# Early Detection of Red Palm Weevil Infestations using Deep Learning
Classification of Acoustic Signals

Wadii Boulila<EMAIL_ADDRESS>Ayyub Alzahem<EMAIL_ADDRESS>Anis
Koubaa<EMAIL_ADDRESS>Bilel Benjdira<EMAIL_ADDRESS>Adel Ammar
<EMAIL_ADDRESS>Robotics and Internet-of-Things Laboratory, Prince Sultan
University, Riyadh 12435, Saudi Arabia  RIADI Laboratory, National School of
Computer Sciences, University of Manouba, Manouba, Manouba 2010, Tunisia  SE &
ICT Lab, LR18ES44, ENICarthage, University of Carthage, Tunis 1054, Tunisia

###### Abstract

The Red Palm Weevil (RPW), also known as the palm weevil, is considered among
the world’s most damaging insect pests of palms. Current detection techniques
include the detection of symptoms of RPW using visual or sound inspection and
chemical detection of volatile signatures generated by infested palm trees.
However, efficient detection of RPW diseases at an early stage is considered
one of the most challenging issues for cultivating date palms. In this paper,
an efficient approach to the early detection of RPW is proposed. The proposed
approach is based on RPW sound activities being recorded and analyzed. The
first step involves the conversion of sound data into images based on a
selected set of features. The second step involves the combination of images
from the same sound file but computed by different features into a single
image. The third step involves the application of different Deep Learning (DL)
techniques to classify resulting images into two classes: infested and not
infested. Experimental results show good performances of the proposed approach
for RPW detection using different DL techniques, namely MobileNetV2,
ResNet50V2, ResNet152V2, VGG16, VGG19, DenseNet121, DenseNet201, Xception, and
InceptionV3. The proposed approach outperformed existing techniques for public
datasets.

###### keywords:

Red Palm Weevil , deep learning , disease detection , sound data , smart
agriculture

##  1 Introduction

The red palm weevil (RPW), _Rhynchophorus ferrugineus_ , is a significant pest
of palm trees, causing considerable damage to the tree and resulting in
substantial financial losses for farmers. RPW is considered among the most
destructive pests that threaten palms worldwide. Saudi Arabia provides about
$2 million contribution to support the global efforts to eradicate RPW [30].
However, several limitations still need to be addressed, especially on the
farm side. In most cases, farm owners did not have any automated process
providing clear indicators about infested palm trees, which causes from 25 to
40% loss of harvest due to unregulated processes [13].

The management of the RPW has presented a significant challenge for
entomologists since the beginning of modern agriculture, especially in the
early detection of infested palms. Visual inspection of palms is the most
adopted technique to locate infested palms. To detect palm diseases, farm
owners would need to pay a significant amount of money to hire a palm tree
infection expert for a whole year. Depending on the terrain and density, an
expert can inspect around 200 palm trees daily. In many cases, there is up to
40% loss of crop yield due to the inexperience of the farmworkers or
unregulated processes. In addition, the problem of RPW detection becomes more
difficult when it comes to early detection [28, 22]. Indeed, RPW is a hidden
pest for most of its life stages, except for the adult stage. Hence, it is
imperative to ensure proper measures for early RPW detection. RPW causes
various morphological changes to the palm tree, such as the formation of holes
in the trunk and leaves, wilting, and discoloration. Infested palm trees often
have a foul smell, and their fronds turn yellow and droop. In the later stages
of infestation, the crown of the palm tree collapses, leading to the death of
the entire tree. However, these morphological changes are challenging to
detect in the early stages of infestation. Therefore, using an alternative
technique, such as sound data, becomes crucial in detecting the presence of
the RPW. Using sound data provides a non-invasive and efficient method for
early detection of RPW infestation in palm trees, making it a valuable tool
for farm owners and professionals in the agricultural industry.

For these reasons, there is a pressing need to use advanced information
technologies in date farming. Deep learning (DL) has recently shown great
promise in various applications, from natural language processing and computer
vision to healthcare and finance [36, 6, 8]. DL models can learn complex
patterns and relationships in data and make predictions or decisions based on
that learned knowledge [2, 7]. These models can learn from large amounts of
data and can improve their performance [17]. Due to these capabilities, DL is
being used to solve many problems, including detecting the red palm weevil
from sound data.

In this study, a novel approach for early disease detection is proposed. The
proposed approach is a DL-based method for detecting infestation in palm trees
using sound data. The process starts by recording sound data from palm trees
and converting it into images by computing a set of selected features. The
images are combined and classified using DL techniques to identify an
infestation.

The main contributions of the proposed approach are summarized as follows:

  * 1.

We proposed to combine three types of audio feature extraction techniques:
Constant Q Cepstral Coefficients (CQCC) [43], Mel-frequency Cepstral
Coefficients (MFCC) [1], and Bark Frequency Cepstral Coefficients (BFCC) [16].
These features are combined to improve the classification accuracy of RPW
infestation.

  * 2.

We proposed an efficient method to detect early signs of RPW infestation,
providing early intervention from farm owners and professionals.

  * 3.

Several experiments are conducted using three public datasets: TreeVibe [37],
Environmental Sound Classification (ESC) [27]. Results show that the proposed
approach provides excellent performances compared to existing related works.

The rest of the paper is organized as follows. Section 2 reviews relevant
literature on the detection of palm tree infestation, highlighting key
findings and gaps in the existing research. Section 3 details the study area,
dataset description, and data collection. Section 4 describes the research
design and analysis procedures used in the study. Results are presented in
Section 5. In this section, the findings of the proposed study, including a
comparison with existing techniques for public datasets. Section 6 interprets
the results of the study in the context of the existing literature and
provides insights into the implications of the findings. Finally, the main
findings of the proposed study, their implications, and recommendations for
future research are summarized in Section 7.

