Robotic herding framework design for remote and small-scale pastoral farming

Authors

  • Jinyu Liu 1. EUREKA Robotics Centre, Cardiff School of Technologies, Cardiff Metropolitan University, CF5 2YB, UK http://orcid.org/0009-0001-3790-5781
  • Esyin Chew 1. EUREKA Robotics Centre, Cardiff School of Technologies, Cardiff Metropolitan University, CF5 2YB, UK http://orcid.org/0000-0003-2644-9888
  • Chow Siing Sia 1. EUREKA Robotics Centre, Cardiff School of Technologies, Cardiff Metropolitan University, CF5 2YB, UK http://orcid.org/0000-0002-7154-1053
  • Hasyiya Karimah Adli 2. Faculty of Data Science&Computing, Universiti Malaysia Kelantan, 16100, Malaysia http://orcid.org/0000-0001-8754-6206
  • Linnan Zhang 3. Village Work Team, Shenyang University of Technology, Shenyang 110870, China
  • Shichen Gai 4. School of Innovation and Entrepreneurship, Shenyang University of Technology, Shenyang 110870, China
  • Jiaji Yang 1. EUREKA Robotics Centre, Cardiff School of Technologies, Cardiff Metropolitan University, CF5 2YB, UK
  • Tao Wang 3. Village Work Team, Shenyang University of Technology, Shenyang 110870, China

DOI:

https://doi.org/10.25165/ijabe.v18i6.9826

Keywords:

smart agriculture, small family farm, herding, animal welfare, interdisciplinary robotics research

Abstract

This study presents a comprehensive framework for designing an intelligent and sustainable robot-assisted herding system, based on a systematic literature review and field investigations conducted in remote pastoral regions operated by small family farms. The study highlights a multi-institutional collaboration between the EUREKA Robotics Centre, Cardiff Metropolitan University, UK; Universiti Malaysia Kelantan; and Shenyang University of Technology, China. The research aims to ensure that advancements in robotic technology are effectively aligned with the practical challenges encountered in livestock herding. The literature review reveals that robotic-assisted herding has evolved from theory to early practical applications through advances in AI, robotics, and agriculture. The study conducted a field survey involving fifty-five farmers, and it revealed low initial awareness from the farmers but high practical acceptance of robotic herding solutions, and challenges in costing and cultural shift. To overcome these challenges, the study applied the integration of object-oriented robotic design with educational initiatives customized to local herding environments. It also coordinated with stakeholders such as farmers, robotic innovators, and local authorities in robotic herding. The proposed framework prioritizes modularity, durability, and adaptability to local context in the robotic design. Future work will focus on iterative development and field trials across China, Malaysia, and the UK. This study is intended to validate and refine the framework. This effort will contribute to global precision livestock farming and the broader transformation toward sustainable agriculture. Key words: smart agriculture; small family farm; herding; animal welfare; interdisciplinary robotics research DOI: 10.25165/j.ijabe.20251806.9826 Citation: Liu J Y, Chew E, Sia C S, Adli H K, Zhang L N, Gai S C, et al. Robotic herding framework design for remote and small-scale pastoral farming. Int J Agric & Biol Eng, 2025; 18(6): 182–190.

References

Javaid M, Haleem A, Singh R P, Suman R. Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks, 2022; 3: 150–164.

Sharma V, Tripathi A K, Mittal H. Technological revolutions in smart farming: Current trends, challenges & future directions. Comput. Electron. Agric., 2022; 201: 107217.

Abbasi R, Martinez P, Ahmad R. The digitization of agricultural industry – a systematic literature review on agriculture 4.0. Smart Agricultural Technology, 2022; 2: 100042.

Aquilani C, Confessore A, Bozzi R, Sirtori F, Pugliese C. Review: Precision Livestock Farming technologies in pasture-based livestock systems. Animal, 2022; 16(1): 100429.

Bianchi M C, Bava L, Sandrucci A, Tangorra F M, Tamburini A, Gislon G, et al. Diffusion of precision livestock farming technologies in dairy cattle farms. Animal, 2022; 16(11): 100650.

Barbedo J G A, Koenigkan L V, Santos P M, Ribeiro A R B. Counting cattle in UAV images—dealing with clustered animals and animal/background contrast changes. Sensors, 2020; 20(7): 2126.

Bao J, Xie Q J. Artificial intelligence in animal farming: A systematic literature review. J. Clean. Prod., 2022; 331: 129956.

Herlin A, Brunberg E, Hultgren J, Hogberg N, Rydberg A, Skarin A. Animal welfare implications of digital tools for monitoring and management of cattle and sheep on pasture. Animals, 2021; 11(3): 829.

