An overview of the current trends in precision pig farming technologies 2may2021

Livestock Science 249 (2021) 104530
Available online 2 May 2021
1871-1413/© 2021 Elsevier B.V. All rights reserved.
Review article
An overview of the current trends in precision pig farming technologies
Christos Tzanidakis a,*
, Panagiotis Simitzis b
, Konstantinos Arvanitis c
, Panagiotis Panagakis a
a
Agricultural University of Athens, Department of Natural Resources Management and Agricultural Engineering, Laboratory of Farm Structures, 11855 Athens, Greece
b
Agricultural University of Athens, Department of Animal Science, Laboratory of Animal Husbandry, 11855 Athens, Greece
c
Agricultural University of Athens, Department of Natural Resources Management and Agricultural Engineering, Laboratory of Farm Machinery, 11855 Athens, Greece
H I G H L I G H T S
• PLF in Pig Farms.
• PLF technologies include vision, sound, other CIT sensors.
• PLF usage generally improves the economy of pig units.
• Future of PLF research for commercial use.
A R T I C L E I N F O
Keywords:
Precision Livestock Farming
Pigs
Technology
Real-time monitoring
Bio-responses
A B S T R A C T
Pig farming is one of the most technologically advanced and continuously growing production sectors. As the pig
herds grow larger and at the same time the number of farmers decreases worldwide, it is almost impossible for
the farmers to assess every individual animal and assure its wellbeing. Precision Livestock Farming (PLF) could
provide solutions to these problems. PLF is intending to achieve fully automated continuous monitoring of pigs,
emphasizing on each pen, by using technological advancements as part of the management process. It can
potentially improve animal welfare, feed efficiency, performance and reduce livestock emissions and therefore
positively impact the financial viability of the unit. The data may be collected by cameras (CCTV, infra-red,
thermal, etc.) and real-time analyses systems, by microphones and sound analyses systems, or by any other
sensor within the production unit or on the animal such as accelerometers, RFID sensors, etc. This overview
presents and critically discusses several papers, attempts to make predictions of future technological de
velopments, and proposes potential research needed.
1. Introduction
The demand for meat products from the growing global population is
continuously increasing (Ahrendt et al., 2011; Berckmans, 2017;
OECD-FAO, 2019) as the latter is expected to rise to 9.15 billion by 2050
- over 34% population increase (OECD-FAO, 2019). Globally, pig meat is
the second most preferred meat for consumption due to its low relative
prices (United States Department of Agriculture (USDA) 2017; OECD-
FAO, 2019). In total numbers, pig meat production is expected to grow
by approximately 11 Mt (i.e., 9.3%) by 2028 (OECD-FAO, 2019). In EU,
the increased environmental and public concerns on manure manage
ment and animal welfare status alongside the EU population structure
changes are expected to limit the production growth (OECD-FAO, 2019).
It must be noted that OECD-FAO (2019) reported that pig meat real price
declined in 2018, implying that that the number of pig farm companies
worldwide will continue to shrink, while at the same time the bigger
companies will grow bigger and thus, the production management
process will evolve further to assess the environmental and welfare
problems of management processes. Thus, the transition will result in
higher energy demands and, in less time spent by the farmer for animal
condition assessment.
Until the 1960s pig production was typically based on numerous
traditional smallholder family rearing systems (Kyriazakis and Whitte
more, 2006; PCIFAP, 2008), with approximately 4,000 m2
of pasture
access per 20 pigs (HIS, 2014). During the last half of the 20th
century
changes in animal farming modified the applied housing practices and
management and the production was converted from extensive to
intensive commercialized facilities (PCIFAP, 2008). By the 1990s, the
* Corresponding author.
E-mail addresses: ctzan@aua.gr (C. Tzanidakis), pansimitzis@aua.gr (P. Simitzis), karvan@aua.gr (K. Arvanitis), ppap@aua.gr (P. Panagakis).
Contents lists available at ScienceDirect
Livestock Science
journal homepage: www.elsevier.com/locate/livsci
https://doi.org/10.1016/j.livsci.2021.104530
Received 30 January 2021; Received in revised form 23 April 2021; Accepted 27 April 2021

2
dominant production type was ‘’megafarms’’ (i.e., more than 10,000
breeding sows in a single location) covering more than half of the pig
production globally (HIS, 2014). NAHMS (2001) reported that less than
5% of the pigs finished in the US were housed on pasture or dirt pens and
only a 9% were housed in open building with outside access. Due to this
great change pig production units have grown a lot bigger, increasing
the risk of disease outbreaks and stereotypes that are negatively related
with the health and welfare status of the animals, respectively (TFECHP,
2014). According to HIS (2014), the largest pig production company
globally houses nearly 1.1 million breeding sows and the next top 9
companies keep more than 100,000 sows each. In addition, TFECHP
(2014) reported that in the UK the average herd size for fattening pigs
was 1,000 or greater compared with that in 1994, when the average
herd size was less than 600. These reports imply that the animal number
per worker has been greatly increased over the past decades.
The transition from extensive to intensive housing has induced great
changes in pigs’ husbandry. For example, the piglets are naturally
weaned at approximately 17 weeks of age (Jensen and Recén, 1989),
while under intensive housing conditions they are commonly weaned
prematurely at approximately 4 - 5 weeks of age (D’Eath and Turner,
2009; OECD, 2018). Furthermore, sows housed extensively will breed
twice a year, while when housed intensively this number is significantly
higher by at least 15 - 20 % (OECD-FAO, 2019). This indicates that the
pigs grow in size and reach puberty and adulthood much faster than
their free-ranging or extensively reared counterparts. The social issue
that arose from this transition, is the lack of space for pigs to express
their natural behaviours such as exploration and foraging. In observa
tions on the foraging behaviour of domestic pigs housed extensively,
each group member kept 3.8 m on average from their nearest neighbour
and different herds kept a distance of 50 m or more, according to feed
availability (HIS, 2014). In contrast, each pig housed under intensive
commercial conditions has only 0.25 - 2.25 m2
at its disposal (OECD-
FAO, 2019), depending on its characteristics (e.g., age, gender, group
size, etc.). Although, behavioural display has a multifactorial origin,
these changes contributed to aggression heightening (Peden et al.,
2018), the appearance of undesired and abnormal social behaviours (e.
g., belly-nosing, tail and ear biting, etc.) and unstable dominance hier
archies (D’Eath and Turner, 2009) posing risks to their health and
welfare status (Hintze et al., 2013). Moreover, it is reported that in USA
diseases and disease spreading is estimated to be responsible for 20% of
mortality losses in pig barns (NASEM, 2019). If a disease cannot be
prevented using medical methods such as vaccinating, it needs to be
detected as early as possible to provide the farmer time to act and
effectively prevent its dispersion within the pig chamber (Pessoa et al.,
2021). As the incidence of resistances to antibiotics tends to increase due
to extensive supplementation (EFSA, 2019), the need for alternative
methods reducing their use is of great importance (Girard and Bee,
2019). Continuous surveillance in contemporary pig farming to assess
disease spreading is a possible approach for the problem (Boyd et al.,
2019). In context Precision Livestock Farming (PLF) could provide so
lutions to these problems.
PLF is the fully automated continuous monitoring of animals,
emphasizing on the individuality (in case of pig farming on each pen), by
using technological advancements as part of the management process
(Banhazi et al., 2012b; Berckmans, 2014a; Berckmans, 2017; Norton
et al., 2019). These advancements, applied at the production level, aim
at increasing the farmer’s ability to continuously monitor pigs’ everyday
lives despite the size of the herd (Vranken and Berckmans, 2017).
Monitoring and analysing bio-responses is the starting point of any PLF
system, providing the datasets that will be used for the development of
algorithms that will control certain parameters in the production process
(Matthews et al., 2016; Nasirahmadi et al., 2019a). When a problem
within the unit is detected, a warning signal is triggered so that imme
diate action can be taken, leading to an early problem solution (Berck
mans, 2017). Thus, PLF decision support tools can potentially improve
animal welfare, feed efficiency, antibiotics use and performance, reduce
livestock emissions, and enhance the economic stability of rural areas by
minimising the annual costs of the units (Banhazi et al., 2012b; Nilsson
et al., 2015; Lopes et al., 2016; Pomar et al., 2019).
