Dairy 4.0: Intelligent Communication Ecosystem for the Cattle Animal Welfare with Blockchain and IoT Enabled Technologies
Source abstract
Abstract
An intelligent ecosystem with real-time wireless technology is now playing a key role in meeting the sustainability requirements set by the United Nations. Dairy cattle are a major source of milk production all over the world. To meet the food demand of the growing population with maximum productivity, it is necessary for dairy farmers to adopt real-time monitoring technologies. In this study, we will be exploring and assimilating the limitless possibilities for technological interventions in dairy cattle to drastically improve their ecosystem. Intelligent systems for sensing, monitoring, and methods for analysis to be used in applications such as animal health monitoring, animal location tracking, milk quality, and supply chain, feed monitoring and safety, etc., have been discussed briefly. Furthermore, generalized architecture has been proposed that can be directly applied in the future for breakthroughs in research and development linked to data gathering and the processing of applications through edge devices, robots, drones, and blockchain for building intelligent ecosystems. In addition, the article discusses the possibilities and challenges of implementing previous techniques for different activities in dairy cattle. High computing power-based wearable devices, renewable energy harvesting, drone-based furious animal attack detection, and blockchain with IoT assisted systems for the milk supply chain are the vital recommendations addressed in this study for the effective implementation of the intelligent ecosystem in dairy cattle.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: UAV Applications and Optimization · Smart Agriculture and AI · Food Supply Chain Traceability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Division of Research & Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar 144009, India
Law College of Dehradun, Uttaranchal University, Dehradun 248007, India
Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia
References (99)
Show source-provided references
1. (2022, January 06). Sustainable Development Goals|United Nations Development Programme. Available online: https://www.undp.org/sustainable-development-goals.
2. (2022, January 06). How the Dairy Sector Supports the UN Sustainable Development Goals|Danish Dairy Board. Available online: https://danishdairyboard.dk/products/sustainable-development-goals/.
3. Milk nutritional composition and its role in human health10.1016/j.nut.2013.10.011 ↗
4. (2022, January 06). India: Increasing Demand Challenges the Dairy Sector. Available online: https://www.fao.org/3/i0588e/i0588e05.htm.
5. (2022, January 06). Sustainable Dairy in India|Virginia Tech CALS Global. Available online: https://globalagriculturalproductivity.org/sustainably-meeting-dairy-demand-in-india/.
6. The food and agriculture organization of the United Nations10.1017/S0020818300006688 ↗
7. Smallholder dairy cattle feeding technologies and practices in Tanzania: Failures, successes, challenges and prospects for sustainability10.1080/14735903.2018.1440474 ↗
8. (2022, January 06). Dairy Production and Products: Farm Practices. Available online: https://www.fao.org/dairy-production-products/production/farm-practices/en/.
9. Digitalization and Big data in smart farming—A review
10. Gill, S.S. (2022). A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions. Innovations in Communication and Computing, EAI/Springer.10.1007/978-3-030-74402-1_13 ↗
11. Kalyani, Y., and Collier, R. (2021). A systematic survey on the role of cloud, fog and edge computing combination in smart agriculture. Sensors, 21.10.3390/s21175922 ↗
12. (2022, January 07). Statistics. Available online: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx.
13. India Data Center Market (2022, January 07). Size, Share and Global Market Forecast to 2022|Markets and Markets. Available online: https://www.marketsandmarkets.com/Market-Reports/india-data-center-market-173006400.html?gclid=CjwKCAjwq832BRA5EiwACvCWsf67cZbBoLrRe8YWerQw-.
14. Ferrández-Pastor, F.J., Mora, H., Jimeno-Morenilla, A., and Volckaert, B. (2018). Deployment of IoT edge and fog computing technologies to develop smart building services. Sustainability, 10.10.3390/su10113832 ↗
15. (2022, January 07). Measuring Digital Development: Facts and Figures 2021. Available online: https://www.itu.int/en/ITU-D/Statistics/Pages/facts/default.aspx.
