Advancements in Cow Health Monitoring: A Systematic Literature Review of IoT Applications

Over the past two decades, the widespread adoption of the Internet has catalyzed significant advancements across various sectors, offering substantial benefits to both entrepreneurs and consumers. The primary advantage of this technological revolution has been the ability to generate and utilize services in real time. Emerging as a pivotal innovation in this digital landscape, IoT extends these benefits, exhibiting the potential to transform operational paradigms across multiple industries. This review explores the application of IoT in diverse sectors, with a particular focus on agriculture, livestock, healthcare, traffic management, security, retail, smart home technology, and urban development.

Specifically, the integration of IoT in livestock farming, especially in monitoring cow health, is identified as a highly promising approach. The utilization of various IoT devices and sensors for monitoring cows' health conditions exemplifies this trend. Recent technological advancements have enhanced cattle health monitoring systems, integrating comprehensive management and decision-making tools. For instance, an IoT monitoring system designed for dairy cows, encompassing hardware components, a cloud-based system, a user-friendly application, and advanced data measurement and analysis algorithms, has been noted [1]. This system is proficient in recording vital parameters such as body temperature, relative humidity, heart rate, and rumination rate at regular intervals. These parameters are pivotal in predicting milk yield and overall bovine health [2]. Furthermore, the introduction of "SmartDairyTracer" demonstrates the breadth of IoT applications, encompassing the tracking of cow well-being, milk processing, and transportation safety, and facilitating the dissemination of crucial data through a secure and reliable platform. This ensures stakeholders and customers have access to meaningful, verifiable information [3].

In light of the burgeoning research in the domain of IoT-based cow health monitoring, a comprehensive compilation and analysis of recent studies are imperative. This research is dedicated to presenting an in-depth, structured evaluation of literature in the field of IoT applications for cow health monitoring, encompassing various sensors and devices. This systematic review aims to encapsulate all pertinent studies, offering insights into the current state of technology and its applications. The contributions of this paper in the realm of IoT cow health monitoring are manifold. A detailed literature review pertaining to cow health monitoring technologies is outlined in Section 2. Section 3 delineates the research methodology, encompassing the research objectives, research questions, search strings, and the criteria for inclusion and exclusion, thereby ensuring the selection of studies specifically related to IoT in cow health monitoring. The results of the research are methodically presented in Section 4, utilizing tables and charts for clarity and comprehensiveness. In Section 5, the findings are synthesized, employing a research hierarchy to structure the analysis. Section 6 is dedicated to discussing potential avenues for future research, aimed at guiding scholars in the field of IoT cow health monitoring. Finally, the conclusion of the article is presented in Section 7.

Recent advancements in the field of IoT-based cow health monitoring have been the subject of extensive research. This section collates and discusses various strategies identified in the literature for monitoring cow health using IoT technologies. A pivotal development in this area is the implementation of the Narrow-Band Internet of Things Communications (NB-IoT) platform, fundamental to an estrus detection system for cows. This technology, aimed at identifying significant changes in cows' physical conditions, facilitates timely estrus status recognition, thereby alerting farm administrators. The adoption of such technology has been observed to mitigate the workload on staff and augment the economic efficacy of pastures [4]. Additionally, an insightful study integrates IoT with artificial intelligence (AI) to enhance cattle health monitoring systems. This research not only describes existing cattle health monitoring architectures but also critically evaluates the challenges faced. Based on this evaluation, it proposes potential modifications to enhance future iterations of these systems [5]. Another noteworthy innovation is the "MOOnitor", an intelligent IoT device designed for livestock tracking. This neck-mounted gadget represents a significant step forward in livestock management technology [6].

The exploration of IoT-based systems for cow health management has yielded diverse and innovative solutions. A system combining health monitoring, disease detection, and management of ailing cows through IoT and intelligent system technologies has been developed. This system integrates monitoring and detection functionalities into a unified application, significantly enhancing farm management efficiency [7]. Further advancements are highlighted, where a proposed system allows farmers continuous online access to monitor vital parameters of each cattle, including body temperature, heart rate, humidity, and posture [8]. The cow disease prediction (CDP) method, a component of the LiveCare architecture, has been introduced as an unsupervised multiclass classifier [9]. Additionally, collar devices are employed for tracking cattle health, collecting data on body temperature, heart rate, and movement [10], thus facilitating proactive health management.