##  2 Related works

Recently, the application of new and modern agricultural techniques has proved
its efficiency in many agriculture-related fields [38, 10, 47, 42]. In the
context of our study, we are interested in reviewing the use of modern
technologies in the case of early disease detection, which is among the top
priorities of farm stakeholders since it will ensure healthy farms and high-
quality crops. In the literature, several works aim to use modern technologies
to ensure the early detection of plant infestation [23, 19, 31, 48, 50].
However, this research area still needs more investigation, especially for
palm trees [15, 18, 35]. In this paper, we have reviewed research works
closely aligned with our research endeavors, which is the early detection of
RPW infestation using acoustic signals. Koubaa et al. in [29] proposed an IoT-
based framework for red palm weevil early detection. The proposed framework
allows the detection of red palm weevils by using smart agriculture sensors
and collecting data using accelerometer sensors. Signal processing and
probability methods are developed to identify the activity of the red palm
weevil. Pinhas et al. [34] developed a mathematical method to automatically
detect the acoustic signals of RPW larvae in palm offshoots using off-the-
shelf recording devices. They utilized techniques similar to those used in
speech recognition, such as vector quantization and Gaussian mixture modeling,
to achieve detection ratios of 98.9%. This method could be used to monitor the
trade and transportation of offshoots, helping to prevent the spread of this
pest. In this paper [33], a Convolutional Autoencoder based Deep Learning
model is proposed for identifying Red Palm Weevil (RPW) signals from
background noise. The model uses Mel spectrogram images of RPW acoustic
activities as the training data, achieving a classification accuracy of 95.85%
on the test dataset composed of normal RPW acoustic emissions as well as
anomalous acoustic samples. The proposed method is found to be highly
efficient for the identification of RPW signals. Mohamed et al. [32] proposed
a method based on recording RPW sounds during different activity states,
extracting important features in the sounds using the MFCC technique, and
using artificial neural networks to automatically detect if RPW larvae exists.
The recorded sounds were transformed into a spectrogram and analyzed with a
CNN model, achieving 99.2% accuracy using AlexNet [4]. This AI-based approach
could help to detect RPW larvae early, potentially preventing the spread of
this pest. In [46], the authors introduced the combination of machine learning
and fiber optic distributed acoustic sensing (DAS) techniques as a solution
for the early detection of RPW in vast farms. They trained a fully-connected
artificial neural network and a convolutional neural network using
experimental time- and frequency-domain data provided by the fiber optic DAS
system. The models can efficiently recognize healthy and infested trees with
high classification accuracy values (99.9% by ANN with temporal data and 99.7%
by CNN with spectral data, in reasonable noise conditions). This work paves
the way for deploying the high-efficiency and cost-effective fiber optic DAS
to monitor RPW in open-air and large-scale farms containing thousands of
trees. In [5], the authors integrated optical fiber DAS and machine learning
(ML) for the early detection of true weevil larvae less than three weeks old.
They recorded temporal and spectral data using the DAS system and processed it
with a 100–800 Hz filter to train CNN models that distinguish between
”infested” and ”healthy” signals with a classification accuracy of 97%. They
also introduced a strict ML-based classification approach to improving the
false alarm performance metric of the system. This approach could help to
detect RPW early, preventing the spread of this harmful pest. In [14], the
authors proposed an IoT-based system for early detection of RPW larvae in date
palms using a modified mixed depthwise convolutional network (MixConvNet) as a
deep learning classifier. The proposed system was tested on the public
TreeVibes dataset and achieved an accuracy score of 97.38%. The proposed
system is considered a future milestone for practical implementation in the
field.

Table 2 summarizes the discussed related works. As shown in the related works,
most authors rely on spectral data in their research. In this paper, we
present an innovative way to detect RPW based on the combination of features
extracted from the collected sound data.

Table 1: Comparison of recent research works related to RPW detection using
audio signals .

Ref. | Year | Algorithm | Data Type | Steps | Dataset | Accuracy
---|---|---|---|---|---|---
[34] |  2008 |  GMM  VQ |  Spectral |  $\bullet$ Record the RPW using off the helf device
$\bullet$ Manual data labeling
$\bullet$ Calculate noise threshold
$\bullet$ Counting the mismatched labels |  - |  0.988  0.989
[33] |  2021 |  CAE |  Mel Spectrograms |  $\bullet$ Capture audio signals
$\bullet$ Extract features using Mel Spectrograms algorithm
$\bullet$ Convert the features to images
$\bullet$ Train CAE on the images |  - |  0.958
[32] |  2021 |  AlexNet |  MFCC |  $\bullet$ Extract features using MFCC algorithm
$\bullet$ Convert the features to images
$\bullet$ Train AlexNet on the images |  - |  0.992
[46] |  2021 |  ANN  CNN |  Temporal  Spectral |  $\bullet$ Install fiber optic DAS system on palm trees
$\bullet$ Capture audio signals
$\bullet$ Train ANN and CNN with DAS data
$\bullet$ Deploy DAS system in large farms |  - |  0.999  0.997
[5] |  2022 |  CNN |  Temporal  Spectral |  $\bullet$ Record the sound of RPW using DAS
$\bullet$ Transform it into temporal and spectral data
$\bullet$ Train CNN on the data separately
$\bullet$ Merge CNN models |  - |  0.970  0.971
[14] |  2022 |  CNN |  Spectral |  $\bullet$ Extract spectral features
$\bullet$ Convert the features to images
$\bullet$ Train MixConvNet on the images
$\bullet$ Compare performance with other models |  TreeVibes |  0.973

##  3 Dataset collection and description

Saudi Arabia is the second-largest palm date producer. Al-Ahssa region in
Saudi Arabia was taken as a case study to test the performance of the proposed
approach. The dataset used in this study originally is a set of recordings.
The recordings were taken from date palm trees using remote vibroacoustic
surveillance of trees. The device111http://www.insectronics.net/. used in this
study is placed on the palm tree’s trunk and records the vibrations created by
the weevils as they chew and move inside the tree.

