84.70$\pm{0.80}$ | 78.30$\pm{0.60}$ | 90.5$\pm{0.60}$ | 91.30$\pm{0.40}$
Tr+T+R | SFC | 87.40$\pm{0.90}$ | 80.80$\pm{0.50}$ | 92.50$\pm{0.50}$ | 93.10$\pm{0.70}$
| PFM | 86.40$\pm{0.90}$ | 83.0 $\pm{0.50}$ | 92.10$\pm{0.60}$ | 93.20$\pm{0.60}$
$1000\times 500$ | Tr+T | SFC | 95.10$\pm{0.50}$ | 93.00$\pm{0.50}$ | 98.90$\pm{0.20}$ | 99.00$\pm{0.30}$
| PFM | 92.80$\pm{0.60}$ | 93.20$\pm{0.50}$ | 98.70$\pm{0.40}$ | 98.60$\pm{0.40}$
Tr+T+R | SFC | 96.80$\pm{0.40}$ | 94.50$\pm{0.40}$ | 99.20$\pm{0.30}$ | 99.30$\pm{0.20}$
| PFM | 95.00$\pm{0.40}$ | 94.90$\pm{0.40}$ | 98.80$\pm{0.20}$ | 98.90$\pm{0.30}$
$500\times 500$ | Tr+T | SFC | 97.80$\pm{0.10}$ | 97.50$\pm{0.10}$ | 99.80$\pm{0.10}$ | 99.80$\pm{0.00}$
| PFM | 96.00$\pm{0.20}$ | 97.00$\pm{0.10}$ | 99.50$\pm{0.10}$ | 99.60$\pm{0.10}$
Tr+T+R | SFC | 97.20$\pm{0.30}$ | 98.30$\pm{0.20}$ | 99.80$\pm{0.00}$ | 99.80$\pm{0.10}$
| PFM | 97.00$\pm{0.20}$ | 97.70$\pm{0.10}$ | 99.60$\pm{0.10}$ | 99.70$\pm{0.10}$
$500\times 500$-OGRN | Tr+T | SFC | 92.40$\pm{0.40}$ | 89.5$\pm{0.40}$ | 97.20$\pm{0.30}$ | 97.90$\pm{0.20}$
| PFM | 89.60$\pm{0.50}$ | 89.80$\pm{0.50}$ | 96.30$\pm{0.30}$ | 97.50$\pm{0.20}$
Tr+T+R | SFC | 93.40$\pm{0.40}$ | 92.50$\pm{0.50}$ | 97.70$\pm{0.20}$ | 98.20$\pm{0.20}$
| PFM | 91.10$\pm{0.40}$ | 92.50$\pm{0.50}$ | 97.00$\pm{0.30}$ | 97.90$\pm{0.20}$

We start the discussion of the results with the performance of the model on
the original dataset, which has fewer images per class compared to the other
datasets. As the results suggest, this dataset yielded the lowest accuracy
among all datasets, with the highest accuracy of $93.2\pm{0.6}$ obtained when
all the anatomical sections are considered using the PFM fusion strategy and
the ConvNext XLarge backbone.

In contrast, the accuracy of the model dramatically improved when we increased
the number of images per class in the datasets. For instance, on the
$1000\times 500$ dataset, it reached an accuracy of $99.3\pm{0.2}$ (all
anatomical sections, SFC fusion strategy and ConvNext XLarge backbone), and on
the $500\times 500$ dataset, it reached an even higher performance of
$99.8\pm{0.0}$ (all anatomical sections, SFC fusion strategy and ConvNetx
Large). Notably, on the $500\times 500$-OGRN dataset, which contains the same
number of images but incorporates noise, the model maintained a high accuracy
of $98.2\pm{0.2}$ when using the ConvNext XLarge backbone. This result
underlines the observation that increasing the number of samples per class in
a dataset significantly enhances the performance of the model.

Another factor that has a positive impact on the model performance is the
inclusion of more anatomical sections in the analysis. This is clear from the
results obtained on the original dataset using the ConvNext Large backbone.
When we considered the transversal section (see Table 2) and the ResNet18
backbone, the accuracy was $67.0\pm{0.90}$. However, the accuracy increased to
$85.7\pm{0.5}$ upon the inclusion of the tangential section and further
increased to $87.4\pm{0.8}$ when also the radial section was incorporated.