An innovative real-time cattle health monitoring system based on IoT has been proposed [11], employing wearable collars and IoT sensors. This approach enhances the efficiency of livestock management. Similarly, an IoT-based cloud-based Livestock Management System (LMS) is suggested [12], offering a comprehensive solution for tracking and monitoring animal health. An IoT-based system designed to identify cattle ailments and issue alerts for animals requiring assistance has been developed [13]. This system is pivotal in ensuring timely veterinary intervention. Lastly, a system capable of wirelessly storing data and monitoring and analyzing an animal's health has been created [14], representing a significant advancement in the field of livestock health technology. The scope of IoT applications in cattle health and activity monitoring continues to expand, as evidenced by recent studies. The CattleCare technology, an IoT-based innovation, plays a crucial role in monitoring the health and activities of cattle. This technology incorporates the "Cattle Activity Prediction" algorithm, which classifies cattle behavior, thus aiding in the detection of behavioral changes and other health-related aspects [15]. A notable development in this field is a system designed for tracking cattle movement and predicting their locations, effectively monitoring animals along set perimeters [16].

Additionally, the utilization of the Digital Smart Collar has been proposed for real-time tracking of cattle growth and health [17]. This device represents a significant leap in monitoring technologies, offering continuous health assessment. Further, a multi-sensor board specifically engineered to monitor various biological parameters, such as skin body temperature and heart rate, has been introduced [18]. This system underscores the importance of multi-faceted health monitoring in cattle. Moreover, a system has been constructed for regular health checks of dairy cows, with the aim of identifying diseases through symptomatic behavioral changes [19]. The integration of IoT and machine learning in estimating cow health status has been explored, highlighting the use of various wearable devices and sensors for precise livestock management [20]. This approach exemplifies the fusion of advanced technologies in enhancing cattle health monitoring. Lastly, a model employing Arduino-based sensors for disease identification in cattle has been proposed [21], reflecting the ongoing evolution of sensor technology in livestock health management.

This study adopts a SLR as its primary research methodology. The focus is placed on analyzing current IoT-based applications, sensors, devices, and communication protocols pertinent to cow health monitoring. The approach, as delineated by Farooq et al. [22], has been employed to ensure impartiality in both the selection of information and the representation of findings. The methodology followed in this research is illustrated in Figure 1.

The objectives of this study are threefold:

O1: To identify more targeted state-of-the-art studies in the field of IoT cow health monitoring.

O2: To delineate the existing IoT applications, sensors, and devices utilized in cow health monitoring.

O3: To ascertain future research directions, particularly focusing on the challenges and unresolved issues in cow health monitoring.

As part of the SLR, four research questions have been formulated, each accompanied by their respective motivations, as presented in Table 1.

A searching string was devised to identify relevant published papers pertaining to the themes of the study. The focus was primarily on IoT applications in cow health monitoring, as determined by a preliminary search. Additionally, the use of IoT sensors, devices, and communication protocols was also considered during the initial search phase. Various search engines and digital libraries were utilized by researchers to gather data, ensuring the selection of the most authoritative sources to address the research questions. Based on their academic rigor and relevance to the study objectives, selected databases included Elsevier, IEEE, MDPI, and Springer. The subsequent phase involved outlining the standard procedures for operationalization and defining specific search terms for querying technical and scientific documentation in digital libraries and search engines. The search string used in this study is detailed in Table 2.

The evaluation of papers for their relevance to the research questions was a critical step in this study. The selection process, as guided by Farooq et al. [22], involved a meticulous screening of articles. Initially, papers were assessed based on their titles, with those not pertinent to the research questions being excluded. For example, articles identified through the keyword 'protocol' but unrelated to IoT technology for cow health monitoring were deemed outside the scope of this research and thus excluded. In the subsequent phase, the abstracts of the articles selected during the initial screening were thoroughly reviewed. This process further refined the selection, ensuring a focus on papers directly relevant to the study's objectives. The inclusion and exclusion criteria played a significant role in this process.

