Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation.

This technical note critically evaluates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the swiftly evolving dairy livestock export industry. We focus on the novel application of the Internet of Things (IoT) in long-distance livestock transportation, particularly in livestock enumeration and identification for precise traceability. Technological advancements in identifying behavioral patterns in 'shy feeder' cows and real-time weight monitoring enhance the accuracy of long-haul livestock transportation. These innovations offer benefits such as improved animal welfare standards, reduced supply chain inaccuracies, and increased operational productivity, expanding market access and enhancing global competitiveness. However, these technologies present challenges, including individual animal customization, economic analysis, data security, privacy, technological adaptability, training, stakeholder engagement, and sustainability concerns. These challenges intertwine with broader ethical considerations around animal treatment, data misuse, and the environmental impacts. By providing a strategic framework for successful technology integration, we emphasize the importance of continuous adaptation and learning. This note underscores the potential of AI, IoT, and sensor technologies to shape the future of the dairy livestock export industry, contributing to a more sustainable and efficient global dairy sector.

Digital Phenotyping: A Game Changer for the Broiler Industry.

In response to escalating global demand for poultry, the industry grapples with an array of intricate challenges, from enhancing productivity to improving animal welfare and attenuating environmental impacts. This comprehensive review explores the transformative potential of digital phenotyping, an emergent technological innovation at the cusp of dramatically reshaping broiler production. The central aim of this study is to critically examine digital phenotyping as a pivotal solution to these multidimensional industry conundrums. Our investigation spotlights the profound implications of 'digital twins' in the burgeoning field of broiler genomics, where the production of exact digital counterparts of physical entities accelerates genomics research and its practical applications. Further, this review probes into the ongoing advancements in the research and development of a context-sensitive, multimodal digital phenotyping platform, custom-built to monitor broiler health. This paper critically evaluates this platform's potential in revolutionizing health monitoring, fortifying the resilience of broiler production, and fostering a harmonious balance between productivity and sustainability. Subsequently, the paper provides a rigorous assessment of the unique challenges that may surface during the integration of digital phenotyping within the industry. These span from technical and economic impediments to ethical deliberations, thus offering a comprehensive perspective. The paper concludes by highlighting the game-changing potential of digital phenotyping in the broiler industry and identifying potential future directions for the field, underlining the significance of continued research and development in unlocking digital phenotyping's full potential. In doing so, it charts a course towards a more robust, sustainable, and productive broiler industry. The insights garnered from this study hold substantial value for a broad spectrum of stakeholders in the broiler industry, setting the stage for an imminent technological evolution in poultry production.

DISubNet: Depthwise Separable Inception Subnetwork for Pig Treatment Classification Using Thermal Data.

Thermal imaging is increasingly used in poultry, swine, and dairy animal husbandry to detect disease and distress. In intensive pig production systems, early detection of health and welfare issues is crucial for timely intervention. Using thermal imaging for pig treatment classification can improve animal welfare and promote sustainable pig production. In this paper, we present a depthwise separable inception subnetwork (DISubNet), a lightweight model for classifying four pig treatments. Based on the modified model architecture, we propose two DISubNet versions: DISubNetV1 and DISubNetV2. Our proposed models are compared to other deep learning models commonly employed for image classification. The thermal dataset captured by a forward-looking infrared (FLIR) camera is used to train these models. The experimental results demonstrate that the proposed models for thermal images of various pig treatments outperform other models. In addition, both proposed models achieve approximately 99.96-99.98% classification accuracy with fewer parameters.

Correction: Neethirajan, S. Affective State Recognition in Livestock-Artificial Intelligence Approaches. Animals 2022, 12, 759.

The authors wish to make the following correction to the original paper [...].

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production.

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.

Interest and investment in precision technologies are growing within the livestock sector. Though these technologies offer many promises of increased efficiency and reduced inputs, there is a need to assess the opportunities and challenges associated with precision technology implementation in livestock production systems. In this review, precision livestock measurement and management tools are explained in the context of a logical and iterative five-step process that highlights the need for systems computer modeling to realize anticipated benefits from these technologies and avoid unintended consequences. This review includes key case studies to highlight past challenges and current opportunities within operations that house animals in a central area or building with sufficient infrastructure (confined livestock production systems) and other operation settings that utilize large grasslands that contain far less infrastructure (extensive livestock production systems). The key to precision livestock management success is training the next generation of animal scientists in computer modeling, precision technologies, computer programming, and data science while still being grounded in traditional animal science principles.

ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences.

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ("big data") and the exponential increase in computing power have allowed the appearance of "new" modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine "old" and "new" modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.

