Review
The Transformative Impact of Artificial Intelligence and Sensor Technologies on Dairy Livestock Exports
Abstract
This study explores the transformative impact of artificial intelligence (AI) and sensor technologies on dairy livestock exports. AI-based predictive analytics, automatic milking systems (AMS), and IoT sensors demonstrate significant potential for enhancing operational efficiency, animal welfare, and environmental sustainability. The research investigates the effects of these technologies on animal health management, disease detection, and monitoring while evaluating the challenges and limitations associated with their implementation. Furthermore, the discussion extends to addressing environmental stress factors caused by climate change and the effects of fluctuating global market demands. Future research directions include explainable AI (XAI), IoT and blockchain integration, ethical frameworks, climate-resilient technologies, and policy recommendations. The findings underscore the potential of AI and sensor technologies to revolutionize dairy livestock exports by fostering sustainability and productivity, emphasizing the need for collective action among stakeholders. The increasing global demand for dairy livestock exports necessitates innovative solutions to address challenges related to operational efficiency, animal welfare, and environmental sustainability. Artificial intelligence (AI) and sensor technologies, including predictive analytics, automatic milking systems (AMS), and Internet of Things (IoT) sensors, have the potential to revolutionize livestock management. This study examines the impact of these technologies on disease detection, real-time monitoring, and logistics optimization while addressing challenges such as data security, cost implications, and regulatory constraints. The discussion extends to climate change-related stress factors and global market fluctuations. Future research should focus on explainable AI (XAI), blockchain-enabled traceability, climate-resilient innovations, and policy frameworks. The findings emphasize the need for multi-stakeholder collaboration to leverage AI and sensor technologies for a sustainable and efficient dairy livestock export industry.
References
- Alshehri M. 2023. Blockchain-assisted Internet of Things framework in smart livestock farming. Internet of Things, 22: 100739.
- Amiri-Zarandi M, Dara RA, Duncan E, Fraser ED. 2022. Big data privacy in smart farming: A review. Sustainability, 14: 9120.
- Andronie M, Lăzăroiu G, Iatagan M, Hurloiu I, Dijmărescu I. 2021. Sustainable cyber-physical production systems in big data-driven smart urban economy: A systematic literature review. Sustainability, 13: 751.
- Bansal M, Singh K. 2021. Advances in smart farming technologies for animal health monitoring. Comput Ind, 128: 103482.
- Bhat SA, Huang NF, Sofi IB, Sultan M. 2021. Agriculture-food supply chain management based on blockchain and IoT: A narrative on enterprise blockchain interoperability. Agriculture, 12: 40.
- Bhattarai BP, Paudyal S, Luo Y, Mohanpurkar M, Cheung K, Tonkoski R, Hovsapian R, Myers KS, Zhang R, Zhao P, Yang C. 2019. Big data analytics in smart grids: State-of-the-art, challenges, opportunities, and future directions. IET Smart Grid, 2: 141-154.
- Brault SA, Hannon SJ, Gow SP, Otto SJ, Booker CW, Morley PS. 2019. Calculation of antimicrobial use indicators in beef feedlots—Effects of choice of metric and standardized values. Front Vet Sci, 6: 330.
- Cockburn M. 2020. Application and prospective discussion of machine learning for the management of dairy farms. Animals, 10: 1690.
- Darvazeh SS, Vanani IR, Musolu FM. 2020. Big data analytics and its applications in supply chain management. In: New trends in the use of artificial intelligence for the industry 4.0,. IntechOpen, pp: 175
- Džermeikaite K, Bacėninaite D, Antanaitis R. 2023. Innovations in cattle farming: Application of innovative technologies and sensors in the diagnosis of diseases. Animals, 13: 780.
- Farooq MS, Sohail OO, Abid A, Rasheed S. 2022. A survey on the role of IoT in agriculture for the implementation of smart livestock environment. IEEE Access, 10: 9483-9505.