In terms of feature fusion strategies, one can see that SFC generally
outperforms PFM. However, it should be noted that the SFC strategy results in
a larger feature vector due to concatenation, which may, sometimes, be a
disadvantage as it can reduce the efficiency of the classification step. On
the other hand, PFM allows for a better control of the number of features
regardless of the number of terms involved in the summations.

Finally, the comparison of different backbones reveals some interesting
insights. Although ResNet18 generally outperforms ResNet50, especially when
using SFC, the ConvNext backbones display superior performance across all
datasets, regardless of the combination of sections and the fusion strategy
considered. Given that they have a considerably larger size, better pre-
training and a more advanced architecture design compared to the ResNets, this
was an expected result.

###  3.2 RADAM Results

In this section, we detail the results obtained with the RADAM feature
extraction method. Similar to GAP, we evaluate the performance across
different datasets starting with the analysis of individual anatomical
sections. The results are listed in Table 4.

Table 4: Accuracies (expressed as percentages) and standard deviations of SVM
using RADAM features for each individual anatomical setion, dataset and
backbone.

|  | Backbone
---|---|---
Dataset | Section | Resnet18 | Resnet50 | Convnext Large | Convnext XLarge
Original | Tr | 77.00$\pm{0.50}$ | 77.20$\pm{0.70}$ | 86.10$\pm{0.70}$ | 87.30$\pm{0.70}$
T | 82.00$\pm{0.40}$ | 83.70$\pm{0.70}$ | 90.30$\pm{0.90}$ | 90.50$\pm{0.60}$
R | 69.70$\pm{0.80}$ | 68.90$\pm{0.60}$ | 81.70$\pm{0.90}$ | 83.60$\pm{0.90}$
$1000\times 500$ | Tr | 94.80$\pm{0.40}$ | 94.30$\pm{0.50}$ | 97.90$\pm{0.30}$ | 98.30$\pm{0.30}$
T | 91.00$\pm{0.50}$ | 91.90$\pm{0.30}$ | 96.90$\pm{0.30}$ | 96.70$\pm{0.50}$
R | 88.80$\pm{0.40}$ | 88.70$\pm{0.70}$ | 95.00$\pm{0.30}$ | 95.30$\pm{0.30}$
$500\times 500$ | Tr | 97.30$\pm{0.20}$ | 97.50$\pm{0.20}$ | 99.10$\pm{0.10}$ | 99.40$\pm{0.10}$
T | 95.50$\pm{0.10}$ | 96.70$\pm{0.20}$ | 99.00$\pm{0.10}$ | 98.90$\pm{0.10}$
R | 90.80$\pm{0.30}$ | 91.90$\pm{0.20}$ | 96.80$\pm{0.20}$ | 96.20$\pm{0.10}$
$500\times 500$-OGRN | Tr | 87.50$\pm{0.20}$ | 87.60$\pm{0.40}$ | 94.70$\pm{0.30}$ | 94.80$\pm{0.40}$
T | 88.20$\pm{0.50}$ | 90.00$\pm{0.30}$ | 95.50$\pm{0.30}$ | 96.40$\pm{0.30}$
R | 75.30$\pm{0.50}$ | 78.10$\pm{0.50}$ | 88.70$\pm{0.30}$ | 88.70$\pm{0.60}$

The results obtained with RADAM mirror the previous observations for GAP. Both
the Tr and T sections appear to have stronger discriminative capabilities
compared to the R section. The superior performance of these sections may be
attributed to their distinctive cellular structures and arrangements that
RADAM effectively captures, thereby providing more characteristic features for
the wood species identification task.

We also made a deeper investigation for each anatomical section by inspecting
its confusion matrix, as we did for GAP. Figure 7 presents the confusion
matrix for the RADAM results on the transversal anatomical section in the
original dataset. Class b) Chrysophyllum africanum has a low classification
accuracy and is often confused with class c) Pentaclethra eetveldeana. This
confusion reflects the model’s struggle to distinctly identify these two
classes for this particular section.