Modeling in the animal sciences has received a boost by large-scale adoption of sensor technology, increased computing power, and the further development of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) models. Together with open-source programming languages, extensive modeling libraries, and heavy marketing, modeling reached a larger audience via AI. However, like most technological innovations, AI overpromised. By adopting an almost singular model-centric view to solving business needs, models failed to integrate with existing business processes. Models, especially AI, need data and both need humans. Together, they need room to learn and fail and by offering them as the end-solution to a problem, they are unable to act as sparring partners for all relevant stakeholders. In this article, we highlight fundamental model limitations exemplified via AI, and we offer solutions toward a more sustainable adoption of AI as a catalyst for modeling. This means sharing data and code and placing a more realistic view on models. Universities and industry both play a fundamental role in offering technological prowess and business experience to the future modeler. People, not technology, are the key to a more successful adoption of models.

Affective State Recognition in Livestock-Artificial Intelligence Approaches.

Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated 'benchmarks' for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, 'digital twins' of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.

Automated Tracking Systems for the Assessment of Farmed Poultry.

The world's growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance its efficiency and quality of production. One of the most commonly farmed livestock is poultry and their significance is felt on a global scale. Current poultry farming practices result in the premature death and rejection of billions of chickens on an annual basis before they are processed for meat. This loss of life is concerning regarding animal welfare, agricultural efficiency, and economic impacts. The best way to prevent these losses is through the individualistic and/or group level assessment of animals on a continuous basis. On large-scale farms, such attention to detail was generally considered to be inaccurate and inefficient, but with the integration of artificial intelligence (AI)-assisted technology individualised, and per-herd assessments of livestock became possible and accurate. Various studies have shown that cameras linked with specialised systems of AI can properly analyse flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on recent advancements, this review explores the aspects of AI in the detection, counting, and tracking of poultry in commercial and research-based applications.

Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming.

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics (digital avatars or metaverse) where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby productivity, and sustainability of the farming industry. The interactive 3D avatars and the digital twins of farm animals enabled by deepfake technology offers a timely and essential way in the digital transformation toward exploring the subtle nuances of animal behavior and cognition in enhancing farm animal welfare. Without offering conclusive remarks, the presented mini review is exploratory in nature due to the nascent stages of the deepfake technology.

Digital Phenotyping in Livestock Farming.

Currently, large volumes of data are being collected on farms using multimodal sensor technologies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored efficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly addressing farm animals' individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future research is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.

The Use of Artificial Intelligence in Assessing Affective States in Livestock.

In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.

Digital Twins in Livestock Farming.

Artificial intelligence (AI), machine learning (ML) and big data are consistently called upon to analyze and comprehend many facets of modern daily life. AI and ML in particular are widely used in animal husbandry to monitor both the animals and environment around the clock, which leads to a better understanding of animal behavior and distress, disease control and prevention, and effective business decisions for the farmer. One particularly promising area that advances upon AI is digital twin technology, which is currently used to improve efficiencies and reduce costs across multiple industries and sectors. In contrast to a model, a digital twin is a digital replica of a real-world entity that is kept current with a constant influx of data. The application of digital twins within the livestock farming sector is the next frontier and has the potential to be used to improve large-scale precision livestock farming practices, machinery and equipment usage, and the health and well-being of a wide variety of farm animals. The mental and emotional states of animals can be monitored using recognition technology that examines facial features, such as ear postures and eye white regions. Used with modeling, simulation and augmented reality technologies, digital twins can help farmers to build more energy-efficient housing structures, predict heat cycles for breeding, discourage negative behaviors of livestock, and potentially much more. As with all disruptive technological advances, the implementation of digital twin technology will demand a thorough cost and benefit analysis of individual farms. Our goal in this review is to assess the progress toward the use of digital twin technology in livestock farming, with the goal of revolutionizing animal husbandry in the future.

Social Network Analysis in Farm Animals: Sensor-Based Approaches.

Natural social systems within animal groups are an essential aspect of agricultural optimization and livestock management strategy. Assessing elements of animal behaviour under domesticated conditions in comparison to natural behaviours found in wild settings has the potential to address issues of animal welfare effectively, such as focusing on reproduction and production success. This review discusses and evaluates to what extent social network analysis (SNA) can be incorporated with sensor-based data collection methods, and what impact the results may have concerning welfare assessment and future farm management processes. The effectiveness and critical features of automated sensor-based technologies deployed in farms include tools for measuring animal social group interactions and the monitoring and recording of farm animal behaviour using SNA. Comparative analyses between the quality of sensor-collected data and traditional observational methods provide an enhanced understanding of the behavioural dynamics of farm animals. The effectiveness of sensor-based approaches in data collection for farm animal behaviour measurement offers unique opportunities for social network research. Sensor-enabled data in livestock SNA addresses the biological aspects of animal behaviour via remote real-time data collection, and the results both directly and indirectly influence welfare assessments, and farm management processes. Finally, we conclude with potential implications of SNA on modern animal farming for improvement of animal welfare.

