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https://doi.org/10.56556/gssr.v3i3.954  

                                                                  

 

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REVIEW ARTICLE  

An Overview of Precision Livestock Farming (PLF) Technologies for 

Digitalizing Animal Husbandry toward Sustainability 
  

Homaira Afroz Himu1, Asif Raihan2* 
 

1Department of Veterinary and Animal Sciences, University of Rajshahi, Rajshahi 6205, Bangladesh 
2National University of Malaysia, Bangi 43600, Selangor, Malaysia 

 

Corresponding Author: Asif Raihan. Email: asifraihan666@gmail.com 

Received: 16 July, 2024, Accepted: 25 August, 2024, Published: 27 August, 2024 

 

Abstract 

As the global population continues to expand, it is imperative for livestock farming to undergo necessary 

adaptations in order to effectively address the escalating food demands and enhance productivity. Concurrently, it 

is imperative to acknowledge and tackle concerns pertaining to animal welfare, environmental sustainability, and 

public health. The primary aim of the article is to provide a comprehensive examination of the latest advancements 

in the utilization of biometric devices, big data, and blockchain technology for the purpose of digitizing animal 

husbandry within the context of Precision Livestock Farming (PLF). Biometric sensors are physiological devices 

utilized for the purpose of monitoring the health and behavioral patterns of an individual animal. This information 

can be utilized by farmers to do population-level analysis. Big data analytics solutions employ statistical algorithms 

to examine extensive and intricate data sets, detecting significant trending patterns and offering advisory 

recommendations for farmers' decision-making. These systems are designed to interpret and integrate data obtained 

from biometric sensors. Blockchain technology, when combined with sensors, facilitates the secure and convenient 

tracking of animal products throughout their journey from the farm to the table. This approach demonstrates 

efficacy in the surveillance of disease outbreaks, the prevention of economic losses, and the mitigation of food-

related health pandemics. The implementation of PLF technology within the livestock industry has the potential to 

contribute to the attainment of sustainable development. 

 

Keywords: Animal husbandry; Precision livestock farming; Technology; Digitalization; Sustainability 

 

 

Introduction  

 

According to projections, the global human population is anticipated to surpass 9 billion by the year 2050, 

indicating a growth of almost 2 billion individuals in comparison to the present population. The primary locus of 

population growth will be observed in developing nations. According to Raihan and Himu (2023), the increase in 

population and rapid growth in these countries will result in a significant amplification of the market demand for 

animal agricultural goods. In developing nations, the production of livestock presents dependable food sources, 

employment opportunities, and potential for increased cash. A substantial proportion of the market demand for 

animal products will be met by local production. Furthermore, in light of the growing population and the escalating 



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need for animal protein, consumers are exhibiting heightened concerns regarding the detrimental impacts of 

livestock production on the environment, public health, and animal welfare (Ochs et al., 2018). Given the 

diminishing availability of water and land resources, livestock farmers must devise strategies to enhance output 

while utilizing their few resources in a sustainable fashion (Baldi & Gottardo, 2017).  

Moreover, a notable transformation in societal perspectives, namely among consumers, has further emphasized the 

imperative for conscientious research and innovation in order to effectively tackle pressing concerns in cattle 

production through the use of circular and sustainable methodologies (Himu & Raihan, 2023). The process of 

digitalization is expected to facilitate the progressive achievement of these objectives. The application of digital 

technologies in livestock farming will provide a thorough examination and accurate understanding of the dynamics 

and impacts of climate change on the ecology of farm animals (Raihan, 2023a). The successful control and 

mitigation of novel infectious animal illnesses in transboundary livestock, including those that can be transmitted 

to people (zoonosis), necessitates the adoption of strategic approaches and optimal methodologies. The process of 

digitalization has the potential to offer several solutions, including the development of prediction tools targeted at 

the prevention, mitigation, and preparedness of animal diseases and pandemic situations (Raihan, 2023b). 