2. PLF technologies main protocol
Every living organism is a “Complex, Individually Different, Time-
Varying and Dynamic (CITD) system” (Quanten et al., 2006). In bio
logical research, most statistical analyses and comparisons are between
groups of living organisms by comparing the differences between the
averages of the groups (Berckmans, 2014b). However, although the
genetic selection is aimed at making pigs as uniform as possible, every
living organism responds differently to various conditions and envi
ronmental changes (Berckmans, 2017). In addition, there are
time-varying characteristics; each individual animal will express a
different response to an environmental change or a stressor each time it
occurs (Quanten et al., 2006; Berckmans, 2014b).
According to Berckmans (2004) a PLF system:
(a) is a support tool and does not intend on replacing the farmer,
(b) is an animal-centric tool – the animal is the main part of the
process
(c) needs ideal conditions for the monitoring and control processes.
PLF are real-time monitoring technologies with core purpose to
“manage even the slightest manageable production unit’s temporal
variability” (i.e., the per pig pen approach) (Halachmi and Guarino,
2016; Halachmi et al., 2018). The process of developing a PLF early
warning system for the farm is based on the hypothesis that when a pig
within a pen experiences dis-comfort conditions, it will exhibit a
bio-response in terms of behavioural changes (Nilsson et al., 2015;
Matthews et al., 2016; Berckmans, 2017). It should be noted that the
most direct insight for animal welfare assessment is animal-based ob
servations (Temple et al., 2012) thus, behavioural analysis should be in
the main core of research. The first signs of a behavioural change should
be detected by the PLF system – either image/video processing, sound
analysis or any other sensor capable for detecting the elements
responsible for behavioural changes such as RIFD, thermometers, etc
(Berckmans, 2014b). Therefore, the first step of creating a system that
automatically monitors pigs’ behaviour and deals with a problem based
on their bio-responses, is to document and label a set of data that is
captured over a suitable time period (Banhazi and Black, 2009). With
the resulting analysis, it is possible to build an automatic classifier that
classifies patterns leading to behavioural differences due to unsuitable
conditions (Berckmans, 2009; Statham et al., 2009). However, the cor
rect decoding and value assessment of the collected data is very
important, before their use in improving the applied management sys
tem (Rojo-Gimeno et al., 2019). Hence, the animals’ bio-responses could
be used as a sensor providing information (i.e., data) and the system’s
developed algorithms are trying to translate them into performance,
welfare, and sustainability production indicators measurements
(Vranken and Berckmans, 2017). The second step is to develop dynamic
mathematical models based on the parameters of these patterns and to
define specific indicators for these behaviours (Berckmans, 2014a). This
protocol unified with a computer system tracking and monitoring these
parameters continuously can produce a tool for real-time problem so
lution that will improve feed efficiency, diet provision and housing
conditions and minimise the annual costs of the pig unit (Berckmans,
2014a).
The final step in developing an automatic dynamic control tool is to
build up a model that links behaviour responses such as resting, feeding,
drinking, panting, huddling, floor occupation area, aggressive interac
tion and activity to the parameter of interest such as growth parameters
like body weight, feed intake, feed conversion rate or environmental
conditions like temperature, relative humidity, airborne particle con
centration, etc. This is not a simple task and several scientists have been
C. Tzanidakis et al.

3
working on such simulation models over many decades (Berckmans,
2006). Controlling each parameter via aspects of behaviour is very
complicated and requires knowledge of how the animals respond to
different conditions and variations of the parameter of interest. The
responses are likely to involve both steady-state and dynamic compo
nents, therefore complex modelling approach, including mechanistic,
empirical and biological models is necessary to describe them (Smouse
et al., 2010). As a result, the model should be able to first describe a
relationship between the parameter of interest, for example temperature
adequacy and the behaviour and secondly predict the animal behaviour
from past information. A third step is a comparison between predicted
and real measured behaviour, which will reveal if the animal status has
been changed (i.e., from comfort to discomfort). The status change can
be either determined by the prediction error, namely the error between
predicted and real measured value, or the change in the model param
eters that are defined by the model structure. The information to what
extent the animal state has changed can then be used as input for the
model-based controller determining possible adjustments in the
parameter settings that are necessary in an effort to bring the animal
back to its normal state (i.e., comfort behaviour; Werkheiser, 2018). The
monitoring and control algorithm would be constructed by the experi
mental data and first validated on an extra data set that would have been
gathered throughout experimentation, but not used for the development
of the algorithms. It has been reported that these algorithms should have
a sensitivity of at least 85% and a specificity of at least 89% for the
system to be commercially adoptable (Petrie and Watson, 2006; Oczak
et al., 2013; Oczak et al., 2014; Viazzi et al., 2014; Berckmans 2014a).
Evidently, the pigs themselves would unconsciously control the
parameter of interest. Systems as such can help the producers in iden
tifying groups of pigs that need special attention (Dominiak et al., 2019).
Although this is a very complicated task, efforts are made towards in
dividual monitoring and behavioural analysis. For example, the Emoti
Pig that intends to analyse pigs’ individual facial expressions and
evaluate their welfare status (Baxter et al., 2019). In addition, Hansen
et al. (2018) achieved a 96.7% accuracy success rate for individual facial
recognition for their CNN model. It should be noted that the CITD nature
of the living organisms is greatly affecting the algorithms used to mea
sure individual bio-responses and it is necessary to be constantly
adapted (Berckmans, 2017). It is evident that animal ethology and
behavioural analysis is the pool of information and data for PLF algo
rithms and applications thus, PLF scientific basis is determined as a
combination of animal behaviour science along with advancements in
computer science. The data may be collected by cameras (CCTV,
infrared, thermal, etc.) and real-time analyses systems, by microphones
and sound analyses systems, or by any other sensor within the produc
tion unit or on the animal such as accelerometers, RFID sensors, etc. In
this paper various published papers referring to PLF systems are dis
cussed considering the assessment of the core definition of PLF (i.e.,
individual pen identification and comfort assessment).
3. Camera-based monitoring
Over the previous three decades, computer vision approaches have
been the main area of focus and analysis in PLF and have been used in
multiple livestock behaviour analysis and classification related appli
cations. McGlone (1986) stated that camera monitoring is generally
used for detailed observations of behavioural responses’ characteristics
such as frequency of appearance, duration, speed, acceleration, etc.
However, only full-time recording and analysing can provide the “whole
picture” for behavioural analysis, since focal sampling could result in
great standard errors that could lead to inaccurate results. The recorded
behaviour can be analysed in (a) slow-motion (Stygar et al., 2017) or
frame-by-frame (Oczak et al., 2013) for analysing individual
bio-responses such as single fights and behavioural patterns or behav
ioural interactions such as bites (average duration 0.5s) (McGlone,
1986), or (b) a faster speed for general activity analysis such as that of
classifying the standing and lying behaviour of the animals (Nir et al.,
2018; Nasirahmadi et al., 2019b). By performing the above analyses,
researchers can save valuable time and effort without compromising the
accuracy of the results (McGlone, 1986). It should be noted that few
cameras above the pigpen could provide a full field of view (FoV)
depending on pen size and camera specifications (i.e., zoom lens and
image stabilisation, focus analysis, frames per second, etc.) are efficient
enough for all image data collection needed and at the same time the
total installation costs of the systems are minimised (Berckmans,
2014a).
A PLF system can potentially trigger bio-responses to prevent certain
behaviours of the animals (Berckmans, 2014a). For example, real-time
monitoring systems have been already applied for investigating and
controlling the feeding behaviour and weight estimation of
growing-finishing pigs (Kashiha et al., 2014a; Stygar et al., 2017; Nir
et al., 2018), monitoring the drinking behaviour (Kashiha et al., 2013a)
automatic detection and counting the numbers of pigs (Tian et al., 2019)
and possible prevention of aggressive interactions by distracting the pigs
with candies (Ismayilova et al., 2013). (Kashiha et al., 2013b), suc
cessfully monitored and identified the resting behaviour of 10 individual
pigs, with a success rate of 88.7 % and (Kashiha et al., 2014b), developed
an algorithm to detect pigs’ locomotion using image analysis, with an
accuracy of 89.8%. Nilsson et al. (2015) developed a model for thermal
dis-comfort detection and automatic micro-climate control management
at pen-level using a simple camera installed above the pen, based on
pigs’ lying positions within the pen. In addition, they reported that the
system could potentially be used to detect health issues and diseases
with some modifications. Nasirahmadi et al. (2016) used a
camera-based system to track pigs’ movement, at an average liveweight
of 30 kg, monitored for 20 consecutive days. They successfully classified
the mounting behaviours with a sensitivity of 94.5%, specificity of
88.6% and an accuracy of 92.7%. Chen et al. (2020) developed a
camera-based method for automatic detection of aggressive behaviours
by improving an already existing system’s accuracy from 97.2% to
98.4% by reducing the fps of aggressive episodes. However, it should be
noted that the limited datasets used during the behavioural analysis (i.
e., usually one or two pens, pigs of a certain hybrid, housed in a single
environment, similar age and weight, etc.) and the low recording
duration (i.e., 8 hours per day or less, less than ten days per recording
session) make the developed systems highly uncertain and too limited
for commercial applications, as the real pig housing parameters vary
from farm to farm.