Figure 7: Confusion matrix for the RADAM results on the transversal anatomical
section in the original dataset. Class b) Chrysophyllum africanum has a low
classification accuracy, and is frequently confused with class c) Pentaclethra
eetveldeana.

For the tangential anatomical section, as shown in Figure 7, class b) Leplaea
laurentii is generally accurately identified by the model. However, a
noticeable confusion emerges with class c) Pericopsis elata, which is
frequently misclassified as Leplaea laurentii.

Figure 8: Confusion matrix for the RADAM results on the tangential anatomical
section in the original dataset. Class b) Leplaea laurentii is in general
correctly classified, but it is also confused with class c) Pericopsis elata.

Finally, for the radial anatomical section, as shown in Figure 9, class b)
Albizia adianthifolia is well distinguished by the model. Yet, it is often
misclassified as class d) Newtonia leucocarpa, which shows a significant
confusion with Albizia adianthifolia. Furthermore, class c) Lophira alata is
often misclassified as one of various other species. This implies that the
model encounters difficulties in accurately classifying these species.

Figure 9: Confusion matrix for the RADAM results on the radial anatomical
section in the original dataset. Class a) Albizia adianthifolia is well
distinguished, but it is also confused with class d) Newtonia leucocarpa.
Also, class c) Lophira alata is misclassified with other many species.

These observations led us to further investigate the combination of anatomical
sections, specifically Tr + T and Tr + T + R, and also testing different
feature fusion strategies, with the intention of complementing information
from different sections to improve the classification accuracy. The results
are shown in Table 5.

Table 5: Accuracies (expressed as percentages) and standard deviations of SVM
using RADAM features for different combinations of anatomical sections, and
each fusion strategy, dataset and backbone.

|  |  | Backbone
---|---|---|---
Dataset | Combination | Fusion Strategy | Resnet18 | Resnet50 | Convnext Large | Convnext XLarge
Original | Tr+T | SFC | 90.20$\pm{0.70}$ | 89.00$\pm{0.60}$ | 93.50$\pm{0.50}$ | 94.00$\pm{0.60}$
| PFM | 88.60$\pm{0.70}$ | 88.20$\pm{0.60}$ | 92.60$\pm{0.50}$ | 93.60$\pm{0.50}$
Tr+T+R | SFC | 91.20$\pm{0.60}$ | 90.60$\pm{0.40}$ | 94.00$\pm{0.70}$ | 94.30$\pm{0.70}$
| PFM | 90.90$\pm{0.80}$ | 89.60$\pm{0.80}$ | 94.70$\pm{0.60}$ | 94.80$\pm{0.60}$
$1000\times 500$ | Tr+T | SFC | 98.10$\pm{0.40}$ | 98.00$\pm{0.30}$ | 99.40$\pm{0.30}$ | 99.30$\pm{0.30}$
| PFM | 97.70$\pm{0.30}$ | 97.90$\pm{0.40}$ | 99.20$\pm{0.30}$ | 99.20$\pm{0.30}$
Tr+T+R | SFC | 98.50$\pm{0.40}$ | 98.60$\pm{0.30}$ | 99.60$\pm{0.20}$ | 99.50$\pm{0.20}$
| PFM | 97.70$\pm{0.40}$ | 98.10$\pm{0.40}$ | 99.40$\pm{0.20}$ | 99.40$\pm{0.20}$
$500\times 500$ | Tr+T | SFC | 99.40$\pm{0.10}$ | 99.50$\pm{0.10}$ | 99.90$\pm{0.00}$ | 99.90$\pm{0.00}$
| PFM | 98.90$\pm{0.10}$ | 99.30$\pm{0.10}$ | 99.90$\pm{0.10}$ | 99.80$\pm{0.00}$
Tr+T+R | SFC | 99.50$\pm{0.10}$ | 99.60$\pm{0.10}$ | 99.90$\pm{0.00}$ | 99.90$\pm{0.10}$
| PFM | 99.10$\pm{0.10}$ | 99.30$\pm{0.10}$ | 99.80$\pm{0.00}$ | 99.80$\pm{0.00}$
$500\times 500$-OGRN | Tr+T | SFC | 96.10$\pm{0.30}$ | 95.70$\pm{0.30}$ | 98.70$\pm{0.20}$ | 98.70$\pm{0.20}$
| PFM | 94.70$\pm{0.40}$ | 95.20$\pm{0.40}$ | 98.50$\pm{0.30}$ | 98.30$\pm{0.20}$
Tr+T+R | SFC | 96.30$\pm{0.30}$ | 96.20$\pm{0.30}$ | 98.70$\pm{0.20}$ | 98.70$\pm{0.20}$
| PFM | 95.40$\pm{0.40}$ | 95.60$\pm{0.40}$ | 98.50$\pm{0.30}$ | 98.60$\pm{0.20}$