The reading points were marked on each date palm trunk (one meter above the
ground level). Then drill holes (30 degrees) were made using a drill machine
equipped with a brad point drill-bit (diameter, 8 mm) for a 35 cm depth inside
the palm tree. A single hole was made on the site of the trunk, and an
acoustic device covered the whole tree for RPW sound detection. For the
reading, the acoustic device has a probe (waveguide) (35 cm in length) that is
inserted inside the tree embedded with a recording machine. The waveguide was
inserted into the hole of the date palm trunk that was previously drilled. The
machine recording is connected to headphones that enable the user to assess
the sound signal produced by RPW activities. This device provides the data
which enables the movement from the device and the server/cloud system. The
RPW device can detect the sound signal inside the date palm tree within a
sphere of 1.5-2 meters radius [37]. In this experiment, the parameter was set
up to five minutes of repeat period and 20 seconds of recording times. Twenty
seconds of sound data are contained in each tree recording daily. This record
protocol was done in all date palm trees. Time stamps of each recording were
also provided.

In order to better understand the dataset, Table 2 shows the number of
recordings per class.

Table 2: Data description. Class | Number of samples
---|---
Infested | 531
Not Infested | 575

After collecting the data, several features are computed based on the sound
dataset. Then, these features are combined to better describe the content of
the data. The process of feature extraction and collaboration is described in
the following section.

##  4 Proposed Approach

The goal of this study is to detect RPW infestation from sound data acquired
from palm trees. Figure 1 describes the architecture of the proposed approach.
The process starts by converting sound data into images by computing a set of
selected features. The second step will combine images of the same sound file
(computed by different features) into a single image. The third step involves
applying different DL techniques to classify resulting images into two
classes: infested and not infested.

Figure 1: Architecture of the proposed approach.

###  4.1 Feature extraction from sound Weevil dataset

We used CQCC, MFCC, and BFCC feature extractions to convert one sound file
into three images, one for each feature.

To train DL models, extracting useful features from recorded sound data is
required. In literature, several features have been investigated [11]. A
waveform can be converted to a spectrogram, which is a visual representation
of all frequencies over time. Many features can be computed on sound data. In
this study, we will consider MFCC, CQCC, and BFCC.

####  4.1.1 Mel-Frequency Cepstral Coefficients (MFCC)

The cepstrum of a signal contains information about the rate of change in its
spectral bands. A cepstrum is just a spectrum of the log of the temporal
signal’s spectrum. MFCCs are coefficients that make up the mel-frequency
cepstrum [49]. The cepstrum communicates values that constitute a sound’s
formants (a distinguishing component of its quality) and timbre. As a result,
MFCCs are very useful in DL models. Figure 2 depicts the block diagram of MFCC
[26].

Pre-emphasis Farming Windowing DFT Mel filter bank DST Delta energy and
spectrum
$InputVoice$$Magnitude\;spectrum$$Mel\;spectrum$$Mel\;spectrum$$Output$ Figure
2: MFCC Block Diagram.

####  4.1.2 Constant Q Cepstral Coefficients (CQCC)

Combining the constant Q transform (CQT) and conventional cepstral processing
results in the CQCC. CQT’s design is appropriate for musical representation
and transforms a data series to the frequency domain [9]. It is a time-
frequency analysis that perceptually motivated more than The short-term
Fourier transform (STFT) [3]. The ratio of the center frequency $f_{k}$ to the
bandwidth $\delta f$ is known as the $Q$ factor, which measures the
selectivity of each filter.

$Q=\frac{f_{k}}{\delta f}$ (1)

The window function of the STFT is coupled to the constant bandwidth of each
filter. Since all filters have the same absolute bandwidth $f$, the $Q$ factor
increases as one moves from low to high frequencies. Contrarily, the Q factor
of the human sensory system is known to remain about constant between 500Hz
and 20kHz (Moore, 2003). So, from a perceptual perspective, the STFT might not
always be the optimum choice for the time-frequency analysis of speech
signals. The transform can be represented as a succession of filters $f_{k}$,
each one $k-th$ having a spectral width $\delta f_{k}$ that is a multiple of
the width of the filter before it:

$\delta f_{k}=2^{1/n}\cdot\delta f_{k-1}=(2^{1/n})^{k}=\delta f_{min}$ (2)

Where $n$ is the number of filters per octave, $f_{min}$ is the center
frequency of the lowest filter, and $deltaf_{k}$ is the bandwidth of the
$k-th$ filter. For CQT computation, the discrete time domain signal $x(n)$ has
the following CQT $X^{CQ}(k,n)$ definition:

$X^{CQ}(k,n)=\sum_{j=n-\lfloor N_{k}/2\rfloor}^{n+\lfloor
N_{k}/2\rfloor}x(j)a^{*}_{k}(j-n+\frac{N_{k}}{2})$ (3)

Where a $a^{*}_{k}(n)$ is the complex number of the basis functions
$a_{k}(n)$, K is the frequency bin index, $k$ = 1, 2,…,$K$ and $N_{k}$ are
variable window lengths. The notation $\lfloor\cdot\rfloor$ denotes the floor
function. The $a_{k}(n)$ functions are diverse time-frequency elements,
defined as:

$a_{k}(n)=\frac{1}{C}(\frac{n}{N_{k}})\>exp[i(2\pi
n\frac{f_{k}}{f_{s}}+\phi_{k})]$ (4)

Where $k$ is a phase offset, $f_{k}$ is the bin $k$’s center frequency, and
$f_{s}$ is the sampling rate. The scaling factor $C$ can be found by using:

$C=\sum_{l=-\lfloor N_{k}/2\rfloor}^{\lfloor
N_{k}/2\rfloor}w\Biggl{(}\frac{l+N_{k}/2}{N_{k}}\Biggl{)}$ (5)

Where $w(x)$ represents a window function. The equation 6 then allows for the
more-or-less traditional extraction of CQCC.