- Faverjon C, Bernstein A, Grütter R, Nathues C, Nathues H, Sarasua C, Sterchi M, Vargas ME, Berezowski J. 2019. A transdisciplinary approach supporting the implementation of a big data project in livestock production: An example from the Swiss pig production industry. Front Vet Sci, 6: 215.
- Gehlot A, Malik PK, Singh R, Akram SV, Alsuwian T. 2022. Dairy 4.0: Intelligent communication ecosystem for the cattle animal welfare with blockchain and IoT enabled technologies. Appl Sci, 12: 7316.
- Ghosh S, Meena S. 2021. Precision agriculture and IoT-based monitoring systems for livestock farming: A review. Comput Electron Agric, 185: 106127.
- Grant RJ, Ferraretto LF. 2018. Silage review: Silage feeding management: Silage characteristics and dairy cow feeding behavior. J Dairy Sci, 101: 4111-4121.
- Guo F, Jin T. 2020. An overview of smart livestock farming based on IoT technologies. Journal of Sensors, 2020: 7451409.
- Haldar A, Mandal SN, Deb S, Roy R, Laishram M. 2022. Application of information and electronic technology for best practice management in livestock production system. In: Agriculture, livestock production and aquaculture: Adv Smallhol Farm Syst 2: 173-218.
- Hansen MF, Smith ML, Smith LN, Jabbar KA, Forbes D. 2018. Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device. Comput Ind, 98: 14-22.
- Hou S, Cheng X, Shi L, Zhang S. 2020. Study on individual behavior of dairy cows based on activity data and clustering. In: Proceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence, Shanghai, China, 17-19 October 2020, pp: 210-216.
- Jukan A, Masip-Bruin X, Amla N. 2017. Smart computing and sensing technologies for animal welfare: A systematic review. ACM Comput Surv, 50: 10.
- Kanjo E, Younis EM, Ang CS. 2019. Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection. Inf Fusion, 49: 46-56.
- Klerkx L, Jakku E, Labarthe P. 2019. A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS-Wageningen J Life Sci, 90: 100315.
- Koltes JE, Cole JB, Clemmens R, Dilger RN, Kramer LM, Lunney JK, McCue ME, McKay SD, Mateescu RG, Murdoch BM, et al. 2019. A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock. Front Genet, 10: 1197.
- Kothari A, Singh A. 2020. IoT-enabled smart farming system for precision agriculture. The 2020 International Conference on Electronics and Sustainable Communication Systems, July 2-4, Coimbatore, India, pp: 222-228.
- Le CS, Chen L. 2020. Applications of blockchain in agricultural and food supply chains: A review. Food Control, 112: 107102.
- Li H, Wang L. 2021. Big data and machine learning in livestock management: A survey. Agricultural Systems, 187: 103021.
- Liu H, Xu X. 2022. IoT-based smart agriculture systems: A comprehensive survey. Sensors, 22: 3969.
- Liu Y, Wu S, Xu J. 2021. Smart livestock farming: Internet of Things (IoT) solutions for animal management. Comput Electron Agric, 180: 105911.
- Marques G, Lima P, Nunes M, Silveira G. 2022. Blockchain in agriculture: A systematic review of its applications in the supply chain. Comput Electron Agric, 190: 106398.
- Meng Q, Lee KY. 2021. IoT-based predictive maintenance system for dairy farming management: Case study of smart dairy farm. Sensors, 21: 245.
- Milani R, Bonaccorsi G, Lorenzetti C. 2021. Big data applications in livestock management: Challenges and opportunities. Animals, 11: 3075.
- Mourtzis D, Vlachos E. 2020. Big data analytics in manufacturing: A review. Procedia CIRP, 88: 114-119.
- Patle S, Srivastava S. 2022. IoT-based animal welfare system: A review and future perspectives. Comput Ind, 128: pp: 103475.
