# Using Deep Learning for Morphological Classification in Pigs with a Focus on
Sanitary Monitoring

Eduardo Bedin<EMAIL_ADDRESS>Junior Silva Souza<EMAIL_ADDRESS>Gabriel Toshio Hirokawa Higa<EMAIL_ADDRESS>Alexandre Pereira
<EMAIL_ADDRESS>Charles Kiefer<EMAIL_ADDRESS>Newton Loebens
<EMAIL_ADDRESS>Hemerson Pistori<EMAIL_ADDRESS>
###### Abstract

The aim of this paper is to evaluate the use of D-CNN (Deep Convolutional
Neural Networks) algorithms to classify pig body conditions in normal or not
normal conditions, with a focus on characteristics that are observed in
sanitary monitoring, and were used six different algorithms to do this task.
The study focused on five pig characteristics, being these caudophagy, ear
hematoma, scratches on the body, redness, and natural stains (brown or black).
The results of the study showed that D-CNN was effective in classifying
deviations in pig body morphologies related to skin characteristics. The
evaluation was conducted by analyzing the performance metrics Precision,
Recall, and F-score, as well as the statistical analyses ANOVA and the Scott-
Knott test. The contribution of this article is characterized by the proposal
of using D-CNN networks for morphological classification in pigs, with a focus
on characteristics identified in sanitary monitoring. Among the best results,
the average Precision metric of 80.6% to classify caudophagy was achieved for
the InceptionResNetV2 network, indicating the potential use of this technology
for the proposed task. Additionally, a new image database was created,
containing various pig’s distinct body characteristics, which can serve as
data for future research.

###### keywords:

CNN , Machine Learning , Computer Vision , Body Condition , Swine

††journal: Smart Agricultural Technology

##  1 Introduction

Sanitary monitoring is crucial to ensure the overall health and well-being of
animals in pig farms, as well as the quality and safety of the final products
[6]. It usually involves a number of inspections, in which one may look for
common symptoms such as sneezing, coughing, diarrhea, omphalitis, and lameness
[13]. The health of the herd also plays a crucial role as a factor that
substantially demands time and effort. This is due to the fact that it
involves applying vaccines, administering treatments and also handling
mortalities. Labor issues can have a negative impact on pig production
performance, particularly when employees are responsible for repairs, feed
production, and manure applications, which can consume valuable time and
reduce productivity. To increase production performance and decrease costs, it
is essential to search for ways to increase labor efficiency through
strategies such as economies of scale, automation, robotization, and
rationalization [32] [12].

From a visual standpoint, according to [3], some of the main characteristics
to look for when evaluating pig health status include the color of the pig’s
epidermis, scratch marks or cuts, and the presence of abscesses, spots, or
crusts on the skin. A healthy pig should have a rosy or blushing dermis color,
an excessively white or pale dermis may indicate anemia, and reddish tones
tending to purple, in turn, may indicate poor blood circulation or breathing
problems. Scratch marks, cuts, signs of recent tail-bitting or aggressive
behavior, as well as abscesses, skin spots, and crusts on the skin can also
indicate health or behavioral issues in pigs. Therefore, regular monitoring of
the visual aspect of pigs is important, since identifying and addressing
potential health issues early on can help prevent disease outbreaks [8] and
improve the overall health of the pig herd.

The absence of injuries, wounds, lesions, and skin conditions is an indicator
of good health and the absence of diseases in pigs. For instance, some
diseases can cause inflammation, discoloration, and spots, resulting in
abnormal pig skin color, as described in [34]. In this context, tail biting
serves as a bodily indicator of health in pigs, as it indicates an injury
caused by aggressive behavior, resulting in lesions [24]. Ear hematomas are a
problem that occurs in pig herds and are caused by the rupture of blood
vessels, leading to a collection of blood predominantly in the ears,
management practices should be implemented to prevent their occurrence [14].
Skin lesions are frequently used as a proxy indicator of post-mixing
aggressive behavior, and scratches are linked to injuries caused by aggressive
behavior [47].