To effectively meet the growing demand for animal protein and simultaneously tackle concerns related to 

environmental sustainability, public health, and animal welfare, farmers and animal scientists are increasingly 

relying on Precision Livestock Farming (PLF) technologies to digitize the entire process of livestock agriculture. 

The present research investigation examines the application of PLF technologies, encompassing biometric sensors, 

big data, and blockchain technology, with the aim of augmenting livestock productivity, particularly with regards 

to improving animal health and well-being. Figure 1 illustrates the application of PLF technology in the context of 

cattle production. 

 

 
Figure 1. Livestock production with PLF technologies (Neethirajan & Kemp, 2021). 



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Livestock Farming Trends 

 

In recent years, significant advancements have been achieved in diverse domains, including automated feeding 

systems, milking robots, waste management, and the enhancement of production efficiency through the utilization 

of precision instruments, animal breeding techniques, genetic research, and nutritional interventions. 

Notwithstanding the aforementioned advancements, significant challenges persist. The implementation of 

intensive livestock management practices is crucial in order to meet the increasing demand for animal products. 

Nevertheless, the density and overcrowding of livestock housing provide difficulties for farmers in effectively 

monitoring the animals' health and welfare (Helwatkar et al., 2014). The escalating impacts of climate change are 

anticipated to heighten the susceptibility of livestock animals to illnesses, heat stress, and various health hazards 

(Bernabucci, 2019; Raihan, 2023c). As a result, there will be a heightened demand for expeditious identification 

of health issues and illness outbreaks, comprehension of disease transmission mechanisms, and implementation of 

preventive measures to alleviate substantial economic repercussions (Thornton, 2010; Raihan, 2023d). In light of 

growing apprehensions regarding animal welfare, transparency, and environmental sustainability (Raihan, 2023e), 

there has been a surge in the exploration of digitalizing livestock agriculture through the utilization of PLF 

technology (Klerkx et al., 2019). The PLF technologies utilize process engineering methodologies to operationalize 

the automation of livestock agriculture. This technology enables farmers to effectively monitor the welfare and 

health of extensive animal populations, promptly detect issues with individual animals, and potentially anticipate 

potential problems through the analysis of historical data (Benjamin & Yik, 2019). Recent advancements in PLF 

technologies encompass a diverse range of applications. Examples of common applications include the 

surveillance of bovine behavior, the identification of vocalizations such as screams in pigs, the monitoring of 

coughs in several animal species to diagnose respiratory ailments, and the detection of bovine pregnancy through 

alterations in body temperature. The utilization of PLF technology has the potential to assist farmers in the 

surveillance of infectious illnesses within the realm of livestock agriculture, hence enhancing both food safety and 

availability. The implementation of PLF technology would ultimately enhance animal welfare and mitigate food 

safety issues, while simultaneously optimizing resource consumption (Norton et al., 2019). 

 

Livestock Industry Challenges 

 

This study identifies three key factors that provide significant challenges in the efficient monitoring of animal 

welfare: cost, reliability, and timeliness of observations. A considerable proportion of current techniques are 

characterized by their time-consuming nature, demanding substantial work and thus, incurring high costs 

(Jorquera-Chavez et al., 2019). Animal husbandry practitioners often rely on the observations made by stockpeople 

to detect and address health and welfare concerns. Nevertheless, a considerable disparity exists among commercial 

establishments in terms of the size of their workforce in relation to the number of animals they house. For example, 

on a commercial pig farm, the typically observed ratio of stockpersons to pigs is 1:300 (Benjamin & Yik, 2019). 

Even stockpeople who possess exceptional attentiveness and advanced skills may inadvertently overlook animals 

in a dangerous condition. Third-party auditing programs offer comprehensive evaluations of animal welfare; yet, 

they often incur significant costs and consume substantial amounts of time.  