- Paton C, Sparks C. 2022. Livestock productivity and health data analytics for improving farm operations and animal welfare. Front Vet Sci, 9: 882.
- Peters K, Karamanis E. 2021. A review of machine learning algorithms in livestock farming. Agricultural Systems, 187: 103002.
- Pinto A, Santos J. 2022. Blockchain for data management in dairy farming: A review. Sustainability, 14: 1785.
- Sharma S, Sharma M. 2021. Artificial intelligence in agriculture: A survey of applications and challenges. Comput Electron Agric, 178: 105760.
- Sharma S, Sharma M. 2021. Artificial intelligence in dairy farming: A review of recent advancements and applications. Artificial Intelligence Review, 54: 3149-3166.
- Sharma S, Sharma M. 2021. Blockchain and IoT in sustainable livestock farming: A new paradigm for precision livestock farming. Sustainability, 12: 4265.
- Singh R, Kaur A. 2021. A review on the IoT-based smart farming systems. Wireless Pers Commun, 116: 3199-3214.
- Singh S, Rana R. 2020. A review on smart farming systems based on IoT technologies. Int J Comput Appl, 177(15): 32-37.
- Song D, Chen Y. 2022. Machine learning-based early detection of diseases in livestock: A review. Comput Ind, 130: 103499.
- Soni P, Nand A. 2021. A review of deep learning applications in precision agriculture. Agricultural Systems, 191: 103141.
- Stojanovic J, Mitić D. 2020. IoT-based smart farming: State-of-the-art and future trends. Journal of Sensors, 2020: 4513158.
- Wang J, Zhang H. 2021. A survey of applications of big data analytics in smart agriculture. Comput Ind, 129: 103465.
- Wang S, Zhao Y. 2021. Intelligent agriculture based on IoT and machine learning: A survey. Comput Ind, 128: 103459.
- Yadav A, Gupta R. 2021. A survey on IoT-based smart farming system for livestock health monitoring. J Ambient Intell Humaniz Comput, 12: 1959-1971.
- Yang J, Wei L. 2022. Artificial intelligence in the agricultural sector: Applications and future trends. Sensors, 22: 2894.
- Yang Y, Gao Z. 2021. IoT-based monitoring system for animal welfare in dairy farming. Sensors, 21: 5867.
- Yao Z, Li M. 2021. A review on the integration of blockchain and IoT for smart farming applications. Future Gener Comput Syst, 116: 176-189.
- Yao Z, Li M. 2021. Machine learning for agricultural applications: A review on data-driven methodologies in livestock farming. Comput Ind, 129: 103463.
- Yi W, Li Z. 2022. A blockchain-based traceability system for dairy products: A case study in smart farming. Sustainability, 14: 1635.
- Zhang Q, Lin Q. 2022. A review of the applications of AI and IoT technologies in smart farming: Current status and future trends. Comput Mater Continua, 69: 3577-3596.
- Zhang W, Yu X. 2020. IoT-based automated milking systems: A comprehensive review. Comput Ind, 121: 103250.
- Zhang X, Chen W. 2021. Data-driven models for monitoring animal welfare in livestock farming. Comput Electron Agric, 187: 106229.
- Zhang Y, Li Z. 2021. AI and IoT applications in agriculture: Current and future trends. Comput Ind, 132: 103530.
- Zhao Y, Wang X. 2022. Internet of Things in precision livestock farming: A comprehensive review. Comput Ind, 131: 103521.
- Zhou X, Zhang H. 2021. Smart agriculture and IoT-based solutions for animal health and welfare management. Agricultural Systems, 189: 103014.
- Zohra F, Kamble S. 2021. Blockchain and IoT integration in dairy farming: A review and future prospects. Sustainability, 13: 4532.