We start the discussion of the results with the performance of the model on
the original dataset, reaching its highest accuracy of $94.8\pm{0.5}$ when all
the anatomical sections are considered using the PFM feature fusion strategy
and ConvNext XLarge.

However, a remarkable performance improvement was observed as the number of
images per class in the datasets increased. Specifically, the model accuracy
increased to $99.50\pm{0.2}$ on the $1000\times 500$ dataset (all anatomical
sections, SFC strategy and ConvNext XLarge), while on the $500\times 500$
dataset it reached a nearly perfect score of $99.9\pm{0.0}$ (multiple
configurations) and finally on the $500\times 500$-OGRN dataset the model
maintained a good performance of $98.7\pm{0.2}$ (multiple configurations).
These results, similar to the previous section, confirm the model’s enhanced
performance with an increase in the number of samples per class in the
dataset.

Furthermore, the inclusion of more anatomical sections in the analysis also
increased the accuracy of the RADAM model. For instance, considering only the
transversal section in the original dataset using the ResNet18 backbone
yielded an accuracy of $77.0\pm{0.5}$ (see Table 4, which increased to
$90.2\pm{0.7}$ when including the tangential section, and further improved to
$91.2\pm{0.6}$ when incorporating the radial section, using the SFC strategy.

As for the feature fusion strategies, in general, SFC continues to be better
than PFM, but we can note that there are some cases, for example, the ConvNext
Large backbone on the original dataset with all anatomical sections, where PFM
outperforms SFC.

Lastly, the results show a similar comparison of the backbones as in the
previous section, where in many cases ResNet18 outperforms ResNet50, but in
general the ConvNexts delivered the best performance across all datasets
regardless of the combination of sections and the fusion strategy considered.

###  3.3 Overall comparison

In this subsection, we present the main results for the four distinct
datasets, taking into account different combinations of anatomical sections
for the methods introduced in this study, as well as the approach proposed by
Rosa da Silva et al [31], which applies LPQ on each section, uses a random
forest classifier on each section separately and further concatenates the
probability matrices obtained in the classification process as input to a
logistic regression model. A comprehensive comparison of these results is
presented in Table 6.

Table 6: Accuracy (expressed as percentages) and standard deviations of the
three methods for different datasets and different anatomical section
combinations. For GAP and RADAM, we used the Convnext XLarge backbone and the
SFC strategy when using more than one anatomical section.