The implementation of PLF technologies, specifically biometric sensors, would facilitate the real-time monitoring 

of animal welfare by farmers, in a manner that is characterized by precision, impartiality, and consistency. This 

will enable the prompt identification of issues and the timely execution of proactive measures to mitigate 

significant operational shortcomings. Polymerase chain reaction (PLF) technologies offer a non-invasive sampling 

method, facilitating the collection of precise data by farmers and researchers. These measurements can then be 



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employed to address welfare concerns (Jorquera-Chavez et al., 2019). Precision farming (PLF) technologies 

provide the capacity to mitigate resource consumption. The implementation of a proactive and tailored strategy 

towards animal health has the potential to substantially decrease the dependence on pharmaceutical interventions, 

particularly antibiotics.  

The escalating apprehension among consumers regarding the sustainability and welfare of animal products has 

resulted in a heightened need for transparency from livestock farmers (Raihan, 2024). The implementation of 

blockchain technology facilitates the establishment of transparency between farmers and consumers on food 

offerings, hence alleviating the need for farmers to allocate additional time resources. In the present setting, the 

time that is conserved could be more efficiently allocated towards the supervision of issues pertaining to animal 

welfare, public safety, and environmental sustainability (Benjamin & Yik, 2019). 

 

Biometric Sensors 

 

Biometric sensors are utilized to observe and analyze the behavioral and physiological attributes of livestock, 

hence facilitating comprehensive evaluation of an animal's health and overall welfare by farmers over a specified 

duration. At present, there exists a wide array of biometric sensors that can be classified into two main categories: 

non-invasive and invasive designs. One example of versatile non-invasive sensors utilized in barn monitoring 

encompasses surveillance cameras and sensors integrated into feeding systems, which enable the monitoring of 

animal weight and feed intake. Non-invasive sensors encompass a range of devices that can be conveniently affixed 

to animals, such as pedometers, GPS (global positioning system), and MEMS (micro-electromechanical) based 

activity sensors. These sensors are employed for the purpose of monitoring and mapping animal behavior 

(Helwatkar et al., 2014). In livestock, invasive sensors are commonly utilized through ingestion or implantation, 

although their exploration in this context is very infrequent. The utilization of these sensors becomes advantageous 

in the surveillance of internal physiological parameters in dairy cows, including rumen health, body temperature, 

and vaginal pressure (Helwatkar et al., 2014). In order to enhance operational efficiency and reduce resource 

requirements, the livestock industry has adopted biometric sensor technologies to effectively monitor a greater 

population of animals. The utilization of this approach enables the industry to obtain precise and impartial 

assessments of animal health and welfare (Helwatkar et al., 2014). The data collected by the sensors is subsequently 

stored in databases and subjected to analysis using algorithms, which are computational procedures designed to 

address specific problems in a sequential manner. Real-time livestock biometric sensors employ sophisticated 

algorithms to interpret unprocessed sensor data and produce biologically meaningful insights. This encompasses 

quantitative measures such as the average duration of different actions displayed by animals within a particular 

day, as well as fluctuations in activity levels across specific time intervals (Benjamin & Yik, 2019). Furthermore, 

these sensors have the capability to monitor behaviors based on predetermined criteria and alert farmers when an 

animal's behavior deviates from typical patterns. This allows farmers to examine the animal and implement suitable 

measures to improve its overall health and welfare. The integration of biometric sensors with big data analytics, 

artificial intelligence, and bioinformatics technologies, including computational genomics, holds promise in 

identifying animals possessing desired traits and facilitating their inclusion in breeding initiatives (Ellen et al., 

2019). 

The forecasted trajectory indicates a rise in the utilization of biometric sensors within the livestock farming and 

animal health sectors over the course of the forthcoming decade. The aforementioned benefits stem from their 

capability to deliver instantaneous outcomes, exceptional precision, and the aptitude to amass substantial volumes 

of data. The timely acquisition of information regarding the welfare of animals facilitates prompt intervention and 

frequently mitigates the necessity for supplementary interventions. Thermal infrared (TIR) imaging presents a 



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viable alternative to invasive thermometers, which necessitate the confinement and regulation of animals, for the 

purpose of monitoring their body temperatures. The application of thermal infrared imaging (TIR) in the eye area 

and general skin temperature has demonstrated potential in the early detection of stress levels and diseases, 

surpassing the capabilities of earlier methodologies, with a typical detection timeframe of 4-6 days (Koltes et al., 