Inspections in the herd can be used as a tool to monitor pig health and
welfare. A monitoring system can provide veterinarians and producers with
several reports about the health status of the herd, giving a quick
identification of potential health issues before they worsen, resulting in
better management practices, reduced treatment costs, and optimized herd
performance [19]. Since many diseases of interest present visual symptoms,
computer vision (CV) and machine learning are powerful tools in epidemiology
and disease control, especially in the context of animal production, where
they can be used to assist in, or even to automate, livestock monitoring,
allowing farmers and other stakeholders to quickly identify and address any
issues that may arise. Another advantage is that it is a less invasive
monitoring method since it avoids techniques such as radio frequency
identification from chips, identification by painting pigs, microphones, and
accelerometers [4]. Overall, the use of computer vision and machine learning
in epidemiology and disease control has enormous potential, and there are many
current research opportunities in this area [16, 25].

As per sources like [24], modern production systems that prioritize rapid
growth of pigs for meat production have significant negative impacts on
animals’ health, given the stressful conditions that they face in the
facilities. This is also a cause of symptoms such as tail-biting, cuts, and
scratches. Furthermore, studies such as the one described in [43] state that
identifying and classifying tail-biting is challenging because of the wide
range of visual characteristics that damaged tails can exhibit, which can vary
from minor abrasions to severe putrefaction. The behavior that leads to these
injuries is also essential, as it can be sudden, strong, and obsessive, which
affects the severity of the damage caused. As reported in [29], the types of
bruises, scratches, cuts, and tail-biting observed in pigs are directly linked
to their specific behaviors.

Usually, in computer vision research applied in a pig environment, the images
are captured using artificial or natural illumination and they are normally
captured from an upper view, typically using RGB color space [36]. The camera
setup, including its positioning, and the lighting conditions are critical
aspects of any image-based research project, and researchers should carefully
consider these factors when designing their experiments, as discussed in [51].
The use of Precision Livestock Farming (PLF) can reduce time-consuming tasks
and extra labor for farmers, these technologies can help monitor health
indicators, allowing for the measurement of the health status of the pig herd
through parameters such as vital signs, body temperature, sounds and calls,
behavioral signs, facial expressions, skin color, and weight [37].

The use of smartphones in farms is becoming an increasingly common practice,
farmers adopt this kind of technology to assist in their daily activities,
improve efficiency, and reduce costs [23]. Some traditional methods of
applying computer vision technologies require the use of computers and complex
software systems, and they involve high-cost investments and personnel trained
in these technologies, in this context, the use of mobile devices to capture
images appears as a good solution [45]. The use of mobile devices to acquire
images for a Computer Vision system can reduce costs and labor associated with
image acquisition. They don’t require a fixed point to install the device,
which allows for more flexibility in capturing images from different positions
and angles and are easy to use with flexible technologies [33]. When a deep
learning system achieves good results using high-resolution images captured by
a mobile and low-cost device, it is an indicator that the proposed system can
be developed in practical terms with few financial requirements [30].

The application of computational techniques for animal farming already has
important precedents. Among the tasks performed, it is possible to include the
identification of pigs, posture recognition and tracking [50] [53] and
behavioral monitoring, as well as health and well-being assessment, as
discussed in [7] and in [15]. In the field of Computer Vision, Deep
Convolutional Neural Networks (CNNs) are a type of deep learning algorithm
that has become widely used. CNNs have shown good performance not only in
computer vision tasks such as classification, object detection, and semantic
segmentation but also in natural language processing, as discussed in [2].
Regarding its application to pig farming, [31] used CNNs for pig facial
recognition with high-resolution images, and [18] used CNNs to identify 10 pig
faces using standard-resolution images.

CNNs have been used for the detection and monitoring of swine behavior
patterns in various works, such as that of [28], which studied the detection
of caudophagy, and that of [15], which monitored the occurrence of caudophagy
with diarrhea. Other studies have focused on the recognition of feeding
behavior [9], on the monitoring of behavioral patterns in environments with
varying greenhouse gas concentrations using Faster R-CNN and YoloV4 [5], and
on the use of Mask CNN to study pig mounting behavior [27]. R-CNN was used by
[46] for segmentation and automatic detection in pigs. [49] applied CNN-SVM to
automate the determination of the body score of pigs with segmentation. [48]
compared a Mask CNN with other neural networks for swine segmentation in the
production environment.

Regarding measuring pig health through body conditions, some works focus on
using computer vision techniques to measure shape and temperature as health
indicators [52], using segmentation to construct a multi-source pig-body
multi-feature representation to analyze pig health. Other studies focus on
detecting pig body posture as a health indicator [36] using 2D cameras. Other
lines of research focus on the use of thermal images to identify high
temperatures in some parts of the pig’s body [44] that can be related to an
injury or other health deviation.