The Transformative Impact of Artificial Intelligence and Sensor Technologies on Dairy Livestock Exports
Abstract
This study explores the transformative impact of artificial intelligence (AI) and sensor technologies on dairy livestock exports. AI-based predictive analytics, automatic milking systems (AMS), and IoT sensors demonstrate significant potential for enhancing operational efficiency, animal welfare, and environmental sustainability. The research investigates the effects of these technologies on animal health management, disease detection, and monitoring while evaluating the challenges and limitations associated with their implementation. Furthermore, the discussion extends to addressing environmental stress factors caused by climate change and the effects of fluctuating global market demands. Future research directions include explainable AI (XAI), IoT and blockchain integration, ethical frameworks, climate-resilient technologies, and policy recommendations. The findings underscore the potential of AI and sensor technologies to revolutionize dairy livestock exports by fostering sustainability and productivity, emphasizing the need for collective action among stakeholders. The increasing global demand for dairy livestock exports necessitates innovative solutions to address challenges related to operational efficiency, animal welfare, and environmental sustainability. Artificial intelligence (AI) and sensor technologies, including predictive analytics, automatic milking systems (AMS), and Internet of Things (IoT) sensors, have the potential to revolutionize livestock management. This study examines the impact of these technologies on disease detection, real-time monitoring, and logistics optimization while addressing challenges such as data security, cost implications, and regulatory constraints. The discussion extends to climate change-related stress factors and global market fluctuations. Future research should focus on explainable AI (XAI), blockchain-enabled traceability, climate-resilient innovations, and policy frameworks. The findings emphasize the need for multi-stakeholder collaboration to leverage AI and sensor technologies for a sustainable and efficient dairy livestock export industry.
References
- Alshehri M. 2023. Blockchain-assisted Internet of Things framework in smart livestock farming. Internet of Things, 22: 100739.
- Amiri-Zarandi M, Dara RA, Duncan E, Fraser ED. 2022. Big data privacy in smart farming: A review. Sustainability, 14: 9120.
- Andronie M, Lăzăroiu G, Iatagan M, Hurloiu I, Dijmărescu I. 2021. Sustainable cyber-physical production systems in big data-driven smart urban economy: A systematic literature review. Sustainability, 13: 751.
- Bansal M, Singh K. 2021. Advances in smart farming technologies for animal health monitoring. Comput Ind, 128: 103482.
- Bhat SA, Huang NF, Sofi IB, Sultan M. 2021. Agriculture-food supply chain management based on blockchain and IoT: A narrative on enterprise blockchain interoperability. Agriculture, 12: 40.
- Bhattarai BP, Paudyal S, Luo Y, Mohanpurkar M, Cheung K, Tonkoski R, Hovsapian R, Myers KS, Zhang R, Zhao P, Yang C. 2019. Big data analytics in smart grids: State-of-the-art, challenges, opportunities, and future directions. IET Smart Grid, 2: 141-154.
- Brault SA, Hannon SJ, Gow SP, Otto SJ, Booker CW, Morley PS. 2019. Calculation of antimicrobial use indicators in beef feedlots—Effects of choice of metric and standardized values. Front Vet Sci, 6: 330.
- Cockburn M. 2020. Application and prospective discussion of machine learning for the management of dairy farms. Animals, 10: 1690.
- Darvazeh SS, Vanani IR, Musolu FM. 2020. Big data analytics and its applications in supply chain management. In: New trends in the use of artificial intelligence for the industry 4.0,. IntechOpen, pp: 175
- Džermeikaite K, Bacėninaite D, Antanaitis R. 2023. Innovations in cattle farming: Application of innovative technologies and sensors in the diagnosis of diseases. Animals, 13: 780.
- Farooq MS, Sohail OO, Abid A, Rasheed S. 2022. A survey on the role of IoT in agriculture for the implementation of smart livestock environment. IEEE Access, 10: 9483-9505.
- Faverjon C, Bernstein A, Grütter R, Nathues C, Nathues H, Sarasua C, Sterchi M, Vargas ME, Berezowski J. 2019. A transdisciplinary approach supporting the implementation of a big data project in livestock production: An example from the Swiss pig production industry. Front Vet Sci, 6: 215.