2018). The implementation of this approach facilitates prompt intervention and mitigates the potential transmission 

of diseases among animal populations, such as flocks or herds (Martinez et al., 2020). In the realm of livestock 

animal monitoring, the primary non-invasive sensors utilized encompass thermometers, accelerometers, radio-

frequency identification (RFID) tags, microphones, and cameras. This technological equipment facilitates the 

ability of farmers to watch and monitor many parameters within the barn, including temperature, activity levels, 

sound levels (such as vocalizations, sneezing, and coughing), and specific behaviors (such as aggression in pigs) 

(Benjamin & Yik, 2019). 

The utilization of thermometers, in conjunction with physiological sensors such as thermoinfrared (TIR) and heart 

rate monitors, enables the assessment of stress levels in animals prior to their slaughter. The aforementioned 

measurements might thereafter be juxtaposed with meat quality indicators in order to enhance the consistency and 

superiority of consumer products (Jorquera-Chavez et al., 2019). Through the utilization of biometric sensors, 

researchers are able to promptly detect variations in heart rate pertaining to both positive (eustress) and negative 

stressors. Additionally, it is possible to do comparisons of individual reactions among animals and see the temporal 

variations in heart rate resulting from different stressors. In a study using porcine subjects, the introduction of a 

negative stressor led to a transient elevation in heart rate lasting for a duration of one minute subsequent to the 

exposure to a high decibel auditory stimulus. The administration of a positive stressor, such as the provision of a 

towel for play, led to a sustained elevation in heart rate for a duration of two minutes. Conventional or indirect 

measures of welfare may exhibit limitations in their capacity to detect intricate fluctuations (Joosen et al., 2019). 

Heart rate monitors are considered to be highly valuable instruments for assessing physiological well-being and 

the production of metabolic energy. Nie et al. (2020) have demonstrated that biometric sensors, specifically 

photoplethysmographic sensors, can be conveniently affixed to ear tags or other anatomical regions, enabling 

uninterrupted monitoring of cardiopulmonary activity in livestock. 

There is a growing trend among livestock producers to employ RFID sensors, which can be affixed to ear tags and 

collars or positioned subcutaneously, for the purpose of monitoring a wide array of behaviors such as general 

activity, eating patterns, and drinking habits. The application of microphones in acoustic analysis facilitates the 

monitoring of vocalizations and coughing, hence enabling the timely identification of welfare issues among 

farmers. Microscopic devices offer the advantage of being easily and inconspicuously deployed within barns for 

the purpose of monitoring large animal populations (Mahdavian et al., 2020). Likewise, the strategic placement of 

cameras within barns enables the recording of a wide array of practical data. According to Jorquera-Chavez et al. 

(2019), computational algorithms designed for video analysis have the capability to detect alterations in the posture 

of animals, potentially serving as an indicator of lameness and other health conditions. The utilization of camera 

image analysis facilitates the surveillance of several parameters relevant to animal behavior, including weight, 

mobility, water consumption, individual identification, and aggression (Norton et al., 2019). The advancement of 

facial detection technology is becoming a larger priority in the field of automated animal welfare monitoring. The 

application of machine learning algorithms in facial recognition technologies facilitates the detection of distinct 

facial traits in animals, hence enabling the recognition of individuals or the monitoring of emotional state 

fluctuations (Marsot et al., 2020). A cohort of researchers specializing in animal welfare is currently engaged in 

the advancement of "grimace scales" for various animal species. The primary objective of these scales is to 

facilitate the reliable monitoring of animals' emotional states, with a specific emphasis on pain (Viscardi et al., 

2017). Research has indicated that livestock animals frequently experience stressful procedures, including 



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dehorning, tail docking, and castration (Viscardi et al., 2017; Müller et al., 2019). Accurate identification of 

behavioral intent in animals can be achieved through the examination of facial expressions. According to 

Camerlink et al. (2018), pigs exhibiting aggression exhibit distinct facial changes in contrast to those that reduce 

or refrain from engaging in violent behavior. Previous research has proposed facial detection as a potentially cost-

effective alternative to RFID tags for the purpose of identifying individual animals (Marsot et al., 2020). 