Measuring Farm Animal Emotions-Sensor-Based Approaches.

Understanding animal emotions is a key to unlocking methods for improving animal welfare. Currently there are no 'benchmarks' or any scientific assessments available for measuring and quantifying the emotional responses of farm animals. Using sensors to collect biometric data as a means of measuring animal emotions is a topic of growing interest in agricultural technology. Here we reviewed several aspects of the use of sensor-based approaches in monitoring animal emotions, beginning with an introduction on animal emotions. Then we reviewed some of the available technological systems for analyzing animal emotions. These systems include a variety of sensors, the algorithms used to process biometric data taken from these sensors, facial expression, and sound analysis. We conclude that a single emotional expression measurement based on either the facial feature of animals or the physiological functions cannot show accurately the farm animal's emotional changes, and hence compound expression recognition measurement is required. We propose some novel ways to combine sensor technologies through sensor fusion into efficient systems for monitoring and measuring the animals' compound expression of emotions. Finally, we explore future perspectives in the field, including challenges and opportunities.

Transforming the Adaptation Physiology of Farm Animals through Sensors.

Despite recent scientific advancements, there is a gap in the use of technology to measure signals, behaviors, and processes of adaptation physiology of farm animals. Sensors present exciting opportunities for sustained, real-time, non-intrusive measurement of farm animal behavioral, mental, and physiological parameters with the integration of nanotechnology and instrumentation. This paper critically reviews the sensing technology and sensor data-based models used to explore biological systems such as animal behavior, energy metabolism, epidemiology, immunity, health, and animal reproduction. The use of sensor technology to assess physiological parameters can provide tremendous benefits and tools to overcome and minimize production losses while making positive contributions to animal welfare. Of course, sensor technology is not free from challenges; these devices are at times highly sensitive and prone to damage from dirt, dust, sunlight, color, fur, feathers, and environmental forces. Rural farmers unfamiliar with the technologies must be convinced and taught to use sensor-based technologies in farming and livestock management. While there is no doubt that demand will grow for non-invasive sensor-based technologies that require minimum contact with animals and can provide remote access to data, their true success lies in the acceptance of these technologies by the livestock industry.

Noninvasive Label-Free Detection of Cortisol and Lactate Using Graphene Embedded Screen-Printed Electrode.

ABSTRACT: A sensitive and specific immunosensor for the detection of the hormones cortisol and lactate in human or animal biological fluids, such as sweat and saliva, was devised using the label-free electrochemical chronoamperometric technique. By using these fluids instead of blood, the biosensor becomes noninvasive and is less stressful to the end user, who may be a small child or a farm animal. Electroreduced graphene oxide (e-RGO) was used as a synergistic platform for signal amplification and template for bioconjugation for the sensing mechanism on a screen-printed electrode. The cortisol and lactate antibodies were bioconjugated to the e-RGO using covalent carbodiimide chemistry. Label-free electrochemical chronoamperometric detection was used to analyze the response to the desired biomolecules over the wide detection range. A detection limit of 0.1 ng mL-1 for cortisol and 0.1 mM for lactate was established and a correlation between concentration and current was observed. A portable, handheld potentiostat assembled with Bluetooth communication and battery operation enables the developed system for point-of-care applications. A sandwich-like structure containing the sensing mechanisms as a prototype was designed to secure the biosensor to skin and use capillary action to draw sweat or other fluids toward the sensing mechanism. Overall, the immunosensor shows remarkable specificity, sensitivity as well as the noninvasive and point-of-care capabilities and allows the biosensor to be used as a versatile sensing platform in both developed and developing countries.

Chiral zirconium quantum dots: A new class of nanocrystals for optical detection of coronavirus.

A synthetic way of chiral zirconium quantum dots (Zr QDs) was presented for the first time using L(+)-ascorbic acid acts as a surface as well as chiral ligands. Different spectroscopic and microscopic analysis was performed for thorough characterization of Zr QDs. As-synthesized QDs exhibited fluorescence and circular dichroism properties, and the peaks were located at 412 nm and 352 nm, respectively. MTT assay was performed to test the cytotoxicity of the synthesized Zr QDs against rat brain glioma C6 cells. Synthesized QDs was further conjugated with anti-infectious bronchitis virus (IBV) antibodies of coronavirus to form an immunolink at the presence of the target analyte and anti-IBV antibody-conjugated magneto-plasmonic nanoparticles (MPNPs). The fluorescence properties of immuno-conjugated QD-MP NPs nanohybrids through separation by an external magnetic field enabled biosensing of coronavirus with a limit of detection of 79.15 EID/50 µL.