- Gehlot A, Malik PK, Singh R, Akram SV, Alsuwian T. 2022. Dairy 4.0: Intelligent communication ecosystem for the cattle animal welfare with blockchain and IoT enabled technologies. Appl Sci, 12: 7316.
- Ghosh S, Meena S. 2021. Precision agriculture and IoT-based monitoring systems for livestock farming: A review. Comput Electron Agric, 185: 106127.
- Grant RJ, Ferraretto LF. 2018. Silage review: Silage feeding management: Silage characteristics and dairy cow feeding behavior. J Dairy Sci, 101: 4111-4121.
- Guo F, Jin T. 2020. An overview of smart livestock farming based on IoT technologies. Journal of Sensors, 2020: 7451409.
- Haldar A, Mandal SN, Deb S, Roy R, Laishram M. 2022. Application of information and electronic technology for best practice management in livestock production system. In: Agriculture, livestock production and aquaculture: Adv Smallhol Farm Syst 2: 173-218.
- Hansen MF, Smith ML, Smith LN, Jabbar KA, Forbes D. 2018. Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device. Comput Ind, 98: 14-22.
- Hou S, Cheng X, Shi L, Zhang S. 2020. Study on individual behavior of dairy cows based on activity data and clustering. In: Proceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence, Shanghai, China, 17-19 October 2020, pp: 210-216.
- Jukan A, Masip-Bruin X, Amla N. 2017. Smart computing and sensing technologies for animal welfare: A systematic review. ACM Comput Surv, 50: 10.
- Kanjo E, Younis EM, Ang CS. 2019. Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection. Inf Fusion, 49: 46-56.
- Klerkx L, Jakku E, Labarthe P. 2019. A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS-Wageningen J Life Sci, 90: 100315.
- Koltes JE, Cole JB, Clemmens R, Dilger RN, Kramer LM, Lunney JK, McCue ME, McKay SD, Mateescu RG, Murdoch BM, et al. 2019. A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock. Front Genet, 10: 1197.
- Kothari A, Singh A. 2020. IoT-enabled smart farming system for precision agriculture. The 2020 International Conference on Electronics and Sustainable Communication Systems, July 2-4, Coimbatore, India, pp: 222-228.
- Le CS, Chen L. 2020. Applications of blockchain in agricultural and food supply chains: A review. Food Control, 112: 107102.
- Li H, Wang L. 2021. Big data and machine learning in livestock management: A survey. Agricultural Systems, 187: 103021.
- Liu H, Xu X. 2022. IoT-based smart agriculture systems: A comprehensive survey. Sensors, 22: 3969.
- Liu Y, Wu S, Xu J. 2021. Smart livestock farming: Internet of Things (IoT) solutions for animal management. Comput Electron Agric, 180: 105911.
- Marques G, Lima P, Nunes M, Silveira G. 2022. Blockchain in agriculture: A systematic review of its applications in the supply chain. Comput Electron Agric, 190: 106398.
- Meng Q, Lee KY. 2021. IoT-based predictive maintenance system for dairy farming management: Case study of smart dairy farm. Sensors, 21: 245.
- Milani R, Bonaccorsi G, Lorenzetti C. 2021. Big data applications in livestock management: Challenges and opportunities. Animals, 11: 3075.
- Mourtzis D, Vlachos E. 2020. Big data analytics in manufacturing: A review. Procedia CIRP, 88: 114-119.
- Patle S, Srivastava S. 2022. IoT-based animal welfare system: A review and future perspectives. Comput Ind, 128: pp: 103475.
- Paton C, Sparks C. 2022. Livestock productivity and health data analytics for improving farm operations and animal welfare. Front Vet Sci, 9: 882.