16. Artificial Intelligence-Based Sensors for Next Generation IoT Applications: A Review10.1109/JSEN.2021.3055618 ↗
17. Survey on collaborative smart drones and internet of things for improving smartness of smart cities10.1109/ACCESS.2019.2934998 ↗
18. Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues10.1016/j.comnet.2018.07.017 ↗
19. Singh, R., Gehlot, A., Akram, S.V., Thakur, A.K., Buddhi, D., and Das, P.K. (2021). Forest 4.0: Digitalization of forest using the Internet of Things (IoT). J. King Saud Univ. Inf. Sci., 1–15.10.1016/j.jksuci.2021.02.009 ↗
20. Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future10.1016/j.jclepro.2020.122877 ↗
21. Artificial intelligence in information systems research: A systematic literature review and research agenda10.1016/j.ijinfomgt.2021.102383 ↗
22. Analysis of high-rise building safety detection methods based on big data and artificial intelligence10.1177/1550147720935307 ↗
23. Huang, Q., Lu, C., and Chen, K. (2017). Smart building applications and information system hardware co-design. Big Data Analytics for Sensor-Network Collected Intelligence, Elsevier.10.1016/B978-0-12-809393-1.00011-8 ↗
24. A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security10.1109/COMST.2020.2988293 ↗
25. Machine learning and data analytics for the IoT10.1007/s00521-020-04874-y ↗
26. Deep learning for intelligent IoT: Opportunities, challenges and solutions10.1016/j.comcom.2020.08.017 ↗
27. Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions10.1016/j.cosrev.2020.100303 ↗
28. Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications10.1109/TWC.2017.2751045 ↗
29. Lagkas, T., Argyriou, V., Bibi, S., and Sarigiannidis, P. (2018). UAV IoT framework views and challenges: Towards protecting drones as “things”. Sensors, 18.10.3390/s18114015 ↗
30. Farnham, T., Jones, S., Aijaz, A., Jin, Y., Mavromatis, I., Raza, U., Portelli, A., and Stanoev, A. (2021, January 9–12). UMBRELLA Collaborative Robotics Testbed and IoT Platform. Proceedings of the 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA.10.1109/CCNC49032.2021.9369615 ↗
31. Patel, A.R., Azadi, S., Babaee, M.H., Mollaei, N., Patel, K.L., and Mehta, D.R. (2018, January 16–18). Significance of Robotics in Manufacturing, Energy, Goods and Transport Sector in Internet of Things (IoT) Paradigm. Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India.10.1109/ICCUBEA.2018.8697488 ↗
32. A Review of Methods to Measure Animal Body Temperature in Precision Farming
33. Wearable wireless temperature sensor nodes appressed to base of a calf’s tail
34. ‘SWEATCH’: A Wearable Platform for Harvesting and Analysing Sweat Sodium Content10.1002/elan.201600106 ↗
35. Bioanalytical devices: Technological leap for sweat sensing10.1038/529475a ↗
36. Wearable Sensor System Powered by a Biofuel Cell for Detection of Lactate Levels in Sweat10.1149/2.0131608jss ↗
37. Stress detection and classification of laying hens by sound analysis10.5713/ajas.14.0654 ↗
38. Lameness detection in dairy cows: Part 2. Use of sensors to automatically register changes in locomotion or behavior10.3390/ani5030388 ↗
39. Sa, J., Ju, M., Han, S., Kim, H., Chung, Y., and Park, D. (2015, January 18–19). Detection of low-weight pigs by using a top-view camera. Proceedings of the Fourth International Conference on Information Science and Cloud Computing, Guangzhou, China.
40. A wearable fingernail chemical sensing platform: PH sensing at your fingertips10.1016/j.talanta.2015.12.083 ↗
41. Recent advancement in biosensors technology for animal and livestock health management10.1016/j.bios.2017.07.015 ↗
42. Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings: A pilot study10.1186/s12917-017-1249-3 ↗
43. SERS based point-of-care detection of food-borne pathogens10.1007/s00604-015-1698-y ↗
44. Design and test of a low-cost electronic nose system for identification of Salmonella enterica in poultry manure