A 2D transition-metal dichalcogenide MoS2 based novel nanocomposite and nanocarrier for multiplex miRNA detection.

Nanoscale MoS2 has attracted extensive attention for sensing due to its superior properties. This study outlines a microfluidic and electrochemical biosensing methodology for the multiplex detection of paratuberculosis-specific miRNAs. Herein, we report the synthesis of MoS2 nanosheets decorated with a copper ferrite (CuFe2O4) nanoparticle composite and molecular probe immobilized MoS2 nanosheets as nanocarriers for the electrochemical detection of miRNAs. Paratuberculosis is a bacterial infection of the intestinal tract of dairy cattle, and is a cause of substantial economic and animal losses all over the world. The designed biosensing electrode was modified with the synthesized MoS2-CuFe2O4 nanocomposites for a highly amplified signal generation. Additionally, selective detection of miRNAs was accomplished by functionalizing the MoS2 nanosheets with a miRNA-specific biotin-tagged thiolated molecular probe and ferrocene thiol. The presence of target miRNA triggered the opening of the molecular probe present on the nanocarriers. The interaction of the molecular probe and miRNA resulted in an increase in the electrochemical signal from ferrocene. The optimized microfluidic biosensor was employed to detect a range of miRNA concentrations from the target analyte. Using square wave voltammetric analysis, a detection limit of 0.48 pM was calculated, with a detection range of 1 pM to 1.5 nM. The application of the biosensor was also assessed by detecting miRNAs in spiked serum and positive clinical samples. The developed nanomaterial enabled biosensor easily discriminated between the target miRNAs and other interfering molecules. The developed microfluidic biosensor has the potential to be used as a point-of-care, miRNA based diagnostic tool for paratuberculosis in dairy cows.

Biosensors for Sustainable Food Engineering: Challenges and Perspectives.

Current food production faces tremendous challenges from growing human population, maintaining clean resources and food qualities, and protecting climate and environment. Food sustainability is mostly a cooperative effort resulting in technology development supported by both governments and enterprises. Multiple attempts have been promoted in tackling challenges and enhancing drivers in food production. Biosensors and biosensing technologies with their applications, are being widely applied to tackling top challenges in food production and its sustainability. Consequently, a growing demand in biosensing technologies exists in food sustainability. Microfluidics represents a technological system integrating multiple technologies. Nanomaterials, with its technology in biosensing, is thought to be the most promising tool in dealing with health, energy, and environmental issues closely related to world populations. The demand of point of care (POC) technologies in this area focus on rapid, simple, accurate, portable, and low-cost analytical instruments. This review provides current viewpoints from the literature on biosensing in food production, food processing, safety and security, food packaging and supply chain, food waste processing, food quality assurance, and food engineering. The current understanding of progress, solution, and future challenges, as well as the commercialization of biosensors are summarized.

Exploration of two-dimensional bio-functionalized phosphorene nanosheets (black phosphorous) for label free haptoglobin electro-immunosensing applications.

We report on the development of an antibody-functionalized interface based on electrochemically active liquid-exfoliated two-dimensional phosphorene (Ph) nanosheets-also known as black phosphorous nanosheets-for the label-free electrochemical immunosensing of a haptoglobin (Hp) biomarker, a clinical marker of severe inflammation. The electrodeposition has been achieved over the screen-printed electrode (SPE) using liquid-assisted ultrasonically exfoliated black phosphorus nanosheets. Subsequently, Ph-SPEs bioconjugated with Hp antibodies (Ab), using electrostatic interactions via a poly-L-lysine linker for biointerface development. Electrochemical analysis demonstrates that the Ab-modified Ph-SPEs (Ab@Ph-SPE) exhibit enhanced electroconducting behavior as compared to the pristine electrodes. This Ab-functionalized phosphorene-based electrochemical immunosensor platform has demonstrated remarkable sensitivity and specificity, having a dynamic linear response range from 0.01-10 mg ml-1 for Hp in standard and serum samples with a low detection limit (∼0.011 mg ml-1) using the label-free electrochemical technique. The sensor electrodes were also studied with other closely relative interferents to investigate cross reactivity and specificity. This strategy opens up avenues to POC (point-of-care) and on-farm livestock disease monitoring technologies for multiplexed diagnosis in complex biological samples such as serum. The technique is simple in fabrication and provides an analytical response in less than 60 s.