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

Neethirajan, Suresh

2023 Volume 23, Issue 16

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Female ; Animals ; Cattle ; Artificial Intelligence ; Livestock ; Acclimatization ; Animal Welfare ; Technology
Language	English
Publishing date	2023-08-09
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23167045
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Digital Phenotyping: A Game Changer for the Broiler Industry.

Neethirajan, Suresh

Animals : an open access journal from MDPI

2023 Volume 13, Issue 16

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2023-08-10
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2606558-7
ISSN	2076-2615
ISSN	2076-2615
DOI	10.3390/ani13162585
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Article: Automated Tracking Systems for the Assessment of Farmed Poultry.

Neethirajan, Suresh

Animals : an open access journal from MDPI

2022 Volume 12, Issue 3

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2022-01-19
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2606558-7
ISSN	2076-2615
ISSN	2076-2615
DOI	10.3390/ani12030232
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Article: Correction: Neethirajan, S. Affective State Recognition in Livestock-Artificial Intelligence Approaches.

Neethirajan, Suresh

Animals : an open access journal from MDPI

2022 Volume 12, Issue 14

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

Abstract	The authors wish to make the following correction to the original paper [...].
Language	English
Publishing date	2022-07-21
Publishing country	Switzerland
Document type	Published Erratum
ZDB-ID	2606558-7
ISSN	2076-2615
ISSN	2076-2615
DOI	10.3390/ani12141856
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Article: Affective State Recognition in Livestock-Artificial Intelligence Approaches.

Neethirajan, Suresh

Animals : an open access journal from MDPI

2022 Volume 12, Issue 6

Abstract: 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, ... ...

Abstract	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.
Language	English
Publishing date	2022-03-17
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2606558-7
ISSN	2076-2615
ISSN	2076-2615
DOI	10.3390/ani12060759
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Article ; Online: Digital Phenotyping: A Game Changer for the Broiler Industry

Neethirajan, Suresh

Animals. 2023 Aug. 10, v. 13, no. 16

2023

Abstract	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.
Keywords	animal welfare ; chicken industry ; ethics ; genomics ; phenotype ; poultry ; poultry production ; research and development ; stakeholders ; technology
Language	English
Dates of publication	2023-0810
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2606558-7
ISSN	2076-2615
ISSN	2076-2615
DOI	10.3390/ani13162585
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: The Significance and Ethics of Digital Livestock Farming

Neethirajan, Suresh

AgriEngineering. 2023 Feb. 26, v. 5, no. 1 p.488-505

2023

Abstract: The emergence of precision and digital livestock farming presents an opportunity for sustainable animal farming practices that enhance animal welfare and health. However, this transformation of modern animal farming through digital technology has several ...

Abstract	The emergence of precision and digital livestock farming presents an opportunity for sustainable animal farming practices that enhance animal welfare and health. However, this transformation of modern animal farming through digital technology has several implications for the technological, social, economic, and environmental aspects of farming. It is crucial to analyze the ethical considerations associated with the digitalization of modern animal farming, particularly in the context of human–animal relationships and potential objectification. This analysis can help develop frameworks for improving animal welfare and promoting sustainability in animal farming. One of the primary ethical concerns of digital livestock farming is the potential for a digital divide between farmers who have access to advanced technologies and those who do not. This could lead to a disparity in animal welfare and health outcomes for different groups of animals. Additionally, the use of artificial intelligence in digital livestock farming may lead to a loss of personal connection between farmers and animals, which could impact the animal’s well-being. Another ethical concern of digital livestock farming is the potential for the objectification of animals as mere data points. The use of sensors and other monitoring technologies can provide valuable data on animal health and behavior, but it is important to remember that animals are sentient beings with complex emotional and social needs. The use of digital technologies should not lead to neglect of animal welfare or a lack of human responsibility toward animals. Furthermore, social context becomes essential while integrating technologies in livestock farming to overcome ethics. By considering the cultural and societal norms of different communities, we can ensure that the use of digital technologies does not undermine these values. To address these ethical challenges, the development of standards and codes of conduct for the adoption and implementation of digital livestock farming tools and platforms can help ensure that animal welfare and sustainability are prioritized. This can help alleviate the privacy concerns of stakeholders and improve sustainability in animal farming practices. Additionally, the use of virtual and augmented reality technologies can provide a way to enhance human–animal interactions and provide more personalized care to animals, further promoting animal welfare.
Keywords	animal health ; animal welfare ; artificial intelligence ; augmented reality ; ethics ; humans ; livestock ; stakeholders
Language	English
Dates of publication	2023-0226
Size	p. 488-505.
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ISSN	2624-7402
DOI	10.3390/agriengineering5010032
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: AI in Sustainable Pig Farming: IoT Insights into Stress and Gait

Neethirajan, Suresh

Agriculture. 2023 Aug. 29, v. 13, no. 9

2023

Abstract: This paper pioneers a novel exploration of environmental impacts in livestock farming, focusing on pig farming’s intersection with climate change and sustainability. It emphasizes the transformative potential of data-driven Artificial Intelligence (AI) ... ...