During the authors’ search, to write this paper, no related works were found
about using computer vision techniques to identify and classify pig body
characteristics related to health status, such as scratches, ear hematomas,
caudophagy, redness, or other morphological characteristics. Based on this,
the purpose of this work is to evaluate the use of D-CNN (Deep Convolutional
Neural Networks) algorithms in classifying pig body morphologies as normal
conditions or non-normal conditions, with a major focus on characteristics
observed in sanitary monitoring. The classified characteristics are
caudophagy, ear hematoma, scratches, signs of redness in the body, and natural
spots, each one was tested separately. Six D-CNN architectures were employed
for evaluation: DenseNet201, EfficientNet, InceptionResNetV2, InceptionV3,
MobileNetV2, and Xception, along with the SGD optimizer.

Five experiments were conducted, each one corresponding to a specific
morphological characteristic to be classified. For each experiment, a specific
dataset was constructed, consisting of two distinct classes: one representing
the normal condition and the other representing the non-normal condition.

The contribution of this article revolves around the proposed utilization of
D-CNN (Deep Convolutional Neural Networks) algorithms for the morphological
classification of pigs, the focus is primarily on classifying pig body
characteristics observed during sanitary monitoring. Among the notable
results, the InceptionResNetV2 network demonstrated an average Precision of
80.6% to classify caudophagy, indicating the potential applicability of this
technology for the proposed task. Furthermore, the article also presents the
creation of a new image database containing pigs with diverse body
characteristics, which can serve as valuable data for future research
purposes. In future applications, this classification approach could be used
to design a system capable of identifying abnormalities in pigs, thereby
assisting farmers and veterinarians in more accurately identifying these
characteristics.

##  2 Materials and Methods

###  2.1 Image acquisition

The images were collected in a pig farm located in São Gabriel do Oeste, Mato
Grosso do Sul, Brazil, with animals aged around 70-180 days, weighing from
30kg to 130kg, in a period from March until July of 2022. An authorization
request for the collection of the images was submitted to the Ethics Committee
on Animal Use of Dom Bosco Catholic University. The authorization was granted
under the certificate Nº 04/2022.

Five different datasets were constructed for this study, with each dataset
dedicated to analyzing a specific morphology characteristic such as
(1)caudophagy, (2)ear hematoma, (3)redness, (4)scratches, and (5)natural
spots. Three characteristics refer to marks on the skin: redness, scratches,
and natural spots, and two refer to the shape of the body: auricular hematoma
and caudophagia. Four of these morphological characteristics refer to health
problems, as mentioned by [3], [34], [24], [14], and [47]. The analysis also
included the selection of natural spots as a characteristic to check if the
networks can be useful in distinguishing these natural patterns in pig bodies.

Each dataset was divided into two classes: one for normal conditions of the
morphology and the other for non-normal conditions. To build these datasets, a
smartphone device was used to capture pig images in a production environment.
Table 1 displays the parameters used for image acquisition. After capturing
the images, they were disposed of and divided into specific datasets based on
the morphology to be analyzed. The images were captured angular to the floor
where the animals were located and the place was influenced by both natural
and artificial light, the presence of well-illuminated images, and poorly
illuminated images with the presence of shadow or low light incidence. The
captures took place in the morning, between 8:00 a.m. and 10:00 a.m., as well
as in the afternoon, between 1:00 p.m. and 3:30 p.m. The animals were housed
in buildings with natural ventilation or climate-controlled buildings.

Table 1: Parameters of Device and Environment in Image Capture Procedure Parameter | Value
---|---
Device | Moto e(6i)
Focal Distance | 3.7mm
Original Resolution | 2340 x 4160 pixels
Acquisition | Manual
Height | 1.00m - 1.5m
Acquisition Angle | Angular
Illumination | Natural and Artificial

As the aim of this paper is to classify the proposed morphologies as normal or
non-normal conditions, after image acquisition, and the images disposed of and
divided into specific datasets based on the morphology to be analyzed, the
dataset-building process was finalized by cropping the original images to
highlight the relevant morphology. This was done to eliminate unnecessary
information contained in the images and focus solely on the respective
morphology that the network should classify in each task. Figure 1 shows
samples of the images that constitute each dataset. The five datasets are
comprised of 715 images. Table 2 shows the number of images that compose each
dataset used in each experiment. It is important to emphasize that each
dataset was used separately in each experiment, to classify the specific
morphology.