3.1. Movement tracking
Various camera-based systems have been introduced the recent years
and as technology evolves, more of them are introduced with improved
results in monitoring and detecting specific parameters of interest. PLF
researchers and system manufacturers should bear in mind the internal
characteristics of cameras such as resolution quality, effective distance
from the lens, focal length, lens type (e.g., fisheye, etc.) to determine the
number that needs to be installed. This is an essential during the
experimental design as it greatly effects the economy of the experiment
and the data analysis process (i.e., video editing and processing).
The most common problem in today’s camera-based tracking sys
tems is “blob merging” (Viazzi et al., 2014; Cowton et al., 2019). This
may be caused due to illumination or environmental changes, such as
vapour condensation or dust accumulation on the camera lens during
the day, similar body appearances and shape deformations, partial oc
clusion and overlapping interfering with the video’s clarity and
increasing the noise of the recordings (Lee et al., 2016; Gangsei and
Kongsro, 2016; Guo et al., 2017; Zhang et al., 2018; Brünger et al., 2018;
Sa et al., 2019). Іn the studies of McFarlane and Schofield (1995) and
Viazzi et al. (2014), when the pigs got too close to each other, and
especially when they slept in piles (i.e., one on top of the other), the
tracking software merged the individual blobs misinterpreting two

4
individuals as one and eventually, it could not identify which pig is
which. Furthermore, Viazzi et al. (2014) software lacked the complete
detection of aggressive behaviour as it showed a value of 89% for ac
curacy, 88.7% for sensitivity and 89.3% for specificity. McFarlane and
Schofield (1995) recommended an increase of the image-capture rate of
the camera to 20 frames per second (fps) with the intention to minimise
this problem. Lee et al. (2016) developed a tool based on the Kinect
depth sensor and used it to track and classify aggressive behaviours
within the pig pen, but they also faced the same problems as Viazzi et al.
(2014). The average classification accuracy was 90.2% and the average
precision and recall were 90.2% and 90.1%, respectively. Even though
the software developed by Lee et al. (2016) showed improved precision,
accuracy, and recall, compared with previous ones, the problem of
real-time monitoring and complete automatic aggression detection is yet
to overcome. Figure 1.I. demonstrates how such a system detects pigs’
movement and Figure 1. II. illustrates the blob merging misinterpreta
tion problem.
PLF data presentation varies from system to system in terms of fre
quency, duration and formats, however farmers rarely have the
knowledge to combine and analyse the information (Van Hertem et al.,
2017; Hartung et al., 2017). PLF manufacturers should simplify their
data presentation models and provide short-time educational seminars
for the farmers to enhance their ability to use this information.
In recent years, Convolutional Neural Network (CNN) approaches
were introduced to assess blob merging problems. Zhang et al. (2018)
proposed a method for monitoring pigs’ movement automatically that
included three stages of analysis: (a) object detection, (b) multiple ob
jects tracking (MOT) and (c) data association. In the first component, the
main purpose of the detector is to extract the foreground features in
order to minimise the illumination fluctuations and strengthen the
background subtraction process. The object detection is a critical pro
cess facilitating high-level tasks such as detecting and analyzing specific
behavioural patterns. CNNs were used for this process as they
demonstrate superior performances compared with other methods. MOT
is the process of understanding all the objects, their shapes, and their
place at the scene of focus. The final process, namely data association,
was used to treat the possible false positives or negatives of the two
previous processes and therefore it was used as a backup security safe
mechanism that would enhance and enforce movement tracking re
covery. This method managed to almost diminish the blob merging
problem even when the pigs got very close or even on top of each other.
Their method achieved percentages of 94.72%, 94.74% and 89.58% for
precision, recall and MOT accuracy, respectively. Furthermore, with the
intention to minimise the blob merging and achieve continuous indi
vidual monitoring, Seo et al. (2019) and Lee et al. (2019) developed a
model based on combination of infrared and depth informative sensors
and a fast CNN object detection technique named YOLO (i.e., ‘You Only
Look Once’) achieving accuracy of 83.33%. It should be noted that the
proposed system’s specifications were rather costly thus, minimising the
application’s possibilities. However, further research is needed due to
the limited data (i.e., herd size and number of pens) that the systems
were based on.
Xiao et al. (2019) developed another tool to track pigs’ movement
housed under commercial environmental conditions including no-light,
sudden illumination changes, adhesion, and occlusion scenes. Their tool
also lacked in accuracy (i.e., 87.32%). However, they reported that it
was able to constantly track pigs’ movement providing valuable data on
their activity that it is strongly related with growth performance and
health status. It should be noted that the stocking density was no more
than 4 pigs/pen and thus, further research is needed under various
managerial and housing conditions (i.e., more than 10 pigs/pen,
different environmental conditions etc.). In addition, Li et al. (2019)
developed a novel model that automatically tracks pigs under com
mercial housing conditions. The model’s core algorithm was based on
analyzing the captured data as the combination of two datasets,
comprising a) dominant orientation templates and b) brightness ratio
Fig. 1. I. Automatic movement tracking system. II. Blob merging in today’s camera-based systems. (Author’s own).

5
templates. This system achieved an average detection rate of 86.8%. The
system overcomes many of the problems of this area such as illumination
changes, varying colour, pigs’ slow movement etc. However, the small
sample size of the dataset (i.e., 2 pens of 4 pigs each) limits the potential
of future application development thus, further research is needed ac
counting for commercial parameters such as group size, pen size, etc.
A different approach in tracking pigs was introduced by Shi et al.
(2019). They developed a camera-based system on LabVIEW that ac
quires real-time data for pigs’ body components measurement such as
body length, body width, body height, hip width and hip height. When
the system was validated against manual measures it was found that a
linear relationship existed with R2
> 0.82 and average deviation of
2.83%. Therefore, this system was able to measure pigs’ growth just by
using a camera in large scale farms. It should be noted that the LabVIEW
environment used in this study is a different approach than that applied
in the majority of previous papers that mostly use the MATLAB platform
and thus, provides researchers with an alternative method in system
programming. Unfortunately, it is based on the mean average of the
overall farm population thus, cannot detect issues at pen level. At the
same time, the use of a relative free software could make this system
more financially viable. Furthermore, Hansen et al. (2018) proposed a
system that classifies pigs’ face characteristics to identify individual
animals based on human face recognition techniques found in the
literature. They reported that this system could be used instead of the
commercial ear tags as it is non-invasive, less time consuming and if
modified it could potentially (a) be used in other animals such as in the
dairy cattle or poultry and (b) be a solution for the camera-based
continuous individual tracking problem (i.e., blob merging). However,
it needs to be tested under commercial conditions.
Shao and Xin (2008) developed a real-time camera-based system for
tracking pigs at rest (i.e., as indicator of thermal comfort) and classify
their thermal bio-responses in cold, comfortable, and/or warm/hot
conditions. This prototype tool achieved an over 90% image classifica
tion rate based on the animals grouping behaviours (i.e., image move
ment variants, run-length ratio, and pig group compactness) as thermal
comfort indicators. They suggested that colour images may provide
more information concerning the animals’ thermal state. Advanced al
gorithms coupled with thermal image analysis must be developed to
achieve faster system response and more accurate detection of pigs’
thermal state and bio-response classification.