The exclusion criteria applied were as follows:

• Articles not presenting novel ideas or findings were excluded.

• Papers published outside of recognized academic platforms such as conferences, journals, and symposia were omitted.

• Non-English language publications were not considered.

• Articles published prior to 2017 were excluded, aligning with the study's focus on recent developments.

• Papers not aligning with the defined searching string were disregarded.

Following these criteria, articles were chosen for a detailed review of their abstracts, ensuring the selection was highly relevant to the research scope.

Following the procedure described in the study [22], keyword analysis was conducted utilizing abstracts of the selected papers. This process entailed two stages. Initially, the main concepts and keywords in each abstract, which succinctly encapsulated the contribution of the studies, were analyzed. Subsequently, these keywords facilitated a more in-depth understanding and classification of the research contributions.

To address the defined research questions, a systematic data extraction approach was employed, aimed at generating a set of probable findings.

RQ1: For each study, the publication sources and channels were identified to respond to this question.

RQ2: Articles were categorized based on their year of publication to quantify their frequency over time.

RQ3: Research methodologies in the selected studies were categorized according to the emerging techniques, encompassing applications, solutions, systems, methods, frameworks, etc.

RQ4: The objective of this research question was to ascertain the specific sensors, devices, and applications employed in IoT cow health monitoring solutions.

This section elucidates the findings derived from the SLR questions, as delineated in Table 1. Following the established screening procedure, selected research papers have been scrutinized to elucidate responses to each research question, thereby contributing significantly to the field of IoT cow health monitoring.

An examination of the primary publication venues for research on IoT cow health monitoring has been conducted, with the findings presented in Table 3. This table enumerates the various sources and channels where articles pertinent to this field have been published.

The trend in publications related to IoT cow health monitoring from 2017 to 2023 is illustrated in Figure 2. An initial observation indicates a modest number of articles published between 2017 and 2018. However, a notable increase in research output is evident from 2019 to 2022. Given that 2023 is ongoing, it is anticipated that the number of publications will rise significantly, reflecting the escalating interest in IoT for cow health monitoring.

Figure 3 presents the methodologies employed in the selected articles to address challenges within IoT-based cow health monitoring. This visualization categorizes the research methods used, offering insights into the diverse approaches adopted in the field.

The analysis of the types of IoT devices and sensors employed in cow health monitoring is presented in Table 4. This table categorizes the studies based on the observed parameters and the specific devices or sensors used in each research.

Table 4 enumerates various IoT devices and sensors applied in the domain of cow health monitoring.

This section has systematically elucidated the array of IoT applications, sensors, and devices dedicated to cow health monitoring. The insights gleaned from this study have been coherently organized into a hierarchical structure, as depicted in Figure 4.

The hierarchy consists of three primary components. Firstly, the applications of IoT in cow health monitoring encompass monitoring, control, and tracking of bovine health conditions. Secondly, sensors and devices, often integrated into Wireless Sensor Networks (WSN), play a pivotal role in gathering essential data by sensing and tracking multiple health-related parameters. Finally, communication protocols such as ZigBee, RFID, Bluetooth, and WiFi are instrumental in transmitting the data collected by these sensors and monitoring devices for user analysis.

The transformation of the livestock industry through the application of IoT in monitoring the health of cows and other cattle presents a promising research avenue. The development of miniature, wireless sensors for the continuous monitoring of health indicators such as heart rate, body temperature, and digestive activity emerges as a crucial area of focus. The integration of advanced data analytics, underpinned by machine learning and AI, holds the potential for precise health predictions and early disease detection, thereby enhancing livestock management efficiency.

This study has provided a systematic review of the literature in the domain of IoT-based cow health monitoring. A structured methodology was employed to select thirty pertinent studies for this review. The study then delved into an analysis of various IoT sensors, devices, and applications utilized in cow health monitoring. Finally, the study highlighted potential future research directions, underscoring the challenges and unresolved issues in this field, thereby offering a roadmap for researchers.