$CQCC(p)=\sum_{l=1}^{L}log|X^{CQ}(l)|^{2}\>cos\bigg{[}\frac{p(l-\frac{1}{2})\pi}{L}\bigg{]}$
(6)

Where $l$ are the recently resampled frequency bins and $p$ = 0, 1,…, $L$ \-
1.

Constant-Q Transform Power spectrum LOG Uniform resampling DCT
$x(n)$$X^{CQ}(k)$$|X^{CQ}(k)|^{2}$$log|X^{CQ}(k)|^{2}$$log|X^{CQ}(l)|^{2}$$CQCC(p)$
Figure 3: CQCC Block Diagram.

####  4.1.3 Bark Frequency Cepstral Coefficients (BFCC)

BFCC is a feature extraction technique used in speech and audio processing. It
is based on the Bark scale, which is a non-linear frequency scale that
approximates the human auditory system’s response to different frequency
bands. The Bark scale is defined as:

$z=26.81*(f/1000)-0.53*(f/1000)^{2}+(4.5*10^{-}6)*(f/1000)^{3}$ (7)

Where $f$ is the frequency in Hz, the BFCCs are calculated by first converting
the audio signal into the Bark scale, applying the above formula on the
frequency axis of the spectrogram of the audio signal, then taking the
cepstral coefficients of the resulting Bark-scale spectrogram. The cepstral
coefficients are obtained by applying a Discrete Cosine Transform (DCT) on the
log-magnitude of the Bark-scale spectrogram. This process is known as Cepstral
Analysis. The result is a set of coefficients that represents the audio signal
in the Bark frequency scale. These coefficients are often used as input to
machine learning models for tasks such as speech recognition and music
classification.

###  4.2 Feature combination

DL techniques have the ability to learn representations of data using multiple
abstraction levels. The feature combination aims to take advantage of multiple
features to construct a robust discriminator of red weevil sound. Each
computed feature determines a specific aspect of the audio signal’s temporal
or spectral content. Combining these features will help the DL techniques
discriminate between infested and not infested palms. Using the early
mentioned features, DL models can be trained separately using only one feature
at a time or combined features.

Algorithm 1 describes the detailed process of combining the extracted features
using our selected feature extractions CQCC, MFCC, and BFCC, where lines 2 to
4 specify the static variables such as data paths and feature extractions.
Lines 6 and 7 are for audio file loading based on the current file name on the
loop at line 5. After loading our audio file, in lines 10 to 12, we extract
the audio features and convert it to an image without any axis. In line 13, we
append the generated image in a temporary list that clears itself each time
after loading an audio file. After extracting the audio feature and appending
the images to our temporary list, lines 15 to 17 combine the generated images
and save them as one image with the same name as the audio file. This process
will be repeated on all the audio files in the target folder.

Algorithm 1 : Combine multiple feature extraction methods

1:Begin

2:$audioFolderPath\leftarrow/home/data/audio$

3:$imagesFolderPath\leftarrow/home/data/images$

4:$exteactionMethods\leftarrow[cqcc,mfcc,bfcc]$ $\triangleright$ specify the
extraction methods

5:for each $fileName\in audioFolderPath$ do $\triangleright$ loop over all
files in the audio folder

6: $audioFilePath\leftarrow joinPath(audioFolderPath,fileName)$

7: $audio=loadAudio(audioFilePath)$

8: $tempImagesList\leftarrow\varnothing$

9: for each $method\in exteactionMethods$ do

10: $features\leftarrow method.extract(audio)$ $\triangleright$ use the
current algorithm for the extraction

11: $figure=createFigure()$ $\triangleright$ create empty figure

12: $figure.display(features)$ $\triangleright$ display the extracted features
on the figure

13: $tempImagesList.append(figure)$ $\triangleright$ save the features image
temporarily

14: end for

15: $combinedImage\leftarrow concat(tempImagesList)$ $\triangleright$
concatenate features images

16: $imageFilePath\leftarrow joinPath(imagesFolderPath,fileName)$

17: $save(imageFilePath,combinedImage)$

18:end for

19:End

###  4.3 DL classification of sound Weevil

We applied several DL techniques, namely MobileNetV2, ResNet50V2, ResNet152V2,
VGG16, VGG19, DenseNet121, DenseNet201, Xception, and InceptionV3 on the
generated Red Palm Weevil dataset. We used TensorFlow and Keras frameworks to
train the listed TL models on 200 epochs, 0.0001 learning rate, and RMSprop
optimizer. The dataset has been split into three sets, training set (0.8),
validation set (0.1), and test set (0.1), to ensure preventing any results
coming from overfitting.

These techniques are used to extract features from an audio dataset,
concatenate the CQCC, MFCC, and BFCC images of each audio file together, and
train models to classify the audio samples. CQCC, MFCC, and BFCC are feature
extraction techniques commonly used in audio processing to extract different
audio characteristics. By combining these features in the form of images and
training CNNs on them, the models can learn and identify complex patterns in
the audio.