(Nasirahmadi et al., 2019b) developed an algorithm/model that
detects and classifies the lateral and sternal lying posture of pigs under
commercial conditions using two-dimensional images. The experi
mental data was collected from 4 pens within a chamber from each of 2
commercial farms, in Germany and 2 chambers with 2 pens each, in
Sweden. Housing temperatures varied from chamber to chamber and for
each farm so that the system could fit into a bigger range of different
commercial conditions. During the image analysis (i.e., background
subtraction method) the binary image properties (i.e., area, perimeter,
and convex hull) of each pig were used as input to train a linear Support
Vector Machine (SVM) classifier. This system showed accuracy and
classification of 94.4% and 94%, respectively. It is a different, more
commercial approach than the previous camera-based systems as it was
developed to work under different housing conditions. Nevertheless, the
system’s core data input is collected from a camera per pen – hence
maximises the total cost and therefore, further research is needed for a
robust commercial application.
Chen et al. (2019) developed a model to automatically detect
aggressive behaviours based on data collected using a camera depth
sensor. This algorithm achieved accuracy of 97.5%, sensitivity of 98.2%,
specificity of 96.7% and precision of 96.8% and could be used for
aggressive behaviour detection. However, the limited data (i.e., two
groups of 8 pigs each), the duration of the recordings (i.e., 3 days for 8
hours for each) and the fact that the pigs were housed in research fa
cilities with totally controlled environment are in contrast with com
mercial conditions and everyday problems. In addition, as in previous
research in tracking pigs’ movement, it lacks in complete detection,
hence false alarms could be a major problem if the system is used under
commercial conditions. It should be noted that systems as such (i.e.,
aggression detection) are still under development and thus, applications
for methods to be followed to successfully address this issue have yet to
be defined.
In addition, (Lee et al., 2019) developed a camera-based system
using an IoT embedded device to detect individual undergrown pigs.
They proposed an image processing method that uses a deep learning
technique with minimum computational overhead, to minimise the
image processing time and increase the accuracy of the system. This
method may lead to a solution for the automated real-time monitoring
bump of PLF systems and help application development as it does not
only minimise the costs of the system but at the same time, it effectively
identifies individual pigs in a real-time sequence. However, further
research is needed under various environmental and housing conditions
and different pig hybrids for the development of a commercial appli
cation. Table 1 summarises various studies conducted with regards to
tracking individual pigs using camera-based image analysis.
3.2. Weight estimation
Weight measuring is probably the most economically important
aspect of pig production, since it is related with feed efficiency and
nutrition management. However, it is generally a time consuming and
intense process that negatively affects pigs’ wellbeing as not only it is
stressful but also increases the possibility of injuries for both the workers
and the pigs. PLF technological advancements in this area have shown
great potential in achieving weight estimation without interfering with
the pigs and at the same time dropping an intensive workload for either
the farmers or labor or both.
Wang et al. (2008) and Banhazi et al. (2011) developed models based
on images captured from above the pen, while the pigs were passing
through experimental corridors one at a time and showed only 3% and
2.1% relative error, respectively. Therefore, these systems could esti
mate the live weight of pigs, with no restrains or lockdowns that com
mon weighing methods use, with great accuracy. However, since the
pigs had to be individually removed from the pen, they were housed into
the experimental corridor one at a time, problems remain even at lower
levels due to induced stress for pigs and the increased workload for
workers during the weighing period.
Table 1
Camera-based analysis for individual classification of pigs.
PLF systems Area of focus Reference
Camera-based
image data
analysis
Movement Tracking Ahrendt et al. (2011)
Kashiha et al., 2013a; Kashiha et al.,
2013b; Kashiha et al., 2014a; (Kashiha
et al., 2014b) )
Nasirahmadi et al. (2016)
Gangsei and Kongsro (2016)
Kim et al. (2017)
Guo et al. (2017)
Brünger et al. (2018)
Jun et al. (2018)
Zhang et al. (2018)
Chen et al. (2019)
Xiao et al. (2019)
Individual
detection/
monitoring
McFarlane and Schofield (1995)
Nilsson et al. (2015)
Lu et al. (2018)
Psota et al. (2019)
Sa et al. (2019)
Lee et al. (2019)(Lee et al., 2019)
Li et al. (2019)
Lying behaviours
detection
(Nasirahmadi et al., 2019b)
Shao and Xin (2008)
Facial features
identification
Hansen et al. (2018)

6
A different approach in the automatic weighing of pigs was reported
by Jun et al. (2018). They developed a model for weight estimation of
pigs between 70-120 kg, based on 2D image analysis. This system ach
ieved an average estimation error of 3.15 kg (R2
= 0.79). In addition,
Pezzuolo et al. (2018) developed a system that automatically estimates
pigs’ weight based on Microsoft Kinect V1 depth sensor (i.e., the same as
in Lee et al. 2016) achieving a R2
> 0.95. These systems would not
interfere with pigs’ everyday lives, as the data is collected from cameras
placed above the pen, making them less intervening compared with the
Wang et al. (2008) method. However, Jun et al. (2018) had a small
model database (i.e., 477 images used for training the algorithm and 103
images used for testing the model) and the images collected were in
pristine clarity, not as in commercial conditions. Furthermore, in the
Pezzuolo et al. (2018) study there was also a small sample size (i.e., 4
pens), limited collected data (i.e., 2 hours per day for the weaning
period) and only a single farm and breed on-farm testing was under
taken. It is of vital importance for a system that classifies animal pa
rameters, to use various datasets and be tested under different housing
conditions and management practices for the development of a com
mercial application. Furthermore, the existence of service groups placed
close to the farm is essential for the proper application and system
operation. Finally, an in-depth economic analysis is necessary as the
particular sensor can be fairly expensive, increasing the installation
costs and making it unprofitable and unattractive for purchase (Berck
mans, 2017).
In the same area as the above (i.e., Jun et al., 2018; Pezzuolo et al.,
2018), Lu et al. (2018) developed a system to estimate in real-time live
weight and carcass traits of pigs from images caught from above the pen.
They achieved accuracy of 97.06% for length and 97.06% for area pa
rameters indicating that this system could be used for body surface pa
rameters extraction. This system could be combined with other early
detection systems such as sound analysis, or RFID sensors in a main
control management system that could potentially improve health due
to disease spreading prevention, welfare, and production of today’s
units.
Psota et al. (2019) developed a method for a camera-based system
that detects several body parts of the pigs (i.e., both ears, the back right
between the shoulders and the tail) in different pen locations. The
proposed method scored over 99% for precision and over 96% for recall.
It should be noted that although this method achieved improved scores
for both precision and recall compared with (Lee et al., 2019) system,
the images used to create the dataset were not a continuous timeline but
fixed images of a total number of 2000. Thus, it was not a system that
tracks pigs’ movement but rather detects their body parts from random
samples of images during the video recording timeline. In addition, the
simple HD camera that was used could record at a frame rate of no more
than 25 fps thus, the method was based on a dataset of just 110s which is
considerably small. However, this is the first effort of developing an
open-source dataset in pig detection in intensive environments that in
cludes a wide range of pigs’ body poses.
3.3. Thermal analysis
Pigs tend to change their postural behaviour by increasing or
decreasing heat loss to achieve the minimum deviation from their
thermal comfort zone (Xin, 1999; Ye and Xin, 2000; Xin and Shao,
2002), which is defined as the effective environmental temperature
within which pigs remain productive up to or nearly up to their genetic
potential performance and experience optimal health status (Baker,
2004; Mitchell, 2006; Andersen et al., 2008). Pigs are very sensitive and
vulnerable to extreme variations of temperature and relative humidity
(Huynh, 2005), as they do not pant effectively and their sweating rate is
very low, namely 30g/m2
h (Ingram, 1965). Continuous exposure to
such climate discomfort leads to reduced feed efficiency and growth
performance (Renaudeau et al., 2012), increased aggression levels and
frequency of belly nosing, ear and tail biting (Geers et al., 1989), and
intense stress conditions (Morgan and Tromborg, 2007) , but also in
duces huddling behaviour (Hillmann et al., 2004a), increased vocali
zation (Hillmann et al., 2004b) and several hierarchy issues within the
pig pen (Edwards, 2008). A continuous monitoring tool that analyses
thermal bio-responses or on-body thermal changes and triggers an alarm
when critical values are exceeded, will aid greatly the farmer, instead of
the common techniques such as measuring pigs’ body temperature
which are mostly conducted manually (Pessoa et al., 2021). The process
that each system follows to achieve this varies depending on the systems
characteristics. For example, a common camera-based system will
collect, process, and present different datasets (e.g., movement analysis
system; Shao and Xin, 2008) compared with a sound analysis system (e.
g., cough analysis system; Wang et al., 2019). However, real-time
continuous monitoring in individual pigs is yet to be achieved.