The utilization of biometric sensors is of paramount importance in the mitigation of disease impacts and 

transmission. These sensors have the ability to monitor variations in temperature, patterns of behavior, levels of 

sound, and physiological indications including pH levels, metabolic activity, pathogens, and the identification of 

toxins or antibiotics within the human body. Presently, the excessive usage of antibiotics in the context of cattle 

farming represents a noteworthy concern that carries substantial implications for human health (Himu & Raihan, 

2023). The capacity to identify the existence of antibiotics empowers farmers to administer healing measures to 

animals afflicted with ailments, while concurrently guaranteeing the generation of secure and nourishing animal 

commodities for the global populace. Biologic sensing technologies have the potential to be utilized for the 

detection and identification of pathogenic infections, such as avian influenza, coronavirus, and Johne's disease. 

Johne's disease is a pathogenic bacterial infection that exerts detrimental effects on ruminant animals, resulting in 

substantial economic ramifications for agricultural practitioners. Biometric sensors possess the ability to identify 

biological markers linked to inflammation, hence facilitating the surveillance of diseases on a significant 

magnitude. One potential use of thermal infrared imaging (TIR) involves the identification of foot disorders 

through the analysis of foot pictures (Jorquera-Chavez et al., 2019). Figure 2 illustrates the various applications of 

biosensors within the context of animal production. 

 

 
Figure 2. Livestock biosensors applications. 



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Big Data and Machine Learning  

 

The application of biometric sensors and bioacoustic devices in the surveillance of livestock welfare and health 

yields substantial volumes of data that necessitate processing and analysis to give valuable insights for animal 

management. Considerable advancements have been made in the domain of big data analytics, a discipline that 

encompasses the acquisition and analysis of vast and complex datasets (Wolfert et al., 2017; Raihan, 2023f). The 

concept of big data pertains to datasets characterized by an extensive quantity of rows and columns, hence posing 

challenges in terms of visual data analysis. Moreover, the aforementioned data sets sometimes encompass a 

multitude of variables or predictors, rendering them intricate and unsuitable for conventional statistical 

methodologies (Morota et al., 2018). The "4 Vs" model, as proposed by Wolfert et al. (2017) and Koltes et al. 

(2019), encompasses four fundamental attributes that define big data. These attributes include volume, which refers 

to the numerical quantity of data; velocity, which pertains to the speed at which data is accessed or utilized; variety, 

which encompasses the diverse forms of data; and veracity, which involves the processes of data cleaning and 

editing. The utilization of modern data analytics and modeling in PLF technologies enables managers to obtain 

precise information pertaining to food requirements, reproductive circumstances, and declining productivity 

trends. This information also serves to identify potential difficulties associated with animal health and welfare. The 

analysis of data obtained from sensors by big data models enables the identification of irregularities that have the 

potential to affect animals. The utilization of big data models enhances the efficacy of sensor technology by 

facilitating the generation of valuable insights for agricultural purposes. These insights encompass the ability to 

predict the probability of future events, enhance farmer responsiveness and decision-making processes, and 

potentially enable the categorization of animals according to their specific requirements. Consequently, this 

facilitates the optimal allocation of resources (Koltes et al., 2019). The data obtained from sensors can be classified 

into two distinct categories: animal-oriented data, which pertains to the phenotypic features of the animals, and 

environment-oriented data, which pertains to the characteristics of the surrounding environment. Simultaneous 

monitoring of both data types is crucial due to their substantial impact on animal health and productivity. The 

incorporation of animal and environmental data in the adoption of digital technologies in cattle agriculture holds 

promise for improving multiple facets, including health management, nutrition, genetics, reproduction, welfare, 

biosecurity, and greenhouse gas emissions (Piñeiro et al., 2019). 