Dataset | Anatomical Section | Method
---|---|---
GAP | RADAM | Rosa da Silva et al. [31]
Original | Tr | 82.2$\pm{1.10}$ | 87.3$\pm{0.70}$ | 56.0$\pm{2.00}$
| T | 88.2$\pm{0.60}$ | 90.5$\pm{0.60}$ | 42.0$\pm{2.00}$
| R | 77.6$\pm{0.80}$ | 83.6$\pm{0.90}$ | 42.0$\pm{2.00}$
| Tr + T | 91.8$\pm{0.70}$ | 94.0$\pm{0.60}$ | 62.0$\pm{4.00}$
| Tr + T + R | 93.1$\pm{0.70}$ | 94.3$\pm{0.70}$ | 66.0$\pm{2.00}$
$1000\times 500$ | Tr | 96.3$\pm{0.40}$ | 98.3$\pm{0.30}$ | 71.0$\pm{2.00}$
| T | 95.4$\pm{0.40}$ | 96.7$\pm{0.50}$ | 71.0$\pm{1.00}$
| R | 91.5$\pm{0.40}$ | 95.3$\pm{0.30}$ | 52.0$\pm{1.00}$
| Tr + T | 99.0$\pm{0.30}$ | 99.3$\pm{0.30}$ | 85.0$\pm{2.00}$
| Tr + T + R | 99.3$\pm{0.20}$ | 99.5$\pm{0.20}$ | 91.0$\pm{2.00}$
$500\times 500$ | Tr | 98.7$\pm{0.20}$ | 99.4$\pm{0.10}$ | 75.0$\pm{2.00}$
| T | 97.5$\pm{0.10}$ | 98.9$\pm{0.10}$ | 69.0$\pm{1.00}$
| R | 92.9$\pm{0.30}$ | 96.2$\pm{0.10}$ | 54.0$\pm{1.00}$
| Tr + T | 99.8$\pm{0.00}$ | 99.9$\pm{0.00}$ | 86.0$\pm{2.00}$
| Tr + T + R | 99.8$\pm{0.10}$ | 99.9$\pm{0.10}$ | 95.0$\pm{1.00}$
$500\times 500$-OGRN | Tr | 92.6$\pm{0.60}$ | 94.8$\pm{0.40}$ | 38.0$\pm{2.00}$
| T | 93.5$\pm{0.40}$ | 96.4$\pm{0.30}$ | 34.0$\pm{1.00}$
| R | 83.7$\pm{0.50}$ | 88.7$\pm{0.60}$ | 27.0$\pm{1.00}$
| Tr + T | 97.9$\pm{0.20}$ | 98.7$\pm{0.20}$ | 48.0$\pm{2.00}$
| Tr + T + R | 98.2$\pm{0.20}$ | 98.7$\pm{0.20}$ | 62.0$\pm{3.00}$

An observation that can be made from our comparison is that both methods
proposed in this study consistently surpass the performance of the approach by
Rosa da Silva et al. [31], regardless of the dataset employed or the
combination of anatomical sections considered. Furthermore, among our proposed
methods, RADAM exhibits superior performance compared to GAP, demonstrating
its robustness across different datasets, anatomical sections and feature
fusion strategies.

###  3.4 Leave-k-trees-out

In our study, we also analyzed the performance of our method using the leave-
k-trees-out cross-validation strategy, as used by Rosa da Silva et al. [31].
This cross-validation approach involves segregating all samples from a single
tree for each species into the test set, thereby ensuring complete
independence between the training and test sets in terms of specimens.
Consequently, 165 samples were allocated for testing and 640 for training.

Table 7 presents a comparison among GAP, RADAM and the method proposed by Rosa
da Silva et al. [31]. All methods were evaluated under the leave-k-trees-out
cross-validation scheme. For our methods, we used all three anatomical
sections and the SFC strategy.

Table 7: Accuracy comparison of GAP, RADAM and the method of Rosa da Silva et al [31], analyzed with the leave-k-trees-out cross validation strategy. For our methods, we used all three anatomical sections and the SFC strategy. | Original | $1000\times 500$ | $500\times 500$ |  $500\times 500$-OGRN
---|---|---|---|---
Rosa da Silva et al. [31] | 30.00 | 28.00 | 27.00 | 22.00
GAP | 92.17 | 91.87 | 95.03 | 93.96
RADAM | 93.98 | 94.28 | 95.03 | 94.12

The results show a notable performance disparity. While the method of Rosa da
Silva et al. exhibited accuracies ranging from 22.00% to 30.00% across
different datasets, both GAP and RADAM demonstrated significantly higher
accuracies, with RADAM slightly outperforming GAP. These results suggest that
our method, although showing a slight inferior performance compared to the
traditional cross-validation configuration, still surpasses the performance of
Rosa da Silva et al.

In summary, the leave-k-trees-out cross-validation approach has demonstrated
the robustness of GAP and RADAM in the context of significant sample
variability when characterizing different trees/specimens.