Brown-Brandl et al. (2013) reported that thermal image analysis can be
used to evaluate pigs’ thermal needs assisting them in achieving thermal
comfort. Thermal camera-based PLF tools integrating thermal (dis)
comfort behavioural responses within the existing climate control sys
tems can not only improve the welfare and productivity of pigs but also
optimise the use of energy/feed resources, leading to and improvement
of the economic status of pig barns (Berckmans, 2014a). Research con
ducted using thermal camera-based image and infrared sensors analysis
is depicted in Table 2.
Under commercial conditions, the most common method for
measuring pigs’ core body temperature, is the measurement of rectal
temperature (Dewulf et al., 2003). However, it is a stress and labor
intense method for both pigs (Godyn and Herbu, 2017) and workers,
negatively affecting the economy of the unit by decreasing the welfare
and at the same time increasing labor costs (Zhang et al., 2019). The
recent decades research in this area is focused on acquiring reliable core
body temperature measurements without interfering with pigs’
everyday lives. One of the most economically viable and effective - if not
the best - methods proposed is the use of infrared technology due to its
high temperature accuracy, systems’ stability and simplicity, and
real-time remote operation (Zhang et al., 2019).
Thermal camera equipment has been used in various areas of pig
production including disease detection based on body temperature
analysis (Islam et al., 2015), early disease diagnosis and effective health
and welfare monitoring (Zhang et al., 2019), as body temperature
measurement tools instead of rectal thermometers (Schmidt et al.,
2013), inflammation and lesions detection (Ruminski et al., 2007), real
estimation of body temperature in groups of pigs (Xin, 1999; Xin and
Shao, 2002; Warriss et al., 2006), welfare degradation factors such as gas
and dust concentrations as result of social activity (Ni et al., 2017), and
Table 2
Thermal camera-based and infrared sensors analysis in pigs.
Thermal camera-based
image data analysis
Estimation of body
temperature
Xin (1999)
Xin and Shao (2002)
Warriss et al. (2006)
Schmidt et al. (2013)
Jiao et al. (2016)
Monitoring lying
behavioural patterns
Huynh et al., 2005
Thermal comfort
assessment
Shao and Xin (2008)
Brown-Brandl et al.
(2013)
Early disease diagnosis Zhang et al. (2019)
Health and welfare
monitoring
Zhang et al. (2019)
Monitoring agonistic
behaviours
Boileau et al. (2019)
Inflammation and lesions
detection
Ruminski et al. (2007)
Infrared motion sensors Health monitoring Soerensen and
Pedersen (2015)
Movement tracking Ni et al. (2017)

7
responses in high temperature housing conditions based on lying
behavioural changes (i.e. huddling and wallowing; Huynh et al., 2005).
Boileau et al. (2019) reported that thermal image analysis can be used in
detecting the intensity and various phases of agonistic behaviours (i.e.,
fighting, mounting) leading the research of this area in a new path.
However, they based their assumptions on 1284 thermal images and
further research should be undertaken using real-time analysis of video
data captured from a thermal camera. Ramirez et al. (2018) successfully
developed a system to achieve pigs’ thermal comfort conditions in the
pig barn. However, they did not include real-time behavioural data to
achieve thermal comfort for the pigs but used global data criteria from a
mechanistic thermal balance model which scales pigs’ thermal comfort
in a scale of 0 to 10 based on environmental conditions, in a simulation.
As thermal comfort zone depends on many unpredictable factors such as
feeding behaviour, individual temperament, aggression levels within
the pen, hierarchy, etc., continuous animal behaviour analysis should be
part of future management tools targeting optimal welfare. A
computer-based system should be able to accurately analyse pig
bio-responses such as feeding and drinking behaviour, or any other
behaviour or behavioural characteristic that is under research, or of
interest, in real-time and use them as input for optimal environmental
control. For this process, Jiao et al. (2016) reported that the angle of
view of the camera is of great importance in temperature measurement
errors. It should be noted that usually there is inadequate equipment in
field research and limited knowledge of its functions (Soerensen and
Pedersen, 2015). However, this technology seems very promising and
research results suggest that soon IRME technology could be in the
centre of PLF research and commercial on-farm applications.
Soerensen and Pedersen (2015) reported that Infrared Measurement
Equipment (IRME) is a useful tool for efficient automatic detection of
pigs’ movement and evaluation of their health status. In agreement with
them, Ni et al. (2017) used IRME motion sensors to study the effect of
pigs’ activity on gas and dust concentration within the housing chamber.
This technology seems very promising and research results suggest that
in the near future IRME technology could be in the centre of PLF
research and commercial on-farm applications. It should be noted that
both studies suggest that the IRME systems should be used in
state-of-the-art environmental condition control chambers to assure the
accuracy of the measurements.
The aforementioned studies have shown great potential in solving
pig health and welfare-related problems and at the same time contrib
uting to an advanced time management by the pig farmer within the day
(Oczak et al., 2013; Oczak et al., 2014; Berckmans, 2014a). It is worth
noting compared with Tscharke and Banhazi (2016) review, that today’s
camera-based systems have better technical specification and are more
economically viable. However, further research is needed towards
minising the frequency of false alarms triggered by low values of PLF
systems’ characteristics such as accuracy, sensitivity, specificity, and
precision.
4. Sound surveillance analysis
Vocalisations and screams are behavioural expressions among pigs
that convey information about their current health and welfare status
(Hillmann et al., 2004b; Vandermeulen et al., 2015). This information
can be of extreme help for the farmer in early problem detection and
disease outbreaks or aggressiveness escalation prevention.
It is known that pigs have a strong tendency for coordination and
synchronization of behaviour in space and time and even one bark alarm
by a single pig may make the whole group or even all the groups in the
chamber to freeze and attend towards the sound source (Talling et al.,
1998; (Marchant-Forde et al., 2001) ; Špinka, 2009). Therefore, the
technology used for monitoring pigs, should not interfere with their lives
as it is known that even a muted sound could be the reason of inaccur
acies in the experimental results (Berckmans, 2014a). During the ex
periments of Aerts et al. (2005) the pigs got used to the labelling sound
of the system and started coughing voluntarily to listen to this particular
sound, leading to experimental errors. Wegner et al. (2019) reported
that age, sex, floor type (e.g., fully slatted or partly slatted), feed
quantity and quality, feed type (e.g., liquid or dry feed), ventilation
systems and the use of bedding materials provide different datasets
implying that each housing system is unique and should be treated
exclusively. Thus, any commercial application should include these
management and housing variables as setpoints individually filled by
the producer, depending on the unit. Table 3 indicates research con
ducted using sound sensors analysis.
Marx et al. (2003) and Diana et al. (2019) reported that the analysis
of audio components of certain vocal behaviours (i.e., duration, mean
frequency, 10th
percentile frequency, mean spectral spread and 10th
percentile spectral flux) can be classified as bio-responses to pain and are
directly linked to bitten or biter pigs. Spensley et al. (1995) and Talling
et al. (1996) stated that external sounds can activate pigs’ defense
mechanisms depending on the sound’s properties such as nominal in
tensity and frequency. For example, (Li et al., 2019) reported that pigs
showed different bio-responses in various types of music preferring
mostly either music played by string instruments at a slow tempo (i.e.,
65 beats per minute) or from wind instruments at a fast tempo (i.e., 200
bpm). Von Borell et al. (2009) developed a tool based on the classifi
cation of three different classes of piglet vocalisations (i.e., grunting,
squealing and screaming). They found that vocalisation analysis in pigs
can help identify both pain and behavioural changes indicating that a
system that combines both camera-based and audio analysis system
could potentially improve contemporary PLF systems. Therefore, a PLF
tool that analyses pigs’ vocal behaviours and automatically detects pain
could potentially be built, providing the farmer with an early warning
signal and/or helping for an early solution, prior to the conflict
escalation.
A reference set of data is a necessity for the development an auto
matic scream classifier (Vandermeulen et al., 2015; Hemeryck and
Berckmans, 2015) that can be built with audio labelling captured data
by a single observer (Guarino et al., 2008; Chung et al., 2013), the same
method as in camera-based systems. Schön et al. (2004) developed an
Table 3
Sound sensors surveillance and sound analysis in pigs.
