Czech J. Anim. Sci., 2024, 69(10):389-399 | DOI: 10.17221/90/2024-CJAS

Approach to creating an intelligent system for free-range livestock farmingOriginal Paper

Radka Malinova ORCID...1, Pencho Malinov2, Evgeni Valchev2, Todorka Glushkova3
1 Department of Animal Sciences, Agricultural University, Plovdiv, Bulgaria
2 Department of Computer Systems, University of Plovdiv, Plovdiv, Bulgaria
3 Department of Computer Technology, University of Plovdiv, Plovdiv, Bulgaria

The development of intelligent systems for the tracking of free-range livestock is a challenge to both information and communication technology (ICT) scientists and those in the animal sciences. Cyber-physical systems make it possible to track and control processes involving intelligent objects from the physical and virtual worlds. In the case of free-range grazing, it is necessary to manage processes in two domains ‒ that of the intelligent pasture management and that of the animals. Due to the differences in the conditions of different types of pastures – plain or high land and the characteristics of the cattle breeds, ready-made models cannot be used, but it is necessary to build a specific multi-aspect model for the behaviour and life cycle of cows. Our team organised their research on cows from two different breeds (Rhodope Shorthorn Cattle and Bulgarian Rhodope Cattle) raised in similar technologies, grazed on two different types of pasture. The aim of the study is to develop a comprehensive model for determining cattle behavioural activities on pastures using sensor groups, by incorporating physical observations and appropriate statistical models.

Keywords: Cyber-Physical Systems (CPS); intelligent agriculture; intelligent livestock; intelligent pasture

Received: June 6, 2024; Accepted: October 6, 2024; Published: October 31, 2024  Show citation

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Malinova R, Malinov P, Valchev E, Glushkova T. Approach to creating an intelligent system for free-range livestock farming. Czech J. Anim. Sci. 2024;69(10):389-399. doi: 10.17221/90/2024-CJAS.
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    References