##  4 Conclusions

In this study, we investigated the efficacy of two feature extraction
techniques (GAP and RADAM) used in conjunction with an SVM classifier for the
purpose of wood species identification through texture analysis on images from
three different anatomical sections. Our results indicate that SVM, when
employing either GAP or RADAM, surpasses the results of Rosa da Silva et al.
[31], demonstrating their effectiveness in the context of wood species
identification through texture analysis techniques. Our research also revealed
that combining multiple anatomical sections can significantly enhance the
performance.

Among the two methods proposed in this study, RADAM consistently demonstrated
superior performance across different datasets, anatomical section
combinations and feature fusion strategies. This highlights the robustness of
RADAM in handling diverse conditions and reinforces its potential as an
effective tool, not only for the recognition of macroscopic textures, but also
for wood species identification using microscopic images.

Moreover, our findings can potentially contribute to the automation of wood
species identification, reducing the dependence on expert knowledge and
enabling more efficient and scalable approaches. This is particularly relevant
in the context of timber supply chain monitoring and regulation, where the
ability to quickly and accurately identify wood species can facilitate
interventions and promote responsible practices.

However, despite the promising results, future research may improve the
characterization of different trees/specimens by further exploring the
integration of other factors such as image resolution or the use of other
neural network architectures such as Vision Transformers (ViTs).

In summary, the work proposed here represents a significant step forward in
the field of wood species identification. Through exploiting the computational
efficiency of pre-trained neural network models and advanced feature
extraction techniques such as RADAM, we provided a robust and efficient
approach to wood species identification, opening doors to more sustainable and
responsible forestry practices.

## Acknowledgements

K. M. Zielinski acknowledges support from the São Paulo Research Foundation
(FAPESP) (Grant #2022/03668-1) and Higher Education Personnel Improvement
Coordination (CAPES) (Grant #88887.631085/2021-00). L. Scabini acknowledges
funding from FAPESP (Grants #2021/09163-6 and #2023/10442-2). L. C. Ribas
acknowledges support from FAPESP (grants #2023/04583-2). O. M. Bruno
acknowledges support from CNPq (Grant #307897/2018-4) and FAPESP (grants
#2018/22214-6 and #2021/08325-2). The authors are also grateful to the NVIDIA
GPU Grant Program. B. De Baets received funding from the Flemish Government
under the ”Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen”
programme.

## References

  * [1] C. May, G. F. I. (Organization)., Transnational Crime and the Developing World, Global Financial Integrity, 2017.
  * [2] CITES, Convention on International Trade in Endangered Species of Wild Fauna and Flora.
URL https://cites.org/

  * [3] ITTO, Biennial Review and Assessment of the World Timber Situation (2020).
URL https://www.itto.int/