Omotayo A O, Adediran S A, Omotoso A B, Olagunju K O, Omotayo O P. Artificial intelligence in agriculture: ethics, impact possibilities, and pathways for policy. Comput. Electron. Agric., 2025; 239: 110927.

Santana T C, Guiselini C, Pandorfi H, Vigoderis R B, Barbosa Filho J A D, Soares R G F, et al. Ethics, animal welfare, and artificial intelligence in livestock: A bibliometric review. AgriEngineering, 2025; 7(7): 202.

Long N K, Sammut K, Sgarioto D, Garratt M, Abbass H A. A comprehensive review of shepherding as a bio-inspired swarm-robotics guidance approach. IEEE Transactions on Emerging Topics in Computational Intelligence, 2020; 4(4): 523–537.

Yaxley K J, Joiner K F, Abbass H. Drone approach parameters leading to lower stress sheep flocking and movement: Sky shepherding. Sci. Rep., 2021; 11(1): 7803.

FAO. Smallholders and family farming. 2025. https://www.fao.org/family-farming/themes/small-family-farmers/en/. Accessed on[2025-03-18].

Bartoli L, De Rosa M. Family farm business and access to rural development polices: A demographic perspective. Agricultural and Food Economics, 2013; 1(1): 12.

Graeub B E, Chappell M J, Wittman H, Ledermann S, Kerr R B, Gemmill-Herren B. The state of family farms in the world. World Dev., 2016; 87: 1–15.

Lowder S K, Skoet J, Raney T. The number, size, and distribution of farms, smallholder farms, and family farms worldwide. World Dev., 2016; 87: 16–29.

Manono B O. Small-scale farming in the United States: Challenges and pathways to enhanced productivity and profitability. Sustainability, 2025; 17(15): 17156752.

Dhillon R, Moncur Q. Small-scale farming: A review of challenges and potential opportunities offered by technological advancements. Sustainability, 2023; 15(21): 15478.

Page M J, McKenzie J E, Bossuyt P M, Boutron I, Hoffmann T C, Mulrow C D, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 2021; 372: n71.

Elsevier. Scopus database. 2023. https://www.scopus.com. Accessed on [2025-01-17].

Clarivate. Web of Science Core Collection. 2022. https://www.webofscience.com. Accessed on [2024-12-09].

Chew E, Turner D A. Can a robot bring your life back? A systematic review for robotics in rehabilitation. In: Sequeira J (Ed.), editors. Robotics in healthcare. Cham: Springer International Publishing. 2019. pp.1–35.

Yang J J, Chew E. A systematic review for service humanoid robotics model in hospitality. Int. J. Soc. Robotics, 2020; 13(6): 1397–1410.

Drach U, Halachmi I, Pnini T, Izhaki I, Degani A. Automatic herding reduces labour and increases milking frequency in robotic milking. Biosyst. Eng., 2017; 155: 134–141.

Nardi S, Mazzitelli F, Pallottino L. A game theoretic robotic team coordination protocol for intruder herding. IEEE Robot. Automation Letter, 2018; 3(4): 4124–4131.

Paranjape A A, Chung S J, Kim K, Shim D H. Robotic herding of a flock of birds using an unmanned aerial vehicle. IEEE Trans. Robot., 2018; 34(4): 901–915.

Elamvazhuthi K, Kakish Z, Shirsat A, Berman S. Controllability and stabilization for herding a robotic swarm using a leader: A mean-field approach. IEEE Trans. Robot, 2021; 37(2): 418–432.

Behjati M, Mohd Noh A B, Alobaidy H A H, Zulkifley M A, Nordin R, Abdullah N F. LoRa communications as an enabler for internet of drones towards large-scale livestock monitoring in rural farms. Sensors, 2021; 21(15): 5044.

Hu J Y, Turgut A E, Krajnik T, Lennox B, Arvin F. Occlusion-based coordination protocol design for autonomous robotic shepherding tasks. IEEE Trans. Cogn. Dev. Syst., 2022; 14(1): 126–135.

Li X H, Huang H L, Savkin A, Zhang J. Robotic herding of farm animals using a network of barking aerial drones. Drones, 2022; 6(2): 29.

Riego Del Castillo V, Sanchez-Gonzalez L, Campazas-Vega A, Strisciuglio N. Vision-based module for herding with a sheepdog robot. Sensors, 2022; 22(14): 5321.

Anzai H, Kumaishi M. Effects of continuous drone herding on behavioral response and spatial distribution of grazing cattle. Appl. Anim. Behav. Sci., 2023; 268: 106089.