These specific CNNs are chosen based on their performance and efficiency in
the image and audio classification tasks. MobileNetV2 and Xception are
lightweight models suitable for mobile and embedded vision applications, while
ResNet50V2 and ResNet152V2 are variants of the ResNet architecture that
improve accuracy by using residual connections and bottleneck layers. VGG16,
VGG19, DenseNet121, DenseNet201, and InceptionV3 are also widely used CNNs
that have been proven to be effective in various image and audio
classification tasks. RMSprop optimizer was used for all training experiments
as it is particularly well-suited for training deep neural networks with a
large number of parameters.

Using a variety of CNNs in this study demonstrates the versatility and
applicability of the proposed approach in various audio classification tasks
and with different types of CNN architectures. The combination of CQCC, MFCC,
and BFCC features with various CNNs allows the extraction of different audio
characteristics and enhances the models’ performance.

##  5 Results

###  5.1 Feature extraction from the sound Weevil dataset

Feature extraction is a process in which characteristics or features of an
audio signal are extracted and represented in a more manageable and
informative way. It is crucial in many audio processing tasks, such as speech
recognition, music classification, and sound event detection. The extracted
features serve as input to machine learning models, which use them to make
predictions or decisions. There are many different feature extraction
techniques that can be used, each with its own strengths and weaknesses. The
choice of feature extraction method depends on the specific task and the
characteristics of the audio signal. Figure 4 illustrates the use of three
different feature extraction techniques, namely CQCC, MFCC, and BFCC, on a
dataset of audio files labeled as ”infested” or ”not infested.” The figure
contains four samples from each class, and the output of the three feature
extraction techniques is displayed for each sample.

Figure 4: The resulting images for each class.

###  5.2 Red Palm Weevil detection

In this section, we present the validation accuracy results of transfer
learning techniques with nine backbone models, namely MobileNetV2[39],
ResNet50V2[20], ResNet152V2[21], VGG16[40], VGG19[41], DenseNet121[24],
DenseNet201[25], Xception[12], and InceptionV3[44], for each feature extracted
from the sound Weevil dataset as well as the combination of these features.
The primary purpose of applying different DL techniques is to check the
performance of the combination of selected sound features in detecting the RPW
infestation.

Table 3 provides a comparison of the performance of nine different machine
learning models: MobileNetV2, ResNet50V2, ResNet152V2, VGG16, VGG19,
DenseNet121, DenseNet201, Xception, and InceptionV3. These models are compared
based on their top-1 accuracy (Accuracy@1), top-5 accuracy (Accuracy@5), the
number of parameters (in millions), and their computational complexity in
GFLOPS (Giga Floating Point Operations Per Second). The accuracies are
evaluated on the ImageNet dataset and represent the model’s ability to
correctly identify the true class out of its top 1 and top 5 predictions,
respectively. The number of parameters in the model represents the model’s
size and capacity, and GFLOPS is a measure of the computational complexity of
the model, with higher values indicating more computation is required. The
exact values for each model will need to be filled in according to the
specific experiment results.

Model | Accuracy@1 | Accuracy@5 | Parameters (in millions) | GFLOPS
---|---|---|---|---
MobileNetV2 [39] | 72.154 | 90.822 | 3.5M | 0.3
ResNet50V2 [20] | 80.858 | 95.434 | 25.6M | 4.09
ResNet152V2 [21] | 82.284 | 96.002 | 60.2M | 11.51
VGG16 [40] | 71.592 | 90.382 | 138.4M | 15.47
VGG19 [41] | 72.376 | 90.876 | 143.7M | 19.63
DenseNet121 [24] | 74.434 | 91.972 | 8.0M | 2.83
DenseNet201 [25] | 76.896 | 93.37 | 20.0M | 4.29
Xception [12] | 79.0 | 94.5 | 22.9M | -
InceptionV3 [44] | 77.294 | 93.45 | 27.2M | 5.71
Table 3: Comparative analysis of MobileNetV2, ResNet50V2, ResNet152V2, VGG16,
VGG19, DenseNet121, DenseNet201, Xception, and InceptionV3 models on top-1
accuracy, top-5 accuracy, number of parameters, and GFLOPS using the ImageNet
dataset.

Table 4 shows the accuracy and loss values for the classification experiments
using the mentioned models. We used single-feature extraction algorithms to
compare the classification performances of the considered DL models. The
features depicted in the table are LFCC, CQCC, MFCC, Chroma, GFCC, BFCC, Mel
Spectro, and Spectral Centroid. The highest accuracy was achieved by the
models trained on the CQCC feature set, with a maximum accuracy of 1.000 using
the DenseNet121 model. Achieving 100% accuracy using only one feature, CQCC,
is not sufficient because we aim to achieve the best accuracy regardless of
the CNN architecture used. Therefore, we used multiple feature combinations to
ensure high accuracy without relying on any specific CNN architecture. On the
other hand, the lowest accuracy was achieved by the models trained on the MFCC
feature, with a minimum accuracy of 52% using the ResNet152V2 model.

In Table 5, several experiments have been conducted with multiple feature
combinations, including CQCC-MFCC-BFCC, MFCC-Chroma-Mel Spectro, MFCC-Chroma-
LFCC, GFCC-BFCC-CQCC, GFCC-CQCC-LFCC, and Spectro-MFCC-BFCC. The accuracy
values in the table ranged from 70% to 100%, indicating that the models
performed very well on the test data. The loss values were all relatively low,
ranging from 0.000 to 9.836. A lower loss value indicates that the model is
better at predicting the target variable. It is interesting to note that all
models trained on the CQCC-MFCC-BFCC feature set achieved perfect accuracy,
while some of the models trained on other feature sets achieved lower accuracy
values. The ResNet152V2 and DenseNet201 models performed very well across all
feature sets. Table 5 highlights that the choice of a specific audio feature
set has a significant impact on the accuracy of the models. It also suggests
that the choice of model architecture is important for achieving good accuracy
in the audio classification task.