Initiating varies
sounds
Triggering bio-responses Spensley et al. (1995)
Talling et al. (1996)
(Li et al., 2019)
Sound data
analysis
Cough and screams monitoring/
analysis and diseases detection
Van Hirtum and
Berckmans (2002)
(Hillmann et al., 2004b)
Aerts et al. (2005)
Ferrari et al. (2008)
(Exadaktylos et al.,
2008)
Guarino et al. (2008)
Silva et al. (2009)
Chung et al. (2013)
Vandermeulen et al.
(2015)
Hemeryck and
Berckmans (2015)
Zhang et al. (2019)
Feeding control Manteuffel (2009)
Idetnification of age, sex and stress Cordeiro et al. (2018)
Air quality Wang et al. (2019)
Stress detection Schön et al. (2004)
Von Borell et al. (2009)
Vandermeulen et al.
(2015)
Cordeiro et al. (2018)
Da Silva et al. (2019)
Pain detection Marx et al. (2003)
Von Borell et al. (2009)
Diana et al. (2019)

8
Artificial Neural Network tool, the STREMODO, for automatic stress
detection based only on vocalisation analysis. During the development
process of the tool, they found that the duration and intensity of screams
are better stress indicators than the actual number of screams. STRE
MODO was tested under commercial conditions and the results were
compared against six human experts’ audio observations and the cortisol
levels in pigs’ blood, achieving more than 99% accuracy and more than
95% precision. Vandermeulen et al. (2015) developed an automatic
classifier for pig screams definition and subtraction from other pro
duction process sounds. An event was labelled as a scream if its duration
was longer than 0.4s. The thresholds that determined a scream’s char
acteristics (i.e., duration, low or high sound power, frequency, specific
formant structure and variation between screams) were combined into a
simple voting system, where each feature had one vote to determine if a
certain sound was a scream or not. Thus, this classifier was based on
votes making it adaptive. The developed tool achieved a 71.83%
sensitivity, 91.43% specificity and an 83.61% precision. The classifier
was then compared with STREMODO in terms of specificity, sensitivity,
and precision. It was found that STREMODO demonstrated improved
results in sensitivity and specificity, while for calculating the correlation
between the two systems and a labeler for ten minutes, both systems
achieved comparable results comprising a correlation of 0.80 (P<0.001)
for the Vandermeulen et al. (2015) method and a 0.84 (P < 0.001) for
STREMODO.
Cough sound analysis can assist not only in monitoring pigs’ health
status but also in the early detection of various respiratory diseases (Van
Hirtum and Berckmans, 2002; Aerts et al., 2005; Silva et al., 2009).
Wang et al. (2019) developed a model that automatically analyses pigs’
cough sounds and provides information of air quality (i.e., temperature,
humidity, ammonia concentration and dust concentration) at room
level, with a 95% average recognition percentage. This system was
developed based on data collected from weaners of certain pig barns and
breeds, therefore it needs to be tested under different managerial
methods and breeds in order to be commercially applicable. Ferrari
et al. (2008), developed an automatic tool that detects and classifies
cough and screams related to pulmonary diseases. They reported that
there was a significant difference between the Root Mean Square of
healthy pigs (0.215) and that infected with disease (0.124) (P = 0).
Moreover, significant differences between the average peak frequency
and length of coughs were found (P < 0.001), namely non-infectious
coughs had an average peak frequency of 1600 Hz and an average
length of 0.43s compared to infectious coughs that were recorded at
600Hz and 0.67s. Chung et al. (2013) achieved a 94% accuracy and a
91% sensitivity with their automatic cough and screams detection tool.
It was noted that even a low-cost microphone can be used to collect the
data further reducing the system costs, however the data analysis of the
developed system was based on certain breed and housing conditions (i.
e., data collected from a single farm), a small sample size (i.e., 36 pigs)
and recording duration (i.e., 30 minutes). Further research is needed to
assess these variables and make the system more commercially attrac
tive for the farmers.
Cordeiro et al. (2018) classified the screams of 40 pigs (i.e., 20 males
and 20 females) for a full production cycle. The intensity differed
significantly between males and females (i.e., 194 Hz and 218.2 Hz,
respectively) and among the stages of the production cycle. The same
authors developed a tool that automatically classifies the age, sex and
stressful events with a precision of 81% based on voice recognition
exclusively. Da Silva et al. (2019) documented the audio data of 40
piglets for 52 days exposed to stressful conditions such as thermal
dis-comfort, hunger, thirst, and pain. A tool was developed based on
paraconsistent logic Eτ for calculating the accuracy using the amount of
true and false predictions. This tool showed high levels of accuracy for
automatically identifying pain (i.e., 98%), but when applied for hunger
or thermal discomfort conditions identification it was considerably
lower achieving only 69% and 71%, respectively. More experimentation
is needed for both studies under different commercial housing
conditions and hybrids to improve the PLF evaluation parameters and
minimise false alarms.
All the previous studies provide evidence that utilization of certain
pigs’ behaviour elements through sound analysis is feasible and it will be
an essential part of future PLF systems. However, these studies are based
on limited data and the developed applications are not used widely due
to high uncertainty that leads to great number of false alarms.
5. Communication Information Technology (CIT) sensors –
combined systems
Various sensors have been used to monitor various parameters of
interest within a pig barn such as radio-frequency identification (RFID)
chips (Adrion et al., 2017), and depth sensors (Kim et al., 2017) along
with more complex systems including neural networks analysis, elec
tronic feeders, and drinkers (Berckmans, 2015). Research in this area
usually refers in a combination of sensors and systems and most research
have been conducted using sensors installed on electronic feeders. The
series of related studies and research conducted in this area is presented
in Table 4.
5.1. Electronic feeders and drinkers combined with various systems
Several breeding companies around the globe use electronic feeders
to test feeding behaviour, growth and performance of their breeds as
data collected from these systems tend to be very accurate and have
great potential in monitoring pigs’ social behaviour when combined
with other sensors (Hoy et al., 2012). In addition, many commercial
units have installed such systems for a variety of purposes like health
disorder detection, feed intake measurements and feed control along
with meat quality estimation (Wallenbeck and Keeling, 2013).
Manteuffel (2009) developed a system for active feeding control in
pigs (i.e., groups of 8 pigs, at 7 weeks old). The pigs were trained to
respond to individual jingles. Call Feeding Stations (CFS) installed in
each pen, produced a jingle sound and if the appropriate animal entered
the feeder, feed was provided. More than 50% of the pigs positively
reacted within the first day of the experiment and by the 4th
day this was
improved to more than 80%. The experimental groups showed improved
meat quality (comprising, 0.2% less intramuscular fat, 1.1% less drip
loss and 7% more oxidative muscle fibres), lower frequency of appear
ance of abnormal behaviours (i.e., belly nosing and healing of skin le
sions) compared with the control groups, while growth performance was
not affected. In addition, this system generally minimised the competi
tive fighting, improved health and welfare and reduced stress levels.
Table 4
Various equipment used for data analysis in pigs.
UHF-RFID data
analysis
Individual identification Maselyne et al.
(2014)
Feeding behaviour Manteuffel (2009)
Wallenbeck and
Keeling (2013)
Maselyne et al.
(2016; 2018)
Adrion et al. (2017;
2018)
Revilla et al. (2019)
Drinking behaviour Maselyne et al.
(2015)
Oestrus, Lameness and health
disorders detection
Cornou et al. (2008)
Accelerometers data
analysis
Reproductive and respiratory
disease syndrome virus detection
Süli et al. (2017)
Sows’ nesting behaviour duration
estimation
Oczak et al. (2019)
Environmental data
analysis
Tail-biting, diarrhea and fouling
detection
Domun et al. (2019)

9
Wallenbeck and Keeling (2013) developed a model that predicts future
tail biting victims 9 weeks before the first visual tail injuries, by ana
lysing their feeding behaviours (i.e., daily frequency of feeder visits and
daily feed consumption) from data collected from electronic feeders.
However, in both studies the collected data was focused on a single farm
and thus, further research is needed for the development of accurate
commercial applications.