45. Mellor, D.J., Beausoleil, N.J., Littlewood, K.E., McLean, A.N., McGreevy, P.D., Jones, B., and Wilkins, C. (2020). The 2020 five domains model: Including human–animal interactions in assessments of animal welfare. Animals, 10.10.3390/ani10101870 ↗
46. Dairy intensification: Drivers, impacts and alternatives10.1007/s13280-019-01177-y ↗
47. French, F. (2017, January 21–23). FarmJam 2017: Designing Enrichment for Farm Animals. Proceedings of the 4th International Conference on Animal-Computer Interaction (ACI 2017), Milton Keynes, UK.10.1145/3152130.3152154 ↗
48. Looking into the eyes of a cow: Can eye whites be used as a measure of emotional state?10.1016/j.applanim.2016.11.005 ↗
49. Nasal temperatures in dairy cows are influenced by positive emotional state10.1016/j.physbeh.2014.11.011 ↗
50. Vocal production in postpartum dairy cows: Temporal organization and association with maternal and stress behaviors10.3168/jds.2020-18891 ↗
51. Evaluation of visible eye white and maximum eye temperature as non-invasive indicators of stress in dairy cows10.1016/j.applanim.2017.10.001 ↗
52. Facial expression and oxytocin as possible markers of positive emotions in horses10.1038/s41598-018-32993-z ↗
53. Recent advances in wearable sensors for animal health management10.1016/j.sbsr.2016.11.004 ↗
54. The role of sensors, big data and machine learning in modern animal farming10.1016/j.sbsr.2020.100367 ↗
55. NeethiraJanuary, S., Reimert, I., and Kemp, B. (2021). Measuring farm animal emotions—Sensor-based approaches. Sensors, 21.10.3390/s21020553 ↗
56. Samadiani, N., Huang, G., Cai, B., Luo, W., Chi, C.H., Xiang, Y., and He, J. (2019). A review on automatic facial expression recognition systems assisted by multimodal sensor data. Sensors, 19.10.3390/s19081863 ↗
57. Measuring the evolution of facial ‘expression’ using multi-species FACS10.1016/j.neubiorev.2020.02.031 ↗
58. Integration of Ensemble and Evolutionary Machine Learning Algorithms for Monitoring Diver Behavior Using Physiological Signals10.1109/ACCESS.2019.2926444 ↗
59. Balducci, F., Impedovo, D., and Pirlo, G. (2018). Machine learning applications on agricultural datasets for smart farm enhancement. Machines, 6.10.3390/machines6030038 ↗
60. Tang, Z., Luo, L., Xie, B., Zhu, Y., Zhao, R., Bi, L., and Lu, C. (2022). Automatic Sparse Connectivity Learning for Neural Networks. IEEE Trans. Neural Netw. Learn. Syst.10.1109/TNNLS.2022.3141665 ↗
61. Improving the generalization ability of deep neural networks for cross-domain visual recognition10.1109/TCDS.2020.2965166 ↗
62. Comprehensive analysis of affiliative and agonistic social networks in lactating dairy cattle groups10.1016/j.applanim.2018.10.016 ↗
63. Leliveld, L.M.C., and Provolo, G. (2020). A review of welfare indicators of indoor-housed dairy cow as a basis for integrated automatic welfare assessment systems. Animals, 10.10.3390/ani10081430 ↗
64. Validation of a real-time location system for zone assignment and neighbor detection in dairy cow groups10.1016/j.compag.2021.106280 ↗
65. Early modification of the circadian organization of cow activity in relation to disease or estrus10.3168/jds.2016-11853 ↗
66. Localisation and identification performances of a real-time location system based on ultra wide band technology for monitoring and tracking dairy cow behaviour in a semi-open free-stall barn10.1016/j.compag.2014.08.001 ↗
67. Wurtz, K., Camerlink, I., D’Eath, R.B., Fernández, A.P., Norton, T., Steibel, J., and Siegford, J. (2019). Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review. PLoS ONE, 14.10.1371/journal.pone.0226669 ↗
68. Tracking group housed sows with an ultra-wideband indoor positioning system: A feasibility study10.1016/j.biosystemseng.2020.09.011 ↗
69. An evaluation of indoor location determination technologies10.1080/17489725.2011.562927 ↗
70. Integration of animal tracking and health monitoring systems10.1088/1757-899X/1098/4/042075 ↗
71. (2022, January 13). LoRaWAN GPS Tracker with 9-axis Accelerometer-LGT92-868MHz—Seeed Studio. Available online: https://www.seeedstudio.com/LoRaWAN-GPS-Tracker-with-9-axis-accelerometer-LGT92-p-2922.html.