  * [4] E. Wheeler, P. Baas, P. Gasson, IAWA List of Microcopie Features for Hardwood Identification, IAWA journal / International Association of Wood Anatomists 10 (1989) 219–332.
  * [5] E. E. Dormontt, M. Boner, B. Braun, G. Breulmann, B. Degen, E. Espinoza, S. Gardner, P. Guillery, J. C. Hermanson, G. Koch, S. L. Lee, M. Kanashiro, A. Rimbawanto, D. Thomas, A. C. Wiedenhoeft, Y. Yin, J. Zahnen, A. J. Lowe, Forensic timber identification: It’s time to integrate disciplines to combat illegal logging, Biological Conservation 191 (2015) 790–798.
  * [6] P. Gasson, How precise can wood identification be? Wood anatomy’s role in support of the legal timber trade, especially cites, IAWA Journal 32 (2) (2011) 137–154.
  * [7] J. W. B. Braga, T. C. M. Pastore, V. T. R. Coradin, J. A. A. Camargos, A. R. da Silva, The use of near Infrared Spectroscopy to Identify solid wood Specimens of Swietenia Macrophylla0 (Cites Appendix II), IAWA Journal 32 (2) (2011) 285–296.
  * [8] S. Hassold, P. P. Lowry, M. R. Bauert, A. Razafintsalama, L. Ramamonjisoa, A. Widmer, DNA Barcoding of Malagasy Rosewoods: Towards a Molecular Identification of CITES-Listed Dalbergia Species, PLOS ONE 11 (6) (2016) e0157881.
  * [9] T. C. M. Pastore, J. W. B. Braga, V. T. R. Coradin, W. L. E. Magalhães, E. Y. A. Okino, J. A. A. Camargos, G. I. B. de Muñiz, O. A. Bressan, F. Davrieux, Near infrared spectroscopy (NIRS) as a potential tool for monitoring trade of similar woods: Discrimination of true mahogany, cedar, andiroba, and curupixá, Holzforschung 65 (1) (2011) 73–80.
  * [10] E. R. Price, I. A. Miles-Bunch, P. E. Gasson, C. A. Lancaster, Pterocarpus wood identification by independent and complementary analysis of DART-TOFMS, microscopic anatomy, and fluorescence spectrometry, IAWA Journal 42 (4) (2021) 397–418.
  * [11] L. Jiao, M. Yu, A. C. Wiedenhoeft, T. He, J. Li, B. Liu, X. Jiang, Y. Yin, DNA Barcode Authentication and Library Development for the Wood of Six Commercial Pterocarpus Species: the Critical Role of Xylarium Specimens, Scientific Reports 8 (1) (2018) 1945.
  * [12] S. Nithaniyal, S. G. Newmaster, S. Ragupathy, D. Krishnamoorthy, S. L. Vassou, M. Parani, DNA Barcode Authentication of Wood Samples of Threatened and Commercial Timber Trees within the Tropical Dry Evergreen Forest of India, PLoS ONE 9 (9) (2014) e107669.
  * [13] F. Hanssen, N. Wischnewski, U. Moreth, E. A. Magel, Molecular Identification of Fitzroya Cupressoides, Sequoia Sempervirens, and Thuja Plicata Wood Using Taxon-Specific RDNA-ITS Primers, IAWA Journal 32 (2) (2011) 273–284.
  * [14] R. Bremananth, B. Nithya, R. Saipriya, Wood Species Recognition System, World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering 3 (2009) 1138–1144.
  * [15] P. Zhao, G. Dou, G.-S. Chen, Wood species identification using feature-level fusion scheme, Optik 125 (3) (2014) 1144–1148.
  * [16] C. Guang-Sheng, Z. Peng, Wood cell recognition using geodesic active contour and principal component analysis, Optik 124 (10) (2013) 949–952.
  * [17] M. Khalid, R. Yusof, A. S. Mohd Khairuddin, Tropical wood species recognition system based on multi-feature extractors and classifiers, in: 2011 2nd International Conference on Instrumentation Control and Automation, IEEE, 2011, pp. 6–11.
  * [18] P. L. P. Filho, L. S. Oliveira, S. Nisgoski, A. S. Britto, Forest species recognition using macroscopic images, Machine Vision and Applications 25 (4) (2014) 1019–1031.
  * [19] H.-j. Wang, H.-n. Qi, X.-F. Wang, A new Gabor based approach for wood recognition, Neurocomputing 116 (2013) 192–200.
  * [20] J. G. Martins, L. S. Oliveira, R. Sabourin, Combining textural descriptors for forest species recognition, in: IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, IEEE, 2012, pp. 1483–1488.
  * [21] P. R. Cavalin, M. N. Kapp, J. Martins, L. E. S. Oliveira, A multiple feature vector framework for forest species recognition, in: Proceedings of the 28th Annual ACM Symposium on Applied Computing, ACM, New York, NY, USA, 2013, pp. 16–20.
  * [22] J. Martins, L. S. Oliveira, S. Nisgoski, R. Sabourin, A database for automatic classification of forest species, Machine Vision and Applications 24 (3) (2013) 567–578.
  * [23] P. Ravindran, A. Costa, R. Soares, A. C. Wiedenhoeft, Classification of CITES-listed and other neotropical Meliaceae wood images using convolutional neural networks, Plant Methods 14 (1) (2018) 25.
  * [24] P. Ravindran, B. J. Thompson, R. K. Soares, A. C. Wiedenhoeft, The XyloTron: Flexible, Open-Source, Image-Based Macroscopic Field Identification of Wood Products, Frontiers in Plant Science 11 (7 2020).
  * [25] F. Lens, C. Liang, Y. Guo, X. Tang, M. Jahanbanifard, F. S. C. da Silva, G. Ceccantini, F. J. Verbeek, Computer-assisted timber identification based on features extracted from microscopic wood sections, IAWA Journal 41 (4) (2020) 660–680.
  * [26] S. J. Pan, Q. Yang, A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering 22 (10) (2010) 1345–1359.
  * [27] N. Tajbakhsh, L. Jeyaseelan, Q. Li, J. N. Chiang, Z. Wu, X. Ding, Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation, Medical Image Analysis 63 (2020) 101693.
  * [28] R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, R. X. Gao, Deep learning and its applications to machine health monitoring, Mechanical Systems and Signal Processing 115 (2019) 213–237.
  * [29] Biovdiversity, COLLECTIONS OF THE ROYAL MUSEUM OF CENTRAL AFRICA.
URL https://www.biodiversity.be/