Table 2: Quantity of images in each morphological dataset. This table columns represent the groups, Number of Images, Number of Anomalous, and Number of Normal Images in each dataset. Group | #Img | Anomalous | Percentage | N
---|---|---|---|---
Caudophagy | 182 | 90 | 49.46% | 92
Ear Hematoma | 143 | 64 | 44.76% | 79
Spots | 212 | 139 | 65.57% | 73
Scratches | 124 | 51 | 41.13% | 73
Redness | 60 | 30 | 50% | 30

(a)

(b)

(c)

(d)

(e)

Figure 1: Examples of pig images that are used in the five datasets. Spotted
Pig (a), Pig with Redness (b), Scratched Pig(3), Pig Tail with Caudophagy (4)
and One Example of Pig with Ear Hematoma (5)

###  2.2 Neural Networks

To develop this work, six different kinds of D-CNN and one optimizer were used
for each morphology to be classified. The purpose was to analyze the capacity
of each proposed network to classify each morphology in normal or abnormal
conditions. The networks used were EfficientNet, Xception, DenseNet201,
InceptionResNetV2, InceptionV2, and MobileNetV2. Stochastic Gradient Descent
(SGD) was used for optimization. Table 3 shows the standard number of
parameters of each neural network111As stated here:
https://keras.io/api/applications/..

Through the parameters, also called weights, which the algorithm learns during
the training phase, the difference between Neural Networks can be analyzed by
the number of parameters. When a Neural Network has more parameters, it has
the capacity to learn more complex patterns. However, networks with fewer
parameters use less computational resources. In this context, the appropriate
choice of Neural Networks can be determined by finding a good balance between
the number of parameters and computational cost [26] [17].

Table 3: Number of parameters of each D-CNN used in the five experiments  Architecture | # of parameters
---|---
EfficientNet | 19.5M
Xception | 22.9M
DenseNet201 | 20.2M
InceptionResNetV2 | 55.9M
InceptionV3 | 23.9M
MobileNetV2 | 3.5M

The EfficientNet architecture is based on a scaling method that simultaneously
increases the depth, width, and resolution of the network, which allows it to
achieve higher accuracy with fewer parameters and computational resources
compared to other state-of-the-art models [42]. Xception is a deep neural
network architecture that was proposed as an alternative to the traditional
Inception architecture for image classification tasks. Its architecture
replaces the standard Inception modules, which perform convolutions with a
mixture of filter sizes, with depthwise separable convolutions. The use of
depthwise separable convolutions in Xception results in a significant
reduction in the number of parameters compared to the traditional Inception
architecture [10].

DenseNet201 is a deep neural network architecture that belongs to the family
of DenseNets, which were designed to address the issue of vanishing gradients
in deep neural networks. Specifically, DenseNet201 is a very deep neural
network architecture that has 201 layers, hence the name. The architecture
consists of several dense blocks, where each dense block contains multiple
convolutional layers that are connected to all subsequent layers in the block
[21]. InceptionResNetV2 is a deep neural network architecture that combines
the Inception and ResNet architectures, this network combines these two
architectures by using Inception modules with residual connections each
Inception module has a residual connection that skips over the module and
directly connects the input to the output of the module [41].

InceptionV3 is a deep neural network architecture that is designed for image
classification tasks, the core idea behind the Inception architecture is to
use convolutional layers with filters of multiple sizes at each layer. This
allows the network to capture features at different scales and resolutions
while minimizing the computational cost of the network [38]. On the other
hand, MobileNetV3 is another convolutional neural network architecture
specifically designed for mobile devices, such as smartphones. It was
developed using a combination of network hardware architecture (NAS) and the
NetAdapt algorithm. The NAS approach involves the use of reinforcement
learning to search for optimal neural network architectures, while NetAdapt is
an algorithm used to adapt neural networks to different hardware constraints
[20].

The optimizer used was SGD (Stochastic Gradient Descent), which is a popular
optimization algorithm used in deep learning to train neural networks [39].
The basic idea behind SGD is to take small steps in the direction of the
steepest descent of the loss function, i.e., the direction in which the loss
function decreases the fastest. This is achieved by computing the gradient of
the loss function with respect to the model parameters for a randomly selected
subset of the training data, known as a mini-batch. The model parameters are
then updated based on this gradient [40].