    1. Arshad J, Siddiqui TA, Sheikh MI, Waseem MS, Nawaz MA, Eldin ET, Rehman AU. Deployment of an intelligent and secure cattle health monitoring system. Egypt Inform J. 2023 Jul 1;24(2):265-75. Go to original source...
    2. Bloch V, Frondelius L, Arcidiacono C, Mancino M, Pastell M. Development and analysis of a CNN-and transfer-learning-based classification model for automated dairy cow feeding behavior recognition from accelerometer data. Sensors. 2023 Feb 27;23(5):2611. Go to original source... Go to PubMed...
    3. Arshad J, Siddiqui TA, Sheikh MI, Waseem MS, Nawaz MA, Eldin ET, Rehman AU. Deployment of an intelligent and secure cattle health monitoring system. Egyptian Informatics Journal. 2023 Jul 1;24(2):265-75. Go to original source...
    4. Bloch V, Frondelius L, Arcidiacono C, Mancino M, Pastell M. Development and analysis of a CNN-and transfer-learning-based classification model for automated dairy cow feeding behavior recognition from accelerometer data. Sensors. 2023 Feb 27;23(5):2611. Go to original source... Go to PubMed...
    5. Caram N, Soca P, Sollenberger LE, Baethgen W, Wallau MO, Mailhos ME. Studying beef production evolution to plan for ecological intensification of grazing ecosystems. Agricultural Systems. 2023 Feb 1;205:103582. Go to original source...
    6. Castonguay AC, Polasky S, Holden MH, Herrero M, Chang J, Mason-D'Croz D, Godde C, Lee K, Bryan BA, Gerber J, Game ET. MOO-GAPS: A multi-objective optimization model for global animal production and sustainability. Journal of Cleaner Production. 2023 Apr 10;396:136440. Go to original source...
    7. Cazzuli F, Sanchez J, Hirigoyen A, Rovira P, Beretta V, Simeone A, Jaurena M, Durante M, Savian JV, Poppi D, Montossi F. Supplement feed efficiency of growing beef cattle grazing native Campos grasslands during winter: a collated analysis. Translational Animal Science. 2023 Jan 1;7(1):txad028. Go to original source... Go to PubMed...
    8. Dumitrache I, Sacala IS, Moisescu MA, Caramihai SI. A conceptual framework for modeling and design of Cyber-Physical Systems. Studies in Informatics and Control. 2017 Sep 1;26(3):325-34. Go to original source...
    9. Golinski P, Sobolewska P, Stefanska B, Golinska B. Virtual fencing technology for cattle management in the pasture feeding system - A review. Agriculture. 2022 Dec 29;13(1):91. Go to original source...
    10. Greenwood PL. An overview of beef production from pasture and feedlot globally, as demand for beef and the need for sustainable practices increase. Anim. 2021 Dec 1; Go to original source... Go to PubMed...
    11. 15:100295.
    12. Kamminga JW, Bisby HC, Le DV, Meratnia N, Havinga PJM. Generic online animal activity recognition on collar tags. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers - UbiComp 2017; Maui, Hawaiitry; Sep 11-15, 2017: 597-606. Go to original source...
    13. Komarek AM, Dunston S, Enahoro D, Godfray HC, Herrero M, Mason-D'Croz D, Rich KM, Scarborough P, Springmann M, Sulser TB, Wiebe K. Income, consumer preferences, and the future of livestock-derived food demand. Glob Environ Change. 2021 Sep 1;70:102343. Go to original source... Go to PubMed...
    14. Liu M, Wu Y, Li G, Liu M, Hu R, Zou H, Wang Z, Peng Y. Classification of cow behavior patterns using inertial measurement units and a fully convolutional network model. J Dairy Sci. 2023 Feb 1;106(2):1351-9. Go to original source... Go to PubMed...
    15. Marais JM, Malekian R, Abu-Mahfouz AM. LoRa and LoRaWAN testbeds: A review. 2017 IEEE Africon. 2017 Sep 18:1496-501. Go to original source...
    16. Pavlovic D, Davison C, Hamilton A, Marko O, Atkinson R, Michie C, Crnojevic V, Andonovic I, Bellekens X, Tachtatzis C. Classification of cattle behaviours using neck-mounted accelerometer-equipped collars and convolutional neural networks. Sensors. 2021 Jun 12; Go to original source... Go to PubMed...
    17. 21(12):4050.
    18. Schmeling L, Elmamooz G, Hoang PT, Kozar A, Nicklas D, Sunke M, Thurner S, Rauch E. Training and validating a machine learning model for the sensor-based monitoring of lying behavior in dairy cows on pasture and in the barn. Animals. 2021 Sep 10;11(9):2660. Go to original source... Go to PubMed...
    19. Serrano J, Mendes S, Shahidian S, Marques da Silva J. Pasture quality monitoring based on proximal and remote optical sensors: A case study in the montado Mediterranean ecosystem. Agriengineering. 2023 Feb 17; Go to original source...
    20. 5(1):25.
    21. Stoyanov S, Glushkova T, Stoyanova-Doycheva A, Doychev E. Virtual physical space - An architecture supporting internet of things applications. In: 2018 20th International Symposium on Electrical Apparatus and Technologies (SIELA); Bourgas, Bulgaria; Jun 3, 2018 : 1-3. Go to original source...
    22. Thornton P, Chang Y, Loboguerrero AM, Campbell B. Perspective: What might it cost to reconfigure food systems? Glob Food Sec. 2023 Mar 1;36:100669. Go to original source...
    23. Umstatter C, Waterhouse A, Holland JP. An automated sensor-based method of simple behavioural classification of sheep in extensive systems. Comput Electron Agric. 2008;64:19-26. Go to original source...
    24. Valchev E, Malinov P, Glushkova T, Stoyanov S. Approach for modeling and implementation of an intelligent system for livestock cattle on pastures. IFAC-PapersOnLine. 2022 Jan 1;55(32):211-6. Go to original source...
    25. Zhang JJ, Wang FY, Wang X, Xiong G, Zhu F, Lv Y, Hou J, Han S, Yuan Y, Lu Q, Lee Y. Cyber-physical-social systems: The state of the art and perspectives. IEEE Trans Comput Soc Syst. 2018 Sep 12;5(3):829-40. Go to original source...

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