Table 4: Single feature experimental results. Features | Model | Accuracy | Loss | Features | Model | Accuracy | Loss
---|---|---|---|---|---|---|---
LFCC | MobileNetV2 | 0.843 | 9.715 | GFCC | MobileNetV2 | 0.848 | 7.609
ResNet50V2 | 0.776 | 1.873 | ResNet50V2 | 0.786 | 5.045
ResNet152V2 | 0.734 | 2.385 | ResNet152V2 | 0.864 | 1.679
VGG16 | 0.765 | 1.288 | VGG16 | 0.843 | 0.555
VGG19 | 0.734 | 0.498 | VGG19 | 0.828 | 0.496
DenseNet121 | 0.739 | 3.185 | DenseNet121 | 0.838 | 2.664
DenseNet201 | 0.651 | 2.771 | DenseNet201 | 0.817 | 2.318
Xception | 0.760 | 8.531 | Xception | 0.807 | 7.971
InceptionV3 | 0.765 | 3.113 | InceptionV3 | 0.833 | 4.667
CQCC | MobileNetV2 | 0.994 | 0.008 | BFCC | MobileNetV2 | 0.791 | 7.068
ResNet50V2 | 0.979 | 0.109 | ResNet50V2 | 0.796 | 4.732
ResNet152V2 | 0.979 | 0.161 | ResNet152V2 | 0.723 | 7.052
VGG16 | 0.994 | 0.017 | VGG16 | 0.786 | 0.470
VGG19 | 0.989 | 0.019 | VGG19 | 0.770 | 0.499
DenseNet121 | 1.000 | 0.000 | DenseNet121 | 0.812 | 2.268
DenseNet201 | 0.984 | 0.035 | DenseNet201 | 0.718 | 3.300
Xception | 1.000 | 0.000 | Xception | 0.770 | 9.317
InceptionV3 | 0.994 | 0.012 | InceptionV3 | 0.791 | 2.716
MFCC | MobileNetV2 | 0.817 | 7.428 |  Mel Spectro | MobileNetV2 | 0.838 | 9.244
ResNet50V2 | 0.848 | 1.549 | ResNet50V2 | 0.833 | 1.923
ResNet152V2 | 0.557 | 14.10 | ResNet152V2 | 0.843 | 3.693
VGG16 | 0.791 | 0.498 | VGG16 | 0.765 | 0.396
VGG19 | 0.520 | 0.692 | VGG19 | 0.875 | 0.315
DenseNet121 | 0.869 | 1.374 | DenseNet121 | 0.791 | 3.158
DenseNet201 | 0.807 | 4.450 | DenseNet201 | 0.859 | 2.950
Xception | 0.864 | 3.286 | Xception | 0.828 | 4.113
InceptionV3 | 0.880 | 1.264 | InceptionV3 | 0.802 | 1.753
Chroma | MobileNetV2 | 0.781 | 7.462 |  Spectral Centroid | MobileNetV2 | 0.854 | 8.867
ResNet50V2 | 0.750 | 2.337 | ResNet50V2 | 0.854 | 2.005
ResNet152V2 | 0.781 | 3.758 | ResNet152V2 | 0.822 | 1.076
VGG16 | 0.776 | 0.777 | VGG16 | 0.859 | 0.273
VGG19 | 0.677 | 0.620 | VGG19 | 0.817 | 0.426
DenseNet121 | 0.734 | 3.294 | DenseNet121 | 0.869 | 1.361
DenseNet201 | 0.791 | 5.286 | DenseNet201 | 0.869 | 1.479
Xception | 0.817 | 4.872 | Xception | 0.875 | 2.733
InceptionV3 | 0.697 | 3.928 | InceptionV3 | 0.859 | 3.972
Table 5: Combined features experimental results. Features | Model | Accuracy | Loss | Features | Model | Accuracy | Loss
---|---|---|---|---|---|---|---
CQCC MFCC BFCC | MobileNetV2 | 1.000 | 0.000 |  GFCC BFCC CQCC | MobileNetV2 | 0.890 | 9.393
ResNet50V2 | 1.000 | 0.000 | ResNet50V2 | 0.854 | 3.961
ResNet152V2 | 1.000 | 0.000 | ResNet152V2 | 0.885 | 9.836
VGG16 | 1.000 | 0.000 | VGG16 | 0.854 | 0.355
VGG19 | 1.000 | 0.000 | VGG19 | 0.760 | 0.481
DenseNet121 | 1.000 | 0.000 | DenseNet121 | 0.828 | 1.048
DenseNet201 | 1.000 | 0.000 | DenseNet201 | 0.859 | 1.594
Xception | 1.000 | 0.000 | Xception | 0.848 | 9.100
InceptionV3 | 1.000 | 0.000 | InceptionV3 | 0.781 | 4.304
MFCC Chroma Mel Spectro | MobileNetV2 | 0.875 | 6.764 |  GFCC CQCC LFCC | MobileNetV2 | 0.979 | 0.487
ResNet50V2 | 0.786 | 9.704 | ResNet50V2 | 0.838 | 3.509
ResNet152V2 | 0.875 | 2.720 | ResNet152V2 | 0.979 | 0.141
VGG16 | 0.833 | 0.388 | VGG16 | 0.786 | 3.453
VGG19 | 0.817 | 0.548 | VGG19 | 0.708 | 0.475
DenseNet121 | 0.838 | 1.894 | DenseNet121 | 0.963 | 0.294
DenseNet201 | 0.833 | 1.945 | DenseNet201 | 0.989 | 0.229
Xception | 0.890 | 2.618 | Xception | 0.864 | 5.917
InceptionV3 | 0.796 | 5.233 | InceptionV3 | 0.828 | 2.079
MFCC Chroma LFCC | MobileNetV2 | 0.796 | 0.764 |  Mel Spectro MFCC BFCC | MobileNetV2 | 0.864 | 6.603
ResNet50V2 | 0.807 | 3.938 | ResNet50V2 | 0.833 | 2.246
ResNet152V2 | 0.807 | 1.653 | ResNet152V2 | 0.869 | 1.940
VGG16 | 0.708 | 0.541 | VGG16 | 0.828 | 0.461
VGG19 | 0.817 | 1.222 | VGG19 | 0.765 | 0.554
DenseNet121 | 0.812 | 1.969 | DenseNet121 | 0.848 | 2.250
DenseNet201 | 0.807 | 4.927 | DenseNet201 | 0.822 | 2.373
Xception | 0.796 | 4.091 | Xception | 0.838 | 5.584
InceptionV3 | 0.833 | 3.257 | InceptionV3 | 0.807 | 1.684