###  2.3 Experimental setup

The experimental setup consists of performing five experiments using the same
six D-CNNs and the SGD optimizer mentioned in Subsection 2.2. Each experiment
corresponds to a specific morphology from the datasets mentioned in Section
2.1: Ear Hematoma, Caudophagy, Scratches, Redness, and Natural Spots. This
procedure aims to evaluate the applicability of these networks in classifying
images of pig morphologies as normal or non-normal conditions.

The Deep Neural Networks222In this work, the TensorFlow [1] framework and the
Keras API [11] were used to train the neural networks. listed above were
tested in a 10-fold stratified cross-validation strategy, and used the Data
Augmentation technique to improve the images samples during the training
phase. In each experiment, 30% of the training images were used for
validation. The values of hyperparameters were adjusted according to
experimental tests during the development phase of this work, and the range of
values that showed good results were presented in Table 4. These values were
the same for all neural networks.

Table 4: Hyper-parameters used in the five experiments and their values Hyperparameters |  Value
---|---
Learning Rate |  0.001
Learning Rate Decay |  0.0001
Bacth Size |  8
Epochs 200 |  200
Patience Percentage |  15
Validation Set |  30
Dropout |  0.5
Data Augmentation |  Flip = True
Fill Mode= ”Nearest”
Zoom = 10
Height Shift Range= 0.5
Widht Shift Range= 0.5
Rotation = 90

Three common classification metrics were used to evaluate the algorithms:
Precision, Recall, and F-score. Precision is used as a measure of the
proportion of correctly predicted positive instances out of all instances,
focusing on the accuracy of positive predictions. Recall measures the
proportion of correctly predicted positive instances out of the actual
positive instances, focusing on the model’s ability to find all positive
instances. The F-score metric is the harmonic mean of Precision and Recall.
This metric is useful when the costs of false positives and false negatives
differ. Box plots were generated from the metrics Precision, Recall, and
F-Score. Grad-CAM scores were also generated and used in the analysis of the
results. This technique enables the visualization of visual maps that
highlight where the algorithm focuses its attention in the classification
task. After running the model and saving the results, an ANOVA hypothesis test
was applied with a threshold of 5% in the three metrics analyzed. The Scott-
Knott clustering test was used post-hoc when the ANOVA results were
significant333Both the ANOVA and the Scott-Knott clustering test were
performed in the R Programming Language [35, 22]..

##  3 Results and discussion

The replicability of the results of this paper is delimited by the procedure
and conditions of image acquisitions described in subsection 2.1, and to the
networks, and hyperparameters presented in sections 2.2 and 2.3. It is
important to emphasize that the primary goal of the aforementioned procedure
is to analyze the capacity of the D-CNN (Deep Convolutional Neural Network) in
classifying some specific pig morphologies as normal or abnormal.

The boxplots for each performance metric are shown in Figure 2. For each
metric, boxplots representing each architecture are grouped by morphology.
Tables 6, 7 and 8 show statistics for Precision, Recall, and F-score,
respectively, for each architecture, also grouped by morphology. Next to the
mean values, the results of the Scott-Knott clustering test are indicated. The
ANOVA results can be found in Table 5. Figures 3, 4, 5, 6 and 7 show the
GradCam comparison of Spots, Ear Hematoma, Caudophagy, Scratches, and Redness.

Figure 2: Boxplot of metrics performance networks in the function of the
morphology that was classified in each experiment

The ANOVA test indicates that for scratching and caudophagy the null
hypothesis is to be rejected for each one of the metrics - Precision, Recall,
and F-score. For auricular hematoma, the ANOVA results for Precision and
F-score were also significant, but the ANOVA result for Recall was marginally
insignificant ($p=0.0554$). In this case, the Scott-Knott test was still
applied and yielded different clusters. For spots, the null hypothesis is to
be accepted for Precision, with a fully insignificant ANOVA result
($p=0.1060$), but not for Recall and F-score. As for redness, the ANOVA
results were not significant for any of the metrics.