The analysis of data can be classified into two primary categories: exploratory and predictive data analysis. 

Exploratory methodologies involve the examination of historical data in order to find pertinent elements, while 

predictive models employ data to make projections about future events based on predetermined criteria (Sasaki, 

2019). When dealing with extensive data sets, it is imperative to employ precise data analysis techniques. 

According to Koltes et al. (2019), the inclusion of varied data necessitates the examination of multiple elements 

within the given situations. Additionally, it is important to eliminate extraneous information in order to cleanse the 

data systematically. The utilization of predictive approaches by farmers enables them to anticipate future outcomes 

and implement a proactive management strategy (Wolfert et al., 2017). The application of big data technology has 

the potential to enhance disease transmission monitoring through the establishment of contact networks and the 

identification of people with a heightened risk (VanderWaal et al., 2017). Machine learning (ML) is a subfield 

within the realm of artificial intelligence that employs algorithms to generate statistical predictions and establish 

conclusions (Morota et al., 2018; Raihan, 2023g). Data mining is a systematic procedure that entails the training 

of databases to identify patterns and extract relevant information (Raihan, 2023h). Machine Learning (ML) is an 

emerging domain within the study of PLF that leverages extensive datasets to shape computer algorithms. These 

algorithms have the capability to continuously gather information from sensor data and enhance their performance 

autonomously, without the need for human data analysis (Benjamin & Yik, 2019). 



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Machine learning (ML) techniques are extensively employed in the field of animal genetics research for the 

purpose of predicting phenotypes based on genotypic data, identifying abnormalities within a population, and 

performing genotype imputation. The utilization of machine learning (ML) extends to the detection of mastitis in 

dairy farms through the implementation of automated milking technology, the estimation of body weight using 

picture analysis, and the monitoring of microbiome health (Morota et al., 2018). Machine learning (ML) and big 

data analytics have the potential to improve the well-being and efficiency of dairy animal husbandry. The diseases 

of lameness and mastitis pose substantial welfare concerns in dairy cattle and can exert adverse effects on optimal 

milk production. Effectively monitoring and accurately anticipating the likelihood of these situations is of utmost 

importance. The references cited are Ebrahimi et al., 2019; Taneja et al., 2020; Warner et al., 2020. 

The application of big data analytics methodologies enables the collection and integration of data from several 

farms, with the objective of improving the effectiveness of production processes and systems (Aiken et al., 2019). 

The significance of big data is contingent upon the automation, accessibility, and correctness of the data being 

considered. In order to uphold the integrity of the data, it is imperative to incorporate error checking and quality 

control protocols (VanderWaal et al., 2017). In order to optimize the utilization of PLF in agricultural settings, it 

is imperative to undertake the development of software, implementation of quality control mechanisms, 

establishment of database systems, and application of statistical approaches to proficiently condense and present 

the data. Furthermore, it will be imperative to carefully choose the most appropriate data models for this objective 

(Koltes et al., 2019). The management of extensive data gathered from agricultural operations presents notable 

obstacles in terms of privacy and security (Wolfert et al., 2017). Consequently, the current utilization of farm data 

collection is limited due to farmers' overriding concern for privacy preservation. By using data obtained from 

biometric and biological sensors, sophisticated data analysis techniques can be employed to create digital farming 

service systems that hold promise for enhancing animal production capacity, productivity, and livestock welfare. 

The development of the MooCare prediction model aims to assist dairy producers in effectively managing their 

dairy farming operations through the utilization of Internet of Things (IoT) sensors and big data analytics. The 

model proposed by Righi et al. (2020) is designed to forecast milk production. In their study, Gulyaeva et al. (2020) 

developed methodologies utilizing extensive datasets for the purpose of detecting and forecasting chicken diseases. 