72. Dairy farmers with larger herd sizes adopt more precision dairy technologies10.3168/jds.2017-13324 ↗
73. Milk quality along dairy farming systems and associated value chains in Kenya: An analysis of composition, contamination and adulteration10.1016/j.foodcont.2020.107482 ↗
74. Plaza, J., Revilla, I., Nieto, J., Hidalgo, C., Sánchez-García, M., and Palacios, C. (2021). Milk Quality and Carbon Footprint Indicators of Dairy Sheep Farms Depend on Grazing Level and Identify the Different Management Systems. Animals, 11.10.3390/ani11051426 ↗
75. Saravanan, S., Kavinkumar, M., Kokul, N.S., Krishna, N.S., and Nitheeshkumar, V.I. (2021, January 6–8). Smart Milk Quality Analysis and Grading Using IoT. Proceedings of the 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.10.1109/ICICCS51141.2021.9432298 ↗
76. Design of a Low Cost System for Determination of Fat Using Iot and Ml10.1088/1742-6596/1969/1/012034 ↗
77. Towards combining data prediction and internet of things to manage milk production on dairy cows10.1016/j.compag.2019.105156 ↗
78. Gastélum-Barrios, A., Soto-Zarazúa, G.M., Escamilla-García, A., Toledano-Ayala, M., Macías-Bobadilla, G., and Jauregui-Vazquez, D. (2020). Optical methods based on ultraviolet, visible, and near-infrared spectra to estimate fat and protein in raw milk: A review. Sensors, 20.10.3390/s20123356 ↗
79. On-line application of near infrared (NIR) spectroscopy in food production10.1016/j.tifs.2015.10.002 ↗
80. The current status of process analytical technologies in the dairy industry10.1016/j.tifs.2015.02.010 ↗
81. Rapid methods and sensors for milk quality monitoring and spoilage detection10.1016/j.bios.2019.04.040 ↗
82. Joshi, K.H., Mason, A., Shaw, A., Korostynska, O., Cullen, J.D., and Al-Shamma’a, A. (2015, January 8–10). Online monitoring of milk quality using electromagnetic wave sensors. Proceedings of the 2015 9th International Conference on Sensing Technology (ICST), Auckland, New Zealand.10.1109/ICSensT.2015.7438487 ↗
83. Adoption of blockchain technology in various realms: Opportunities and challenges10.1002/spy2.109 ↗
84. Validation of a system for monitoring feeding behavior of dairy cows10.3168/jds.S0022-0302(03)73962-9 ↗
85. Assessing the potential of photogrammetry to monitor feed intake of dairy cows10.1017/S0022029918000882 ↗
86. A computerized system for monitoring feeding behavior and individual feed intake of dairy cattle10.3168/jds.S0022-0302(04)73565-1 ↗
87. Validation of a system for monitoring individual feeding behavior and individual feed intake in dairy cattle10.3168/jds.2014-8925 ↗
88. A review on real-time implantable and wearable health monitoring sensors based on triboelectric nanogenerator approach10.1016/j.nanoen.2020.105566 ↗
89. Wearable sensors: At the frontier of personalised health monitoring, smart prosthetics and assistive technologies10.1016/j.bios.2020.112946 ↗
90. Rajawat, A.S., Bedi, P., Goyal, S.B., Shaw, R.N., Ghosh, A., and Aggarwal, S. (2022). Anomalies Detection on Attached IoT Device at Cattle Body in Smart Cities Areas Using Deep Learning. AI and IoT for Smart City Applications, Springer.10.1007/978-981-16-7498-3_14 ↗
91. Dairy supply chain system based on blockchain technology10.9734/ajeba/2020/v14i230189 ↗
92. Pavkin, D.Y., Shilin, D.V., Nikitin, E.A., and Kiryushin, I.A. (2021). Designing and Simulating the Control Process of a Feed Pusher Robot Used on a Dairy Farm. Appl. Sci., 11.10.3390/app112210665 ↗
93. Martin, T., Gasselin, P., Hostiou, N., Feron, G., Laurens, L., and Purseigle, F. (April, January 29). Robots and Transformations of Work on Farms: A Systematic Review. Proceedings of the 2nd International Symposium on Work in Agriculture, Clermont-Ferrand, France.
94. Bajeh, A.O., Mojeed, H.A., Ameen, A.O., Abikoye, O.C., Salihu, S.A., Abdulraheem, M., Oladipo, I.D., and Awotunde, J.B. (2021). Internet of robotic things: Its domain, methodologies and applications. Emergence of Cyber Physical System and IoT in Smart Automation and Robotics, Springer.10.1007/978-3-030-66222-6_9 ↗
95. Mitra, A., Bera, B., and Das, A.K. (2021, January 10–13). Design and testbed experiments of public blockchain-based security framework for IoT-enabled drone-assisted wildlife monitoring. Proceedings of the IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver, BC, Canada.10.1109/INFOCOMWKSHPS51825.2021.9484468 ↗
96. Ramadan, R.A., Emara, A.-H., Al-Sarem, M., and Elhamahmy, M. (2021). Internet of Drones Intrusion Detection Using Deep Learning. Electronics, 10.10.3390/electronics10212633 ↗
97. Amira, A., Agoulmine, N., Bensaali, F., Bermak, A., and Dimitrakopoulos, G. (2019). Empowering eHealth with smart internet of things (IoT) medical devices. J. Sens. Actuator Netw., 8.10.3390/jsan8020033 ↗
98. Akram, S.V., Singh, R., AlZain, M.A., Gehlot, A., Rashid, M., Faragallah, O.S., El-Shafai, W., and Prashar, D. (2021). Performance Analysis of IoT and Long-Range Radio-Based Sensor Node and Gateway Architecture for Solid Waste Management. Sensors, 21.10.3390/s21082774 ↗
99. Highway 4.0: Digitalization of highways for vulnerable road safety development with intelligent IoT sensors and machine learning10.1016/j.ssci.2021.105407 ↗