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Robotic Livestock Breeding: A Historical and Technological Rewiew

Year 2025, Issue: 8, 76 - 94, 29.06.2025

Abstract

Aristotle (384–323 BCE) once said, “If every tool could work by itself, by appropriate command or in a predetermined way… then there would be no need for workers or slaves.” This early philosophical insight significantly anticipates our current era, in which autonomous tools, powered by robotics and artificial intelligence (AI), increasingly replace the need for human labor. Yet robotics progressed slowly until the Industrial Revolution, and was only marginally developed by modern standards until the 13th century. One notable exception is the work of Ismail al-Jazari (1136, Upper Mesopotamia – 1206, Cizre), who is generally considered the father of cybernetics but is not widely recognized in Western scientific discourse. Al-Jazari laid out the basic principles of cybernetics, the field concerned with the control and regulation of complex systems, both living and nonliving. He is credited with designing and operating what can be considered the first robot, and his work is believed to have influenced Leonardo da Vinci (1452-1519) (Aleksandr, 1999). Living in the Cizre and Diyarbakır regions, Al-Jazari documented approximately 50 mechanical devices and instructions for their construction (Anonymous, 2025b). Today, the field of robotics has advanced rapidly. One important application area is robotic animal husbandry, often described as the “farming of the future” through the lens of artificial intelligence. This field integrates robotics, AI-enabled systems, sensors, automation, and the Internet of Things (IoT) into livestock management to improve animal welfare, reduce labor demands, and increase overall productivity.