The utilization of wearable sensors and livestock husbandry sensing systems enables the collection of digital data, 

which can subsequently be employed to develop precise digital fingerprints for animals. The aforementioned 

fingerprint can then be employed in predictive and adaptive decision-making methodologies. According to Tsay et 

al. (2019), the three elements, Footprint, Fingerprint, and Forecast, serve as both guiding principles for livestock 

producers in the management of animal production and as tools for the creation of integrated application systems 

pertaining to livestock value, supply, and food chains. Figure 3 illustrates the utilization of sensor-based big data 

applications in the context of precision livestock production. 

A blockchain can be defined as a secure and decentralized database of transactions, wherein each transaction is 

associated with a distinct node (Elisa et al., 2023). The nodes are organized into records, commonly known as 

"blocks", based on a consensus reached among the participating organizations. The blocks exhibit interconnectivity 

and include distinct hash codes, so establishing a sequential chain. Upon the occurrence of a new transaction, a 

node is promptly generated, including pertinent details regarding said transaction, and subsequently appended to 

the blockchain (Chattu et al., 2019). The core attributes of blockchain technology encompass its distributed nature, 

transparency, immutability, and democratic underpinnings. Within the realm of livestock management, it is 

important to assign a distinct identification to every animal present on the farm. The distinct identification will 

remain associated with the animal throughout its entire lifespan, encompassing data regarding the farm(s) in which 

it resided, the mode of transportation utilized for transferring the animal from the farm(s) to the slaughterhouse, 

the veterinarian tasked with examining the animal at the slaughterhouse, the evaluation of quality subsequent to 



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slaughter, the transportation of the meat product, and ultimately, particulars pertaining to the packaging and 

disposition of the product. 

 

 
Figure 3. Precision cattle production using sensor-based big data (Neethirajan & Kemp, 2021). 

 

Blockchain  

 

The implementation of blockchain technology in cattle farming presents a multitude of advantages, including the 

facilitation of decentralized and automated transactions. This has the potential to enhance the effectiveness of 

auditing systems utilized by certification and regulatory authorities (Akram et al., 2024). Furthermore, it facilitates 

seamless integration of systems and effectively manages comprehensive documentation of all transactions 

pertaining to the transportation of animals from their farm to the dining table. Furthermore, it enhances the level 

of traceability and openness within the cattle farm industry (Picchi et al., 2019). In contemporary times, there has 

been a growing dearth of trust between farmers and clients due to the escalating demand for transparency in 

agricultural commodities. The potential of blockchain technology lies in its ability to enhance confidence among 

consumers through the provision of transparent and comprehensive information pertaining to the complete lifespan 

of an animal (Patel et al., 2023). 

The application of blockchain technology has significant promise in facilitating the detection and surveillance of 

animal disease outbreaks, encompassing H1N1 swine flu, Foot-and-Mouth and Mad Cow diseases, Avian 

influenza, and the recent surge in salmonella infections. There is an increasing level of consumer consciousness 

regarding the environmental and ethical dimensions associated with animal farming (Raihan, 2023i). Furthermore, 

there is a growing demand for transparency for the methodologies employed in animal husbandry. Ensuring food 



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safety is a significant priority for consumers. Based on data provided by the World Health Organization (WHO, 

2020), it is estimated that around 10% of the global population has food-related illnesses each year, leading to an 

annual mortality rate above 420,000 individuals. The implementation of blockchain technology has promise in 

enhancing the capacity to monitor and identify the source of hazardous food products, hence enhancing the 

traceability and accountability for problematic practices within the livestock farming industry (Lin et al., 2018). 

An essential advantage of blockchain technology is the decentralized distribution of information among a network 

of users, as opposed to being centralized and managed by a single people or group. In the event of a cattle disease 

outbreak, producers worldwide would have the ability to securely input and retrieve disease management data. The 

aforementioned would empower individuals to actively participate in the management of the outbreak or equip 

themselves for an anticipated outbreak that could impact their agricultural operations (Chattu et al., 2019). 