- Peters K, Karamanis E. 2021. A review of machine learning algorithms in livestock farming. Agricultural Systems, 187: 103002.
- Pinto A, Santos J. 2022. Blockchain for data management in dairy farming: A review. Sustainability, 14: 1785.
- Sharma S, Sharma M. 2021. Artificial intelligence in agriculture: A survey of applications and challenges. Comput Electron Agric, 178: 105760.
- Sharma S, Sharma M. 2021. Artificial intelligence in dairy farming: A review of recent advancements and applications. Artificial Intelligence Review, 54: 3149-3166.
- Sharma S, Sharma M. 2021. Blockchain and IoT in sustainable livestock farming: A new paradigm for precision livestock farming. Sustainability, 12: 4265.
- Singh R, Kaur A. 2021. A review on the IoT-based smart farming systems. Wireless Pers Commun, 116: 3199-3214.
- Singh S, Rana R. 2020. A review on smart farming systems based on IoT technologies. Int J Comput Appl, 177(15): 32-37.
- Song D, Chen Y. 2022. Machine learning-based early detection of diseases in livestock: A review. Comput Ind, 130: 103499.
- Soni P, Nand A. 2021. A review of deep learning applications in precision agriculture. Agricultural Systems, 191: 103141.
- Stojanovic J, Mitić D. 2020. IoT-based smart farming: State-of-the-art and future trends. Journal of Sensors, 2020: 4513158.
- Wang J, Zhang H. 2021. A survey of applications of big data analytics in smart agriculture. Comput Ind, 129: 103465.
- Wang S, Zhao Y. 2021. Intelligent agriculture based on IoT and machine learning: A survey. Comput Ind, 128: 103459.
- Yadav A, Gupta R. 2021. A survey on IoT-based smart farming system for livestock health monitoring. J Ambient Intell Humaniz Comput, 12: 1959-1971.
- Yang J, Wei L. 2022. Artificial intelligence in the agricultural sector: Applications and future trends. Sensors, 22: 2894.
- Yang Y, Gao Z. 2021. IoT-based monitoring system for animal welfare in dairy farming. Sensors, 21: 5867.
- Yao Z, Li M. 2021. A review on the integration of blockchain and IoT for smart farming applications. Future Gener Comput Syst, 116: 176-189.
- Yao Z, Li M. 2021. Machine learning for agricultural applications: A review on data-driven methodologies in livestock farming. Comput Ind, 129: 103463.
- Yi W, Li Z. 2022. A blockchain-based traceability system for dairy products: A case study in smart farming. Sustainability, 14: 1635.
- Zhang Q, Lin Q. 2022. A review of the applications of AI and IoT technologies in smart farming: Current status and future trends. Comput Mater Continua, 69: 3577-3596.
- Zhang W, Yu X. 2020. IoT-based automated milking systems: A comprehensive review. Comput Ind, 121: 103250.
- Zhang X, Chen W. 2021. Data-driven models for monitoring animal welfare in livestock farming. Comput Electron Agric, 187: 106229.
- Zhang Y, Li Z. 2021. AI and IoT applications in agriculture: Current and future trends. Comput Ind, 132: 103530.
- Zhao Y, Wang X. 2022. Internet of Things in precision livestock farming: A comprehensive review. Comput Ind, 131: 103521.
- Zhou X, Zhang H. 2021. Smart agriculture and IoT-based solutions for animal health and welfare management. Agricultural Systems, 189: 103014.
- Zohra F, Kamble S. 2021. Blockchain and IoT integration in dairy farming: A review and future prospects. Sustainability, 13: 4532.
There are 59 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Agricultural Engineering (Other) |
| Journal Section | Reviews |
| Authors | |
| Early Pub Date | July 12, 2025 |
| Publication Date | July 15, 2025 |
| Submission Date | February 16, 2025 |
| Acceptance Date | June 17, 2025 |
| Published in Issue | Year 2025 Volume: 8 Issue: 4 |