Table 5: ANOVA Summary for Each Experimental Test

Morphology | Precision | Recall | Fscore
---|---|---|---
Scratches | 0.0010 | 0.0090 | 0.0003
Ear hematoma | 0.0218 | 0.0554 | 0.0075
Caudophagy | 1.04e-05 | 7.19e-06 | 4.67e-06
Spots | 0.1060 | 0.0431 | 0.0243
Redness | 0.5910 | 0.3140 | 0.2840

Figure 2 shows that many of the larger IQR values were observed for the
redness morphology, which suggests greater heterogeneity in this dataset since
it was divided in folds, but its reduced size can be considered responsible
for at least part of the great variability in these results, since it means
that there is less data available for training and, most importantly for this
specific point, for testing, in each one of the ten folds. On the other hand,
the spots morphology, which has the largest dataset, presented the lowest
observed averages, with small interquartile ranges.

For the natural spots, the ANOVA results for Precision were not significant,
and, as Tables 6, 7 and 8 show, EfficientNet and Xception networks achieved
the higher performance measured by Recall and F-score. Possible reasons for
spots being of more difficult identification for the neural networks are that
these natural spots are more likely to be confused with dirt and other
environment-related conditions (e.g. shadows) in virtue of their color (dark-
grayish, as opposed to the more vibrant reddish tones). Admittedly, Figure
3(a) does not seem to follow this logic, and one could argue that the neural
network is giving more importance to the actually spotted pig on the left. On
the other hand, Figure 3, which shows the corresponding GradCAM, suggests that
the non-spotted pig does influence the prediction, and it is possible to
support the presented argument by blaming the light conditions that are
causing shadows that resemble spots.

(a)

(b)

Figure 3: GradCAM analisys of No-Spot that was wrong Classificated.

(a)

(b)

Figure 4: GradCAM of ear hematoma that was correctly classified.

(a)

(b)

Figure 5: GradCAM of normal tail mistakenly classified as tail with
caudophagy.

(a)

(b)

Figure 6: GradCAM of scratches in pig body that was correctly classified.

(a)

(b)

Figure 7: GradCAM of normal pig skin color that was mistakenly classified as
redness.

More generally, during the evaluation of network errors in image
classification, certain characteristics were identified. For instance, when
using a morphology-based format to identify features like caudophagy and ear
hematoma, the D-CNN model tended to misclassify images with poorly-defined
formats or with environment colors similar to the body’s tone. In addition,
the model struggled with distinguishing between normal characteristics and
mild deformations in such morphologies, leading to incorrect classifications.
Figure 4 is an example of right Ear Hematoma classification, when is possible
to evaluate that the network focused on the deformed ear to classify the image
correctly, and 5 is an example of wrong Caudofagy classification, when is
possible to observe that the network focused in an upper side of the tail and
classified it as a caudophagy but it isn’t.

Table 6: Precision statistics: The table shows the networks that presented the
best results.

Scratches
---
Architecture | Median (IQR) | Mean SK (SD)
EfficientNet | 0.7757 (0.1715) | 0.7178 a ($\pm 0.2116$)
Ear hematoma
EfficientNet | 0.7411 (0.2614) | 0.7614 a ($\pm 0.1477$)
InceptionResNetV2 | 0.7744 (0.0926) | 0.7117 a ($\pm 0.1719$)
Caudophagy
InceptionResNetV2 | 0.8375 (0.0965) | 0.8063 a ($\pm 0.0954$)
Spots
EfficientNet | 0.5795 (0.4227) | 0.5646 ($\pm 0.2374$)
Redness
EfficientNet | 0.8375 (0.3333) | 0.6925 ($\pm 0.1835$)

Table 7: Recall statistics. The table shows the networks that presented the
best results.

Scratches
---
Architecture | Median (IQR) | Mean SK (SD)
EfficientNet | 0.6786 (0.1905) | 0.6876 a ($\pm 0.1889$)
InceptionResNetV2 | 0.6786 (0.2000) | 0.7029 a ($\pm 0.1578$)
Ear hematoma
EfficientNet | 0.7046 (0.2381) | 0.7362 a ($\pm 0.1417$)
Xception | 0.7678 (0.1279) | 0.7183 a ($\pm 0.0927$)
Caudophagy
Xception | 0.8056 (0.1540) | 0.7909 a ($\pm 0.0985$)
InceptionResNetV2 | 0.8056 (0.1540) | 0.7717 a ($\pm 0.1157$)
Spots
EfficientNet | 0.5516 (0.1483) | 0.5927 a ($\pm 0.1358$)
Redness
EfficientNet | 0.7500 (0.2916) | 0.6833 ($\pm 0.1834$)
InceptionResNetV2 | 0.6667 (0.1249) | 0.7166 ($\pm 0.1765$)

Table 8: F-Score statistics. The table shows the networks that presented the
best results.