  * [30] N. Rosa da Silva, M. De Ridder, J. M. Baetens, J. Van den Bulcke, M. Rousseau, O. Martinez Bruno, H. Beeckman, J. Van Acker, B. De Baets, Automated classification of wood transverse cross-section micro-imagery from 77 commercial Central-African timber species, Annals of Forest Science 74 (2) (2017) 30.
  * [31] N. R. da Silva, V. Deklerck, J. M. Baetens, J. V. den Bulcke, M. D. Ridder, M. Rousseau, O. M. Bruno, H. Beeckman, J. V. Acker, B. D. Baets, J. Verwaeren, Improved wood species identification based on multi-view imagery of the three anatomical planes, Plant Methods 18 (1) (6 2022).
  * [32] V. Ojansivu, J. Heikkilä, Blur Insensitive Texture Classification Using Local Phase Quantization, 2008, pp. 236–243.
  * [33] L. Breiman, Random Forests, Machine Learning 45 (1) (2001) 5–32.
  * [34] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, ImageNet: A large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255.
  * [35] L. Scabini, K. M. Zielinski, L. C. Ribas, W. N. Gonçalves, B. De Baets, O. M. Bruno, RADAM: Texture recognition through randomized aggregated encoding of deep activation maps, Pattern Recognition 143 (2023) 109802.
  * [36] C. Cortes, V. Vapnik, Support-vector networks, Machine Learning 20 (3) (1995) 273–297.
  * [37] L. Kasun, H. Zhou, G.-B. Huang, C.-M. Vong, Representational Learning with ELMs for Big Data, IEEE Intelligent Systems 28 (2013) 31–34.
  * [38] E. Cambria, G.-B. Huang, L. L. C. Kasun, H. Zhou, C. M. Vong, J. Lin, J. Yin, Z. Cai, Q. Liu, K. Li, V. C. Leung, L. Feng, Y.-S. Ong, M.-H. Lim, A. Akusok, A. Lendasse, F. Corona, R. Nian, Y. Miche, P. Gastaldo, R. Zunino, S. Decherchi, X. Yang, K. Mao, B.-S. Oh, J. Jeon, K.-A. Toh, A. B. J. Teoh, J. Kim, H. Yu, Y. Chen, J. Liu, Extreme Learning Machines [Trends & Controversies], IEEE Intelligent Systems 28 (6) (2013) 30–59.  doi:10.1109/MIS.2013.140.
  * [39] D. E. Knuth, The Art of Computer Programming, Volume 2: Seminumerical Algorithms, 3rd Edition, Vol. 2, Addison-Wesley Longman Publishing Co., Inc., 1997.
  * [40] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, PyTorch: An Imperative Style, High-Performance Deep Learning Library, in: Advances in Neural Information Processing Systems 32 (NeurIPS), 2019, pp. 8024–8035.
URL http://papers.neurips.cc

  * [41] R. Wightman, PyTorch Image Models (timm), https://github.com/rwightman/pytorch-image-models (2019).
  * [42] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research 12 (2011) 2825–2830.
URL http://www.jmlr.org

  * [43] K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2016, pp. 770–778.
  * [44] Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, S. Xie, A convnet for the 2020s, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11976–11986.