Behavioral Monitoring Tool for Pig Farmers: Ear Tag Sensors, Machine Intelligence, and Technology Adoption Roadmap

Pandey, Santosh; Kalwa, Upender; Kong, Taejoon; Guo, Baoqing; Gauger, Phillip C.; Peters, David J.; Yoon, Kyoung‐Jin

doi:10.3390/ani11092665

Cited by 32 publications

(20 citation statements)

References 40 publications

(62 reference statements)

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“…Regarding livestock management, precision farming principles are based on the production and management of animals, driven through sensors and data services; as demonstrated by Pandey et al, who work with ear-tag sensors and machine intelligence for the remote behavioural trail analysis of pigs [27]. In other words, precision livestock farming concerns the implementation of technologies to enable real-time monitoring for a per-animal approach.…”

Section: Ar In For Crop and Livestock Managementmentioning

confidence: 99%

Augmented Reality in Precision Farming: Concepts and Applications

2021

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The amount of arable land is limited, yet the demand for agricultural food products is increasing. This issue has led to the notion of precision farming, where smart city-based technologies (e.g., Internet of Things, digital twins, artificial intelligence) are employed in combination to cater for increased production with fewer resources. Widely used in manufacturing, augmented reality has demonstrated impactful solutions for information communication, remote monitoring and increased interaction. Yet, the technology has only recently begun to find a footing alongside precision farming solutions, despite the many benefits possible to farmers through augmenting the physical world with digital objects. Therefore, this article reflects on literature discussing current applied solutions within agriculture, where augmented realty has demonstrated a significant impact for monitoring and production. The findings discuss that augmented reality must be coupled with other technologies (e.g., simultaneous localization and mapping algorithms, global positioning systems, and sensors), specifically 9 are identified across 2 application domains (livestock and crop farming) to be beneficial. Attention is also provided on how augmented reality should be employed within agriculture, where related-work examples are drawn from in order to discuss suitable hardware approaches and constraints (e.g., mobility).

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Section: Ar In For Crop and Livestock Managementmentioning

confidence: 99%

Augmented Reality in Precision Farming: Concepts and Applications

2021

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“…In the swine industry, it is important and challenging to monitor the health and behavior of the full herd of swine. Therefore, smart tools are needed that can remotely monitor the pigs and can provide accurate information to the farm operator [1]. As the profit of swine farms relies on the normal health and behavior of swine, the industry owner demands the good health of pigs, ensuring optimum body weight, height, behavior, posture, and glossy skins, with the absence of any symptoms of diseases, malnutrition, or cataracts [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, smart tools are needed that can remotely monitor the pigs and can provide accurate information to the farm operator [1]. As the profit of swine farms relies on the normal health and behavior of swine, the industry owner demands the good health of pigs, ensuring optimum body weight, height, behavior, posture, and glossy skins, with the absence of any symptoms of diseases, malnutrition, or cataracts [1,2]. A remote monitoring tool can help to identify the health and behavior status of individual pigs, i.e., their temperature, posture, body weight, estrus, gait, abnormal behavior, etc.…”

Section: Introductionmentioning

confidence: 99%

Applications of Smart Technology as a Sustainable Strategy in Modern Swine Farming

et al. 2022

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The size of the pork market is increasing globally to meet the demand for animal protein, resulting in greater farm size for swine and creating a great challenge to swine farmers and industry owners in monitoring the farm activities and the health and behavior of the herd of swine. In addition, the growth of swine production is resulting in a changing climate pattern along with the environment, animal welfare, and human health issues, such as antimicrobial resistance, zoonosis, etc. The profit of swine farms depends on the optimum growth and good health of swine, while modern farming practices can ensure healthy swine production. To solve these issues, a future strategy should be considered with information and communication technology (ICT)-based smart swine farming, considering auto-identification, remote monitoring, feeding behavior, animal rights/welfare, zoonotic diseases, nutrition and food quality, labor management, farm operations, etc., with a view to improving meat production from the swine industry. Presently, swine farming is not only focused on the development of infrastructure but is also occupied with the application of technological knowledge for designing feeding programs, monitoring health and welfare, and the reproduction of the herd. ICT-based smart technologies, including smart ear tags, smart sensors, the Internet of Things (IoT), deep learning, big data, and robotics systems, can take part directly in the operation of farm activities, and have been proven to be effective tools for collecting, processing, and analyzing data from farms. In this review, which considers the beneficial role of smart technologies in swine farming, we suggest that smart technologies should be applied in the swine industry. Thus, the future swine industry should be automated, considering sustainability and productivity.

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“…The application of machine learning tools in farm animals has been accompanied by the development of precision livestock farming tools in the recent years and could be of help for the identification of key markers associated with a specific health status. This approach has been previously and recently used for the detection of factors associated with porcine reproductive and respiratory syndrome virus (PRRSV) outbreaks ( 25 ) or to study the behavioral traits for animal well-being assessment ( 26 ). One of the goals of precision livestock farming is the early detection of illness or physiological status at the farm level, so there is a niche for machine learning approaches within animal production ( 27 ).…”

Section: Introductionmentioning

confidence: 99%

The Connection Between Stress and Immune Status in Pigs: A First Salivary Analytical Panel for Disease Differentiation

et al. 2022

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This paper analyzes the association between stress and immune response activations in different diseases, based on the salivary analytics. Moreover, a first attempt to discriminate between diseases was performed by principal component analysis. The salivary analytics consisted of the measurement of psychosocial stress (cortisol and salivary alpha-amylase) indicators, innate (acute phase proteins: C-reactive protein and haptoglobin), and adaptive immune (adenosine deaminase, Cu and Zn) markers and oxidative stress parameters (antioxidant capacity and oxidative status). A total of 107 commercial growing pigs in the field were divided into six groups according to the signs of disease after proper veterinary clinical examination, especially, healthy pigs, pigs with rectal prolapse, tail-biting lesions, diarrhea, lameness, or dyspnea. Associations between stress and immune markers were observed with different intensities. High associations (r = 0.61) were observed between oxidative stress markers and adaptive immune markers. On the other hand, moderate associations (r = 0.31–0.48) between psychosocial stress markers with both innate and adaptive immune markers were observed. All pathological conditions showed statistically significant differences in at least 4 out of the 11 salivary markers studied, with no individual marker dysregulated in all the diseases. Moreover, each disease condition showed differences in the degree of activation of the analyzed systems which could be used to create different salivary profiles. A total of two dimensions were selected through the principal component analysis to explain the 48.3% of the variance of our data. Lameness and rectal prolapse were the two pathological conditions most distant from the healthy condition followed by dyspnea. Tail-biting lesions and diarrhea were also far from the other diseases but near to healthy animals. There is still room for improvements, but these preliminary results displayed a great potential for disease detection and characterization using salivary biomarkers profiling in the near future.

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Behavioral Monitoring Tool for Pig Farmers: Ear Tag Sensors, Machine Intelligence, and Technology Adoption Roadmap

Cited by 32 publications

References 40 publications

Augmented Reality in Precision Farming: Concepts and Applications

Augmented Reality in Precision Farming: Concepts and Applications

Applications of Smart Technology as a Sustainable Strategy in Modern Swine Farming

The Connection Between Stress and Immune Status in Pigs: A First Salivary Analytical Panel for Disease Differentiation

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