Zhang S, Lei X K, Duan M Y, Peng X G, Pan J. A distributed outmost push approach for multirobot herding. IEEE Trans. Robot, 2024; 40: 1706–1723.

Yang X, Jove de Castro B, Sanchez-Gonzalez L, Rodriguez Lera F J. Dataset for herding and predator detection with the use of robots. Data Brief, 2024; 55(110691): 110691.

Anzai H, Sakurai H. Preliminary study on the application of robotic herding to manipulation of grazing distribution: Behavioral response of cattle to herding by an unmanned vehicle and its manipulation performance. Appl. Anim. Behav. Sci., 2022; 256: 105751.

Chen Y J, Zhang Z X, Wu Z, Wu Y N, He B W, Zhang H, et al. Multiple mobile robots planning framework for herding non-cooperative target. IEEE Trans. Autom. Sci. Eng., 2024; 21(4): 7363–7378.

Tola T, Mi J, Che Y. Mapping and localization of autonomous mobile robots in simulated indoor environments. Frontiers, 2024; 4(3): 91–100.

Martin T, Gasselin P, Hostiou N, Feron G, Laurens L, Purseigle F, et al. Robots and transformations of work in farm: a systematic review of the literature and a research agenda. Agronomy for Sustainable Development, 2022; 42(4): 66.

Barbosa M W. Government support mechanisms for sustainable agriculture: A systematic literature review and future research agenda. Sustainability, 2024; 16(5): 2185.

Lei X, Yang D. An analysis of the impact of digital technology adoption on the income of high quality farmers in production and operating. PLoS One, 2024; 19(9): e0309675.

Guo D, Guo Y, Jiang K. Government R&D support and firms’ access to external financing: funding effects, certification effects, or both? Technovation, 2022; 115: 102469.

Seol J, Park Y, Pak J, Jo Y, Lee G, Kim Y, et al. Human-centered robotic system for agricultural applications: Design, development, and field evaluation. Agriculture, 2024; 14(11): 1985.

Zhang Z, Hu Y, Batunacun. Analysis of the driving mechanism of grassland degradation in Inner Mongolia Grassland from 2015 to 2020 using interpretable machine learning methods. Land, 2025; 14(2): 386.

Creswell J W. Qualitative inquiry and research design: Choosing among five approaches. Thousand Oaks, CA: SAGE Publications, 2013; 448p.

Tashakkori A, Teddlie C. Sage handbook of mixed methods in social & behavioral research. Thousand Oaks, CA: SAGE Publications. 2010. 894 p.

Bryman A. Social research methods. Oxford: Oxford University Press, 2016; 747p.

De Vaus D. Surveys in social research. Abingdon, Oxon: Routledge. 2014.

Etikan I. Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 2016; 5(1): 1–4.

Fowler F J. Survey research methods. Thousand Oaks, CA: SAGE Publications, 2014; 184p.

Dillman D A, Smyth J D, Christian L M. Internet, phone, mail, and mixed-mode surveys: The tailored design method. New Jersey: John Wiley and Sons. 2014. 509 p.

Denzin N K, Lincoln Y S. The sage handbook of qualitative research. Thousand Oaks, CA: SAGE Publications, 2018; 1152p.

Ahmed S K, Mohammed R A, Nashwan A J, Ibrahim R H, Abdalla A Q, Ameen B M M, et al. Using thematic analysis in qualitative research. Journal of Medicine, Surgery, and Public Health, 2025; 6: 100198.

Shang L, Heckelei T, Gerullis M K, Börner J, Rasch S. Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction. Agric. Syst., 2021; 190: 103074.

Stahl B C, Akintoye S, Bitsch L, Bringedal B, Eke D, Farisco M, et al. From Responsible Research and Innovation to responsibility by design. J. Responsible Innov., 2021; 8(2): 175–198.

Schnack A, Bartsch F, Osburg V-S, Errmann A. Sustainable agricultural technologies of the future: Determination of adoption readiness for different consumer groups. Technological Forecasting and Social Change, 2024; 208: 123697.

Choi H, Crump C, Duriez C, Elmquist A, Hager G, Han D, et al. On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward. Proc. Natl. Acad. Sci. U.S.A., 2021; 118(1): e1907856118.

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Published

2025-12-26

How to Cite

Liu, J., Chew, E., Sia, C. S., Adli, H. K., Zhang, L., Gai, S., … Wang, T. (2025). Robotic herding framework design for remote and small-scale pastoral farming. International Journal of Agricultural and Biological Engineering, 18(6), 182–190. https://doi.org/10.25165/ijabe.v18i6.9826

Issue

Vol. 18 No. 6 (2025): IJABE

Section

Information Technology, Sensors and Control Systems

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