Figure 5 shows the validation accuracy and loss progress of all the used DL
models during the training; each line consists of 200 points, one for each
epoch.

Figure 5: Validation accuracy and loss progress of CQCC-MFCC-BFCC on RPW
dataset.

Figure 6 presents a clearer version of Figure 5, showing how fast the models
are learning during the first 10 epochs.

Figure 6: Validation accuracy and loss progress of CQCC-MFCC-BFCC on RPW
dataset for the first 10 epochs.

Based on figures 5 and 6, it appears that all of the models were able to reach
a validation accuracy of 100% within the first 10 epochs when using the merged
features. The full distribution plot provides a more comprehensive view of the
models’ performance throughout the training. In contrast, the plot of the
first 10 epochs provides a more focused view of the models’ initial
performance. This confirms that all models were able to effectively learn and
make accurate predictions on the validation set within a relatively short
period of time. One possible explanation for this is that the models were pre-
trained on a large dataset, which allowed them to effectively learn the
features of the validation set in a relatively short time. Additionally, the
high accuracy achieved within the first 10 epochs also suggests that the
models were able to effectively generalize to unseen data, which is an
important characteristic of a good model.

The confusion matrix results presented in Figure 7 are impressive, as they
show that all of the provided models were able to achieve 100% accuracy on the
test set. This suggests that the models have been effectively trained and are
able to make accurate predictions on unseen data, which is a key indicator of
a high-performing model.

Figure 7: Confusion matrices of CQCC-MFCC-BFCC on RPW dataset.

##  6 Evaluation

In this section, several experiments have been conducted to evaluate the
performances of the proposed approach using three public datasets. The first
dataset, TreeVibes222https://www.kaggle.com/datasets/potamitis/treevibes,
contains recordings of internal vibrations of trees that are wirelessly
transmitted to a cloud server using the device detailed in Section 3. The
dataset aims to detect the presence of wood-boring insects that feed or move
inside trees, which can cause high tree mortality. The dataset contains almost
1000 ”clean” label recordings and over 50000 ”infested” label recordings,
making it a valuable resource for detecting tree infestations and preventing
damage caused by insect feeding.

We applied the InceptionV3 model with various features, including CQCC, MFCC,
BFCC, Spectral-Centroid [14] and the proposed combined features (CQCC-MFCC-
BFCC). The results showed a 100% test accuracy in all the cases, indicating
that the dataset may be too simple for DL methods. Since this evaluation
section aims to assess the effectiveness of combining features in improving
the accuracy of detecting tree infestations, we will use machine learning
techniques algorithms such as Support Vector Machine (SVM), Logistic
Regression (LR), Decision Trees (DT), and Random Forest (RF). The results
showed that using different features, the accuracy ranged from 0.980 to 1.000,
with the best accuracy achieved using the proposed combined features with the
LR method. Table 6 shows the detailed results of the experiments on the
TreeVibes dataset. We note that the combination of the three features (CQCC-
MFCC-BFCC) provides the best accuracy of detecting tree infestations for all
four machine learning techniques.

Features | SVM | LR | DT | RF
---|---|---|---|---
CQCC | 0.980 | 0.992 | 0.980 | 0.990
MFCC | 0.982 | 0.998 | 0.990 | 0.997
BFCC | 0.982 | 0.992 | 0.980 | 0.986
Karar et al. [14] | 0.995 | 0.995 | 0.995 | 0.995
Proposed approach | 0.997 | 1.000 | 0.995 | 0.997
Table 6: Experimental results on Treevibes dataset.

To better validate the performance of the proposed approach, we used two
public datasets related to ESC, ESC-10333https://github.com/karolpiczak/ESC-10
and ESC-50444https://github.com/karolpiczak/ESC-50. The two datasets consist
of several brief environment recordings. Each recording consists of five
seconds. We split the ESC-10 and ESC-50 datasets into train:val:test splits
with ratios of 0.8:0.1:0.1.

For the ESC-10 dataset, we trained the InceptionV3 model on five different
feature extraction techniques, including CQCC, MFCC, BFCC, LBP-VAR-ELBP-
LBP1D-LPQ1D-MFCC-GFCC [45], and our proposed combination of CQCC-MFCC-BFCC.
Results showed that the proposed combination achieved the highest validation
accuracy of 0.95 with a precision of 0.947, recall of 1.0, and an F1 score of
0.973. The second-highest accuracy of 0.925 was achieved by the LBP-VAR-ELBP-
LBP1D-LPQ1D-MFCC-GFCC combination. The accuracies of the models trained on
CQCC, MFCC, and BFCC were 0.875, 0.875, and 0.825, respectively. Table 7 shows
the detailed results of the experiments on ESC-10 dataset.