Given the growing globalization of food chains and systems (Raihan, 2023j), it has become imperative for animal 

products to adhere to various norms and standards pertaining to animal welfare and sustainability. Efficient 

retrieval of regulatory documentation poses a challenge for regulators and third-party inspectors, particularly when 

such material is available in physical format or limited databases (Motta et al., 2020). Based on the findings of 

Motta et al. (2020), it is evident that the cattle farm industry exhibits a relatively lower level of digitalization in 

comparison to other industries, suggesting a notable opportunity for advancement. The integration of blockchain 

technology within the realm of animal agriculture has promised in mitigating the aforementioned issues pertaining 

to disease outbreaks and quality assurance in food production. Depicted in Figure 4 is the streamlined operational 

framework of the system, which facilitates the provision of services to diverse users. The main objective of the 

quarantine department is to ensure the comprehensive verification of vaccination quarantines and the meticulous 

examination of health data information. In contrast, the agricultural regulatory agency is primarily concerned with 

the specific rules pertaining to the farming process. The environmental protection agency has expressed 

apprehension regarding the environmental challenges presented during the breeding process, namely pertaining to 

the management of breeding waste (Raihan, 2023k). However, the main emphasis for farmers lies in acquiring 

animal breeding genetic data. It is imperative for slaughterhouses to implement a comprehensive system that 

facilitates the efficient management of livestock slaughter, while distributors necessitate unrestricted access to 

pertinent slaughter information. In order to conduct precise risk assessments, insurance organizations require 

comprehensive data regarding the health condition of the authorized livestock, whereas financial institutions 

prioritize the collection of information pertaining to farmers' assets and livestock breeding techniques. 

 

 
Figure 4. Livestock insurance's streamlined business strategy (Shen et al., 2023). 



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Notwithstanding its manifold benefits, blockchain technology is now in its nascent stages of advancement for 

widespread implementation in the food industry, with a restricted body of study investigating its impact on 

livestock farming (Picchi et al., 2019). Bioengineers and data scientists possess the potential to make substantial 

contributions towards the establishment of precise criteria for the selection of the most efficient blockchain solution 

tailored to the unique requirements of various companies within the cattle production sector. 

 

Conclusions 

 

The primary objective of this review article is to examine PLF technologies, which are designed to optimize 

livestock production while addressing client concerns. The technological approaches investigated in this study 

encompass biometric sensors, big data analysis, and blockchain technology. The use and integration of PLF 

technologies have the potential to address the growing apprehensions among consumers pertaining to animal 

welfare, environmental sustainability, and public health. Furthermore, it can also contribute to addressing the 

increasing need for animal-derived goods as a result of the growing global population. Biometric sensors facilitate 

the collection of real-time data pertaining to the welfare and state of animals, thereby empowering farmers to adopt 

proactive management strategies that promote sustainable and secure food production. Big data analysis is a 

process that transforms sensor data into valuable and applicable outcomes for agricultural operations. The 

utilization of blockchain technology in animal husbandry serves to augment transparency and traceability, hence 

fostering heightened customer confidence and enhancing food safety measures. Nevertheless, the application of 

PLF technology in animal agriculture is presently in its nascent phase, necessitating the resolution of various 

obstacles prior to the widespread adoption of these technologies by farmers and consumers on a global scale. The 

realization of a society that is both digitally inclusive and robust, facilitated by the implementation of novel 

digitalization methods in cattle farming, necessitates the active involvement and engagement of citizens in the 

collaborative process and endorsement of technical advancements. 

 

Declaration  

 

Acknowledgment: N/A 

 

Funding: This research received no funding. 

 

Conflict of interest: The authors declare no conflict of interest. 
 

Ethics approval/declaration: N/A 

 

Consent to participate: N/A 

 

Consent for publication: N/A 

 

Data availability: N/A 

 

Authors contribution: Homaira Afroz Himu and Asif Raihan contributed to conceptualization, visualization, 

methodology, reviewing literature, extracting information, synthesize, and manuscript writing. 

 

 

 



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https://www.who.int/news-room/fact-sheets/detail/food-safety
https://www.who.int/news-room/fact-sheets/detail/food-safety