Revilla et al. (2019) monitored 325 piglets for 75 days during
post-weaning (i.e., initial age of 28 days). They developed a dynamic
model that estimates the individual live weight trajectory based on data
collected from electronic feeders and provides information on individual
amplitude and length of perturbation caused by weaning, and the dy
namic of animal recovery. The developed algorithm showed an average
coefficient of determination (R2
) of 0.99 and concordance correlation
coefficient of 0.99 for perturbation versus weight dynamics of individual
animals. This model could be used to develop a PLF application that will
provide the farmer information on each pig’s weight dynamic traits
hence, which pig requires special feeding treatment. However, hybrid,
housing conditions and management practices should be available as
selection buttons for the producers to insert.
Maselyne et al. (2016) used High Frequency RFID tags on each ear of
two groups of pigs (59 pigs each; 9-10 weeks of age) that were monitored
until slaughter (i.e.,110-120kg). They developed an algorithm that de
tects pigs’ individual visits at the feeder. The proposed method achieved
an average sensitivity of 83%, 98% for specificity, 97% for accuracy and
a 75% precision for the first group and an 80% sensitivity, 99% speci
ficity, 98 % accuracy and 78% precision for the second group. The main
question asked by the producers is the units’ gain from this method,
which has been proven very accurate. Therefore, further research is
necessary as to find the appropriate way of introducing management
interventions based on this information with the intention to improve
the production process. Maselyne et al. (2018) used the same method as
described and developed a system that detects the feeding patterns of
individual pigs by analyzing their variables such as the number of trough
visits, the duration, and the average interval between the visits. This
system achieved a sensitivity of 58%, 98.7% for specificity, 96.7% for
accuracy and 71.1% for precision. They also stated that it requires
further development to improve its practicality. It should be noted that
the RFID ear tags used in the industry need extra time from the farmer
for data collection, have limited range for efficient measurement (i.e., a
maximum of 120 cm) and at the same time may negatively affect the
welfare working as an extra stressor for the pigs (Maselyne et al., 2014).
A combination of the RFID tag methods (Jun et al. (2018); Lu et al.,
2018; Pezzuolo et al., 2018), could possibly provide indication for feed
conversion estimation in growing-fattening pigs.
Cross et al. (2018) developed a model based on finishing pigs’
feeding behaviour, feed-forward and generalised regression neural
networks modelling to achieve automated disease detection, like
pneumonia outbreaks, at an early stage. However, the r2
was not greater
than 0.67 and further research is needed to improve the models’ PLF
evaluation parameters. Eissen et al. (1998) suggested that frequent
checking and correction of feeding stations function for certain
recording periods and frequent maintenance, may improve the system’s
viability by reducing the errors occurred when a feeding station is not
functioning optimally.
Data collected from the electronic sow feeders can also be used in
oestrus, lameness, and health disorders detection (Cornou et al., 2008).
This model showed specificity higher than 93% for all three tested pa
rameters (P<0.003). Sensitivity was not higher than 75% and was re
ported as satisfying compared with other commercial methods.
However, this method was associated with a high number of false alarms
and further research is needed for more concrete results. In addition,
Scheel et al. (2015), proposed a method to automatically detect lame
ness of 14 sows at early stages using accelerometers placed in ear tags.
They measured general activity such as daily variance, average variation
and average squared variation and compared individual acceleration
data between consecutive days. They reported that they are working
towards the development of an on-farm application with an early
warning system based on the proposed method. It is suggested that the
system should be tested in different pig barns in developing a com
mercial application.
5.2. Other systems and sensors
A variety of mixed systems and analysis methods are discussed
including accelerometers, dynamic models, flow and RHID sensors, and
implanted microchips.
In nature, sows built nests before farrowing to protect their piglets
from potential dangers such as extreme weather conditions and possible
predators (Sala et al., 2019). However, under commercial housing
conditions they are confined in alternative farrowing crates preventing
them from crushing the piglets and thus, lack the opportunity to express
these behaviours negatively affecting their welfare. Oczak et al. (2019)
developed a model that predicts the most appropriate time that sows
should be confined in farrowing crates so as they can express their
natural behavioural instincts. This model was based on data collected
from accelerometers placed in ear tags and successfully predicted the
starting (i.e., 7 h and 45 min before farrowing) and the ending (i.e., 1 h
and 9 min before farrowing) of nest building behaviour. It should be
noted that the system was not applied in a real-time series but on
recorded data and thus, a commercial application is still under
development.
Domun et al. (2019) developed three dynamic models and a neural
network to detect behavioural changes such as tail-biting, diarrhea and
fouling under various environmental conditions. 1624 finishers housed
in 112 pens were monitored. Photoelectric flow sensors were installed at
the drinkers to monitor pigs’ drinking behaviour and each pen’s tem
perature was controlled by two temperature probes. An automatic
shower system was installed above each pen for immediate control of
the environment. Each parameter (i.e., temperature, water consump
tion, RH, ventilation and heating and cooling outputs, age and number
of pigs, straw bedding renewal and tail type) was documented and
worked as an input for the neural network. The output was the fre
quency of fouling, diarrhea and tail-biting. They reported that the results
were promising, and that further research is needed to improve the
model’s sensitivity and specificity. It has to be noted that the data was
collected by the educated staff of Aarhus University. This may be a
problem if applied in commercial units as the staff although experi
enced, lacks in education and thus, this method of data collection might
not be reliable. Probably a combination of a camera & sound-based
automatic system would be more ideal for continuous monitoring
under commercial conditions.
Adrion et al. (2018) tested an ultra-high frequency (UHF) RFID
system for its efficiency in tracking pigs’ trough visits using cameras.
This system showed a 49.7% sensitivity, a 99.0% specificity and a 97.9%
accuracy. They reported that this technology needs further research and
development. In addition, Maselyne et al. (2015) placed a high fre
quency RFID system around the nipple drinkers and 55 RFID tags were
put on an equal number of pigs to monitor pigs’ individual drinking
behaviour. The system was validated by live observations and flowme
ters installed before each nipple drinker. It successfully registered 97%
of the total drinking bouts, hence the 99.2% of the total drinking
duration. However, they reported that the number and duration of the
drinking bouts was overestimated by 10 and 19% respectively, implying
that further research is needed for a PLF signal warning application as an
early problem detection system.
Süli et al. (2017) developed a method to detect reproductive and
respiratory syndrome virus disease using transponder microchips
implanted behind the ears of the pigs. Every pig had a leather collar
around the neck where an accelerometer was placed. A hand-held
scanner and telemeter for RFID tags were used to collect measure
ments of the transponders. Authors reported that an accelerometer can

10
“detect in almost real-time” behavioural changes in pigs’ movement due
to infections. In addition, the under-the-skin microchips provided in
formation that was collected only when the pig was immobilized. This
project is in touch with future research methods and commercial ap
plications used in the PLF area, as it does not only use data collected
from different sensors, but it also recovers data from pigs’ bodies
directly and in real-time without interfering with their everyday lives.
However, further research is needed to test whether this combination of
sensors affects pigs’ welfare, performance, and quality of the end
product.
6. The future of PLF
As pig enterprise tends to grow globally, farmers have less and less
time to spend with animals. This transition has a negative impact on
pigs’ lives and eventually production. PLF could assist in solving this
problem as today’s computer systems can potentially track continuously
any parameter of interest within, or out of the pig barn. However, pigs
tend to destroy any system they have access to (Bracke, 2007), thus the
future of PLF in this area is directed towards Information and Commu
nications Technology (ICT) sensors, cameras and microphone-based
systems (Berckmans, 2014a). These systems can monitor and track
pigs’ behaviours and adjust certain housing parameters according to
pigs’ needs without negatively affecting their lives.
PLF technologies basic working gear is the animal. Thus, future PLF
systems aim at real-time continuous monitoring and tracking of indi
vidual pigs and behaviours/bio-responses that can be associated with
numerous parameters, such as feed efficiency and health status. By
achieving this, PLF will positively affect the production chain process in
many ways. To the best of our knowledge commercially available PLF
systems estimating feed efficiency have yet to be developed as real-time
continuous monitoring and automated management of the pigs are yet
to be presented in today’s everyday pig farming.