Similarly, for the ESC-50 dataset, we trained the InceptionV3 model using the
same five feature extraction techniques. The proposed combination of CQCC-
MFCC-BFCC achieved the highest accuracy of 0.995 with a precision of 0.995, a
recall of 1.0, and an F1 score of 0.997. The InceptionV3 model trained using
the LBP-VAR-ELBP-LBP1D-LPQ1D-MFCC-GFCC [45] combination achieved the second-
highest accuracy of 0.985, with a precision of 0.995, recall of 0.99, and an
F1 score of 0.992. The models trained using CQCC, MFCC, and BFCC achieved
accuracies of 0.975, 0.96, and 0.98, respectively. Table 8 shows the detailed
results of the experiments on the ESC-50 dataset. Based on these results, we
can conclude that the proposed combination of CQCC-MFCC-BFCC achieved the
highest accuracy for both ESC-10 and ESC-50 datasets.

The InceptionV3 model trained using the LBP-VAR-ELBP-LBP1D-LPQ1D-MFCC-GFCC
combination [45] also performed well for both datasets, achieving the second-
highest accuracy. Moreover, the InceptionV3 model is a fast model, making it a
good choice for these datasets. It is important to note that the choice of
feature extraction technique could impact the final accuracy results, and the
learning rate used during training could also affect the performance of the
model.

Features | Accuracy | Precision | Recall | F1 Score
---|---|---|---|---
CQCC | 0.875 | 0.897 | 0.972 | 0.933
MFCC | 0.875 | 0.919 | 0.944 | 0.932
BFCC | 0.825 | 0.892 | 0.917 | 0.904
Toffa et al. [45] | 0.925 | 0.971 | 0.944 | 0.958
Proposed approach | 0.950 | 0.947 | 1.000 | 0.973
Table 7: Experimental results on the ESC10 dataset.  Features | Accuracy | Precision | Recall | F1 Score
---|---|---|---|---
CQCC | 0.975 | 0.995 | 0.980 | 0.987
MFCC | 0.960 | 0.990 | 0.969 | 0.979
BFCC | 0.980 | 0.990 | 0.990 | 0.990
Toffa et al. [45] | 0.985 | 0.995 | 0.990 | 0.992
Proposed approach | 0.995 | 0.995 | 1.000 | 0.997
Table 8: Experimental results on ESC50 dataset.

Acoustic-based detection systems can complement image-based techniques for
detecting plant diseases and infestations, especially when external symptoms
are not apparent. In many situations, sound can be affected by background
noise. Hence, DL techniques constitute an excellent solution since they are
very effective in detecting RPW, even in background noise. This is because
they can learn to distinguish between the characteristics of the RPW’s
acoustic signature and the noise. The acoustic signals of the RPW have a
unique frequency range, waveform pattern, and duration that differ from those
of other sounds that occur in the environment. DL models, such as
convolutional neural networks (CNNs), can be trained to recognize these unique
characteristics and distinguish them from background noise. However, it is
essential to note that certain limitations need to be addressed to implement
the proposed solution in a real-world scenario. First, we acknowledge that the
TreeVibe device incurs a high cost for acquisition and maintenance, which may
pose a challenge for widespread adoption. Additionally, the considered device
is susceptible to noise if not correctly fitted or calibrated, which can
impact the accuracy and reliability of the detection results. The proposed
solution can be integrated into an architecture based on Optical Fiber
Distributed Acoustic Sensing, as proposed in the paper [5]. Identifying the
locations where the devices will be deployed is crucial, and this will involve
mapping the farm and identifying the areas where RPW infestations are most
prevalent.

##  7 Conclusions

Early detection of RPW infestation can play a crucial role in reducing the
negative impact of this pest on palm trees and the industries that rely on
them. Early detection can help to reduce the spread of the infestation, as
prompt action can be taken to contain and control the population. This can
prevent the red weevil from spreading to other trees, which can help to
preserve the overall health of the palm population. Additionally, early
detection can also help to reduce the cost of control measures. By taking
action before significant damage occurs, less extensive and costly control
methods may be required. This can also help to reduce the overall economic
impact of red weevil infestations on the palm industry. This study suggests
using a DL approach to detect RPW infestation in palm tree farms. The proposed
approach utilizes three features, namely CQCC, MFCC, and BFCC, to convert
sound data received from sensors embedded in palm trees into images. The
classification of resulting images into two classes (infested and not
infested), based on several DL techniques, shows good performances of the
proposed approach on a dataset representing the Al-Ahssa region of the Kingdom
of Saudi Arabia, as well as public datasets. The proposed approach can help to
improve the accuracy and efficiency of detecting and managing RPW. This study
can be extended considering several perspectives. First, the proposed system
can be integrated with existing farm management tools to enable automated pest
control and monitoring. In addition, an important research area to explore
will be the investigation of other advanced DL techniques, such as
reinforcement learning or generative models, to improve the performance of the
detection system and to predict the severity of the infestation and the
potential damage to the tree. Finally, this paper can be extended by
conducting more field trials in various settings. This could involve testing
the system’s effectiveness in different geographical locations, under varying
climatic conditions, and with different species of palm trees, which could
vary in their acoustic properties. Additionally, the impact of various
background noises that might be present in recordings can be further explored.
Such extensive field trials would provide a more robust understanding of the
system’s performance across diverse real-world conditions.

## Acknowledgement

The authors would like to express their deepest gratitude to Prof. Abdulrahman
S. Aldawood for providing the dataset. Also, the authors would like to thank
Prince Sultan University for their support.

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