Abstract	This paper pioneers a novel exploration of environmental impacts in livestock farming, focusing on pig farming’s intersection with climate change and sustainability. It emphasizes the transformative potential of data-driven Artificial Intelligence (AI) methodologies, specifically the Internet of Things (IoT) and multimodal data analysis, in promoting equitable and sustainable food systems. The study observes five pigs aged 86 to 108 days using a tripartite sensor that records heart rate, respiration rate, and accelerometer data. The unique experimental design alternates between periods of isolation during feeding and subsequent pairing, enabling the investigation of stress-induced changes. Key inquiries include discerning patterns in heart rate data during isolation versus paired settings, fluctuations in respiration rates, and behavioral shifts induced by isolation or pairing. The study also explores the potential detection of gait abnormalities, correlations between pigs’ age and their gait or activity patterns, and the evolution of pigs’ walking abilities with age. The paper scrutinizes accelerometer data to detect activity changes when pigs are paired, potentially indicating increased stress or aggression. It also examines the adaptation of pigs to alternating isolation and pairing over time and how their heart rate, respiration rate, and activity data reflect this process. The study considers other significant variables, such as time of day and isolation duration, affecting the pigs’ physiological parameters. Sensor data are further utilized to identify behavioral patterns during periods of feeding, isolation, or pairing. In conclusion, this study harnesses IoT and multimodal data analysis in a groundbreaking approach to pig welfare research. It underscores the compelling potential of technology to inform about overall pig welfare, particularly stress levels and gait quality, and the power of data-driven insights in fostering equitable, healthy, and environmentally conscious livestock production systems.
Keywords	Internet ; accelerometers ; aggression ; agriculture ; artificial intelligence ; cell respiration ; climate change ; evolution ; gait ; heart rate ; livestock production ; swine
Language	English
Dates of publication	2023-0829
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2651678-0
ISSN	2077-0472
ISSN	2077-0472
DOI	10.3390/agriculture13091706
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals

Neethirajan, Suresh

Agriculture. 2023 Feb. 13, v. 13, no. 2

2023

Abstract: Sensor-enabled big data and artificial intelligence platforms have the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased ... ...

Abstract	Sensor-enabled big data and artificial intelligence platforms have the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased interest in animal welfare, the likely reduction in the number of animals in relation to population growth in the coming decade and the growing demand for animal proteins pose an acute challenge to prioritizing animal welfare on the one hand, while maximizing the efficiency of production systems on the other. Current digital approaches do not meet these challenges due to a lack of efficient and lack of real-time non-invasive precision measurement technologies that can detect and monitor animal diseases and identify resilience in animals. In this opinion review paper, I offer a critical view of the potential of wearable sensor technologies as a unique and necessary contribution to the global market for farm animal health monitoring. To stimulate the sustainable, digital and resilient recovery of the agricultural and livestock industrial sector, there is an urgent need for testing and developing new ideas and products such as wearable sensors. By validating and demonstrating a fully functional wearable sensor prototype within an operational environment on the livestock farm that includes a miniaturized animal-borne biosensor and an artificial intelligence (AI)-based data acquisition and processing platform, the current needs, which have not yet been met, can be fulfilled. The expected quantifiable results from wearable biosensors will demonstrate that the digitization technology can perform acceptably within the performance parameters specified by the agricultural sector and under operational conditions, to measurably improve livestock productivity and health. The successful implementation of the digital wearable sensor networks would provide actionable real-time information on animal health status and can be deployed directly on the livestock farm, which will strengthen the green and digital recovery of the economy due to its significant and innovative potential.
Keywords	agricultural industry ; agriculture ; animal health ; animal welfare ; artificial intelligence ; biosensors ; data collection ; farms ; health status ; livestock ; livestock production ; livestock productivity ; population growth ; prototypes ; socioeconomics ; world markets
Language	English
Dates of publication	2023-0213
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2651678-0
ISSN	2077-0472
ISSN	2077-0472
DOI	10.3390/agriculture13020436
Database	NAL-Catalogue (AGRICOLA)

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

Neethirajan, Suresh

Frontiers in veterinary science

2021 Volume 8, Page(s) 715261

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2021-08-02
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2834243-4
ISSN	2297-1769
ISSN	2297-1769
DOI	10.3389/fvets.2021.715261
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