Firstly, the farmers will benefit as they will not have to manually
adjust housing conditions and the housing practices will evolve to
remote control systems (Banhazi and Black, 2009; Halachmi and Guar
ino, 2016; Halachmi et al., 2018). Thus, farmers will save both time and
money. For example, aggression in a pig barn may cost the farmer either
due to deaths or, medical treatments and mainly delayed growth of the
injured pigs (Peden et al., 2019). A PLF system that could reduce the
costs due to aggression even by 2%, it is estimated that it would greatly
benefit the farmer overcoming its installation and everyday functional
energy costs (Tzanidakis, 2018). Farmers associations can use the pro
duced knowledge to promote the efforts of intensive pig production
towards improvements in environmental enrichment, animal welfare
and health but at the same time, effectiveness, and practicality of PLF
systems under commercial conditions should be improved (Vranken and
Berckmans, 2017). Products and applications resulting from PLF
research can be an add-on to pig companies related to pig unit’s oper
ation (i.e., feed production companies) existing portfolios, strengthening
their position in the market as well as their image as innovative com
panies (Banhazi et al., 2012a). Furthermore, farm advisors can use the
produced knowledge to support the farmers in optimizing production,
management processes and increase the units’ income. Researchers and
developers will also benefit from the future developed PLF tools as these
technologies offer the opportunity of better understanding the impor
tance of an animal-based approach to steer processes in livestock pro
duction (Berckmans, 2014a). It is worth noting that this technology can
become a daily management decision making tool assisting farmers to
improve their units and minimise the reliance on human labor (Kam
phuis et al., 2015). They create knowledge on how to convert biological
information into mathematical algorithms that makes it possible to
implement biological knowledge in a technical solution (Berckmans,
2014b). Furthermore, PLF deepens the knowledge of capturing and
analysing big datasets (big data) and thus, it saves time compared with
past monitoring methods and data analyses. Additionally, and in
agreement with Terrasson et al. (2017), the on-line application of PLF
products is going to improve their effectiveness and use as the farmers
will be able to control more effectively and almost immediately any
parameter of interest of the management process. Finally, future PLF
systems will positively affect human societies by improving pigs’
housing conditions and product quality, minimising carbon footprint per
animal and at the same time by saving energy not only in means of
electrical power but also in relation to feed intake, feed conversion and
growth.
Legislation strictly states that the implementation of under-the-skin
sensors is illegal. However, they could be a feasible approach for
important data collection in the near future, as research is conducted
towards the definition of acceptable laws in terms of animal welfare and
well-being (Berckmans, 2014a). Major companies focused on technol
ogies in the pig sector, have already developed a sensor that can detect
and treat an infection at the first cell of a pig’s body by measuring the
pro-inflammatory cytokine, acute phase proteins, effector molecules and
several other blood and body fluid variables. This sensor is attached to
the pig’s body and the blood is being pumped through the sensor. Re
searchers are working towards the development of an algorithm indi
vidually identifying the concentration and the variation of infected cells
(Berckmans, 2014a). It is possible that these technological advance
ments will not only lead to better food quality for the consumers, but
also push to further developments in the human medical area resulting
in possible better and faster treatments for the patients and improve
their everyday lives.
Another promising PLF application could be the automatic envi
ronmental adjustment in real time based on pigs’ needs and preferences.
It can potentially improve welfare, performance and thus the economy
of the units. Currently micro-climate control systems are based on pre
defined set-point temperature and relative humidity values combined
with farmers’ everyday experience. These values have been determined
on experimental measurements collected between 1950 and 1980
(Fournel et al., 2017). The technological advancements and the use of
different construction materials of today’s livestock buildings suggest
that these values need to be re-determined. Moreover, the definition of
living organisms (CITD) suggests that these methods are failing to ach
ieve thermal comfort for the pigs due to different surrounding conditions
within the pen. In agreement with Fournel et al. (2017), it is suggested
that PLF scientists and researchers should approach environmental
control considering: a) micro-climate rates using the latest bio-generic
models, and b) real-time thermal behaviour analysis and bio-responses
(i.e. lying positions and lying dispersion) of the pigs within the pen, or
behavioural changes such as drinker and feeder visits. It should be noted
that real-time continuous monitoring and analysis is essential for such
systems to exist and probably that is why they have not been introduced
yet.
To date, PLF technology has been mainly tested under laboratory
conditions (Berckmans, 2014a; 2014b; Wurtz et al., 2019) and research
under commercial conditions has just boomed the past decade and the
concept of a fully automated management process is almost eye clear
(Berckmans, 2017). Hartung et al. (2017) reported that in 2014 only one
out of eight farmers in absolute numbers, were familiar with the term
PLF. Within the same study, it was stated that this has improved radi
cally by 2016 where all eight farmers felt familiar with the term. This
decision was motivated by the free delivery and installation of the sys
tems due to the farmers’ lack of knowledge and understanding of the
great benefits for the economy, the labor productivity and the produc
tivity of the farm. This phenomenon could be mostly attributed to the
cost-benefit ratio and the lack of perceived economic value (Russel and
Bewley, 2013). Therefore, in agreement with van Hartem et al. (2017), it
is suggested that all future commercial applications should provide data
related with economic benefits which are more interesting for the
farmers. For example, the automatic weight monitoring and estimation
of pigs, although is the most important aspect of pig production, it has
almost no value at all for the farmer since in most cases it cannot be

11
controlled by single actions, but it is a very complicated issue (Berck
mans, 2014b).
In the early years of Precision Agriculture initial reports indicated
that the economic profitability was site-specific (Griffin et al., 2018).
Abeni et al. (2019) reported that the farmers evaluated the “bene
fit-to-cost ratio” of PLF tools, as the most important factor affecting their
decision making in purchasing them. Although in the literature it has
been stated that PLF tools will benefit the producers financially, pub
lished economic evaluation and analysis for each production manage
ment process using PLF is rare (Ouden et al., 1996; Lansink and
Reinhard, 2004). Thus, it is evident that PLF systems should be followed
by economic analysis of the total installation and operational costs
demonstrating their economic benefits on on-farm applications (i.e.,
value for money), whereas training sessions for the efficient usage of
these tools by the farmers should be organized (Obayelu et al., 2017). At
this stage, this may be one of the most important research concepts in
today’s PLF development centres.
Another debate is the installation and maintenance costs of PLF
systems. PLF technology can potentially be fairly cheap. Berckmans
(2014a) reported a cost difference of 11,900 euros (i.e., 12,300 vs. 400)
between 1989 and 2014 for an implanted sensor measuring heart rate,
temperature and body movement for chickens and pigs. This cost could
be reduced to 0.12 euros if 100,000,000 sensors per year were to be
produced. By applying the same philosophy to other monitoring sensors
such as cameras, microphones etc., it is evident that if a certain tech
nological development finds useful applications within the pig barns,
then purchase costs could almost be eliminated. However, labour costs
associated with the insertion and possible removal of these sensors and
the related food safety issues should be taken into consideration. The
possibility of biodegradable sensors such as skin-mounted epidermal
electronic systems (i.e., EES) for measuring skin temperature, heart rate,
blood pressure, etc. for humans (Rodrigeues et al., 2020) should be
taken in consideration in minimizing the installation costs.
7. Conclusion
It is evident that technological advancements have risen the past
decades and they will continue rising this century at an even faster pace.
Researchers in the PLF area have a vast variety of sensors and systems to
test in pig barns for almost every aspect of the production process. Today
the developed sensors can measure almost everything that is needed to
be measured. These technological advancements are a challenge to
wards developing systems that are easy to use, economically viable,
efficient, more environmentally friendly and at the same time improve
the welfare of the animals and the quantity and quality of the end
product. Combining different PLF technologies, namely 2D and 3D
cameras, microphones, sensors monitoring lameness and mobility, skin
temperature or conductivity, and glucose level, wireless communication
tools, internet connections and cloud storage, makes possible to screen
environmental, physiological and behavioural variables, ensuring
improved health and welfare status of animals, enhanced productive and
reproductive performance, and reduced environmental impact per ani
mal unit. However, PLF technologies are still under development and
fully automated systems that do not require human observation are yet
to be discovered.
The concept of “interesting technologies” research has been the main
field of PLF research the past three decades without assessing the value
of the information extracted from them. Therefore, it is evident that
researchers need to clearly testify and express the practicality of a PLF
system prior development, so that the farmer can clearly understand the
application’s beneficial impact on the production process. Fortunately,
this has been the main concept of PLF for the past five years and re
searchers are focusing on commercial application development. It seems
that both researchers and farmers start to understand the spirit of the era
that could lead to sustainable units with increased number of animals
and improved meat quality.
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.
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An overview of the current trends in precision pig farming technologies 2may2021

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