Scratches
---
Architecture | Median (IQR) | Mean SK (SD)
EfficientNet | 0.6773 (0.2052) | 0.6788 a ($\pm 0.2003$)
InceptionResNetV2 | 0.6773 (0.2036) | 0.6852 a ($\pm 0.1818$)
Ear hematoma
EfficientNet | 0.7098 (0.2430) | 0.7347 a ($\pm 0.1447$)
Xception | 0.7664 (0.1420) | 0.6976 a ($\pm 0.1241$)
Caudophagy
Xception | 0.8039 (0.1516) | 0.7890 a ($\pm 0.09949$)
Spots
EfficientNet | 0.5262 (0.2378) | 0.5494 a ($\pm 0.1779$)
Redness
EfficientNet | 0.7476 (0.3080) | 0.6587 ($\pm 0.2074$)
InceptionResNetV2 | 0.6667 (0.1527) | 0.6787 ($\pm 0.2287$)

When it comes to morphologies based on skin characteristics, the success of
the networks in correctly classifying images depends on the contrast between
the pig’s body and the background. However, the networks often make mistakes
in classifying these morphologies, particularly when the images have poorly-
defined contours, details close to the ground or wall, and weak contrast
between the foreground and background. These factors can make it challenging
for the network to distinguish between normal and abnormal skin
characteristics, resulting in incorrect classifications. Interestingly, for
scratches, the density of the scratches seems to play a significant role in
the network’s ability to identify them. This indicates that the network may
perform better at identifying scratches that are more prominent or visible.
Figure 6 is an example of correct Scratches classification, it is possible to
observe that due to the high density of scratches on the pig’s body, the
network focuses on a specific region and makes the correct classification. On
the other hand, in Figure 7, we see an example of incorrect Redness
classification. In this case, the image provided to the network features an
entirely white pig, but the network focuses on a shiny part and misclassifies
it as redness, which is not accurate.

Upon observing the results of the Scott-Knott test, MobileNetV2 was clustered
in group ’b’ in 4 out of the 5 morphologies studied, with the exception of the
redness morphology. This network presented the smallest means and medians in
each situation, including redness, which can be attributed to its design that
utilizes a reduced number of parameters, as can be seen in Table 3. This
architectural option was taken in order to achieve high performance on devices
with smaller computational capabilities. This is relevant for the purposes of
a future system since it suggests that smaller networks perform poorly on this
task, but more tests with other smaller and newer networks may show otherwise.
Also, on the other hand, using bigger neural networks does not necessarily
lead to better performance in the present problem, since usually there was no
significant difference between EfficientNet, the second smallest neural
network tested by us, and InceptionResNetV2, the biggest one. In all the cases
where a significant difference was observed, EfficientNet had the upper hand.

##  4 Conclusion

In this work, we studied the possibility of applying neural networks to the
classification of normal from abnormal morphological characteristics in pigs.
Turning possible to classify these characteristics is important for possible
applicability in sanitary monitoring, which can help to improve animal health
and welfare through the possibility of a better way to classify pig body
morphology conditions. The results indicate that the proposed use is feasible,
but the performance is still below necessary for the considered morphological
characteristics: caudophagy, ear hematomas, redness, scratches, and spots.
Given the scarcity of references on the specific subject, it becomes notable
to conduct further research in this area. Such research could be centered on
studying and developing algorithms that can more accurately detect and
classify abnormalities in swine bodies, which could be indicative of poor
health. Moreover, the research could delve into exploring additional
morphologies that may signal a deviation from good health.

##  5 Declaration of competing interest

The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported
in this paper.

## Acknowledgments

Some of the authors were awarded scholarships from the Brazilian National
Council of Technological and Scientific Development (CNPQ), and Foundation
University of Technology and Sciences Enterprise (Fundatec), and University
Higher Education Personnel Improvement Coordination (CAPES). This work has
received infrastructure support from Dom Bosco Catholic University. We would
also like to thank NVIDIA for providing the Titan X GPUs used in the
experiments.

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