References

  • Alvarenga, F. A. P., Borges, I., Palkovič, L., Rodina, J., Oddy, V. H., & Dobos, R. (2016). Using a three-axis accelerometer to identify and classify sheep behaviour at pasture. Applied Animal Behaviour Science, 181, 91–99. https://doi.org/10.1016/j.applanim.2016.05.026
  • Banhazi, T. M., Lehr, H., Black, J. L., Crabtree, H., Schofield, C. P., Tscharke, M., & Berckmans, D. (2012). Precision livestock farming: An international review of scientific and commercial aspects. International Journal of Agricultural and Biological Engineering, 5(3), 1–9.
  • Berckmans, D. (2014). Precision livestock farming technologies for welfare management in intensive livestock systems. Revue Scientifique et Technique (International Office of Epizootics), 33(1), 189–196. https://doi.org/10.20506/rst.33.1.2273
  • Bhuiyan, M. R., & Wree, P. (2023). Animal behavior for chicken identification and monitoring the health condition using computer vision: A systematic review. IEEE Access, PP(99), 1–1. https://doi.org/10.1109/ACCESS.2023.3331092
  • Caja, G., Carné, S., Salama, A. A. K., Ait-Saidi, A., Rojas-Olivares, M. A., Rovai, M., Capote, J., Castro, N., Argüello, A., Ayadi, M., Aljumaah, R., & Alshaikh, M. A. (2014). State-of-the-art electronic identification techniques and applications in goats. Small Ruminant Research, 121, 42–50. https://doi.org/10.1016/j.smallrumres.2014.05.012
  • Caja, G., Castro-Costa, A., & Knight, C. H. (2016). Engineering to support wellbeing of dairy animals. Journal of Dairy Research, 83(2), 136–147. https://doi.org/10.1017/S0022029916000250
  • Campbell, M., Miller, P., Díaz-Chito, K., Hong, X., McLaughlin, N., Parvinzamir, F., Martinez-del-Rincon, J., & O'Connell, N. E. (2024). A computer vision approach to monitor activity in commercial broiler chickens using trajectory-based clustering analysis. Computers and Electronics in Agriculture, 217, 108591. https://doi.org/10.1016/j.compag.2023.108591
  • Cappai, M. G., Picciau, M., Nieddu, G., Bitti, M. P. L., & Pinna, W. (2014). Long term performance of RFID technology in the large scale identification of small ruminants through electronic ceramic boluses: Implications for animal welfare and regulation compliance. Small Ruminant Research, 117(2–3), 169–175. https://doi.org/10.1016/j.smallrumres.2013.12.022
  • Chaurasia, A. (2024). IoT-based smart pet feeder system for poultry farms. Vellore Institute of Technology University. https://doi.org/10.13140/RG.2.2.34066.57285
  • Chebli, Y., El Otmani, S., Hornick, J.-L., Bindelle, J., Cabaraux, J.-F., & Chentouf, M. (2022). Estimation of grazing activity of dairy goats using accelerometers and Global Positioning System. Sensors, 22(15), 5629. https://doi.org/10.3390/s22155629
  • Ciurea, A. V., & Bratu, B.-G. (2022). Leonardo da Vinci (1452–1519): Overview of neuroanatomy contribution. Revista Medico-Chirurgicală, December 2022. https://doi.org/10.22551/MSJ.2022.04.19
  • Cora, Ö. N., & Şahin, M. E. (2020). Turkish Journal of Electromechanics & Energy in its fifth year, and portrait of a pioneer in engineering: Al-Jazari. Turkish Journal of Electromechanics and Energy, 5(1), 1–2.
  • Dilaver, H., & Dilaver, K. F. (2024). Robotics systems and artificial intelligence applications in livestock farming. Journal of Animal Science and Economics, 3(2), 63–72. https://doi.org/10.5281/zenodo.12518170
  • Dos Santos, C. A., Landim, N., de Araújo, H. X., & Paim, T. (2022). Automated systems for estrous and calving detection in dairy cattle. AgriEngineering, 4(2), 475–482. https://doi.org/10.3390/agriengineering4020031
  • Džermeikaitė, K., Bačėninaitė, D., & Antanaitis, R. (2023). Innovations in cattle farming: Application of innovative technologies and sensors in the diagnosis of diseases. Animals, 13(5), 780. https://doi.org/10.3390/ani13050780
  • Ebertz, P., Krommweh, M. S., & Büscher, W. (2019). Feasibility study: Improving floor cleanliness by using a robot scraper in group-housed pregnant sows and their reactions on the new device. Animals, 9(4), 185. https://doi.org/10.3390/ani9040185
  • Elischer, M. F., Arceo, M. E., Karcher, E. L., & Siegford, J. M. (2013). Validating the accuracy of activity and rumination monitor data from dairy cows housed in a pasture-based automatic milking system. Journal of Dairy Science, 96, 6412–6422. https://doi.org/10.3168/jds.2013-6790
  • Gabel, J. (2018, February 13). Fitbits for animals: New data could revolutionise Aussie farming. La Trobe University News. https://www.latrobe.edu.au/news/articles/2018/release/fitbits-for-animals
  • Grinter, L. N., Campler, M. R., & Costa, J. H. C. (2019). Technical note: Validation of a behavior-monitoring collar’s precision and accuracy to measure rumination, feeding, and resting time of lactating dairy cows. Journal of Dairy Science, 102, 3487–3494. https://doi.org/10.3168/jds.2018-15563
  • Hofmann, W., Neal, M., Woodward, S., & O'Neill, T. (2022). GPS technology as a tool to aid pasture management on dairy farms. Journal of New Zealand Grasslands, 84, Article 3561. https://doi.org/10.33584/jnzg.2022.84.3561
  • Kamboj, A., Ji, T., & Driggs-Campbell, K. (2022). Examining audio communication mechanisms for supervising fleets of agricultural robots. arXiv. https://doi.org/10.48550/arXiv.2208.10455
  • Long, N. K., Sammut, K., Sgarioto, D., Garratt, M., & Abbass, H. (2020). A comprehensive review of shepherding as a bio-inspired swarm-robotics guidance approach (arXiv:1912.07796v4). arXiv. https://arxiv.org/pdf/1912.07796
  • Løvendahl, P., & Sørensen, L. P. (2016). Frequently recorded sensor data may correctly provide health status of cows if data are handled carefully and errors are filtered away. Biotechnology, Agronomy, Society and Environment, 20(1), 3–12. https://doi.org/10.25518/1780-4507.12562
  • Meijer, N., Filter, M., Józwiak, Á., Willems, D., Frewer, L., Fischer, A., Liu, N., Bouzembrak, Y., Valentin, L., Fuhrmann, M., Mylord, T., Kerekes, K., Farkas, Z., Hadjigeorgiou, E., Clark, B., Coles, D., Comber, R., Simpson, E., & Marvin, H. (2020). Determination and metrics for emerging risks identification (DEMETER): Final report (EFSA Supporting Publication 2020:EN-1889). European Food Safety Authority. https://doi.org/10.2903/sp.efsa.2020.EN-1889
  • Nielsen, P. P., Fontana, I., Sloth, K. H., Guarino, M., & Blokhuis, H. (2018). Technical note: Validation and comparison of 2 commercially available activity loggers. Journal of Dairy Science, 101, 5449–5453. https://doi.org/10.3168/jds.2017-13784
  • Ozentürk, U., Chen, Z., Jamone, L., & Versace, E. (2023). Robotics for poultry farming: Challenges and opportunities. arXiv preprint. https://arxiv.org/abs/2311.05069
  • Ozentürk, U., Chen, Z., Jamone, L., & Versace, E. (2024). Robotics for poultry farming: Challenges and opportunities. Computers and Electronics in Agriculture, 226, 109411. https://doi.org/10.1016/j.compag.2024.109411
  • Pastell, M., Aisla, A. M., Hautala, M., Poikalainen, V., Praks, J., Veermäe, I., & Ahokas, J. (2006). Contactless measurement of cow behavior in a milking robot. Behavior Research Methods, 38(3), 479–486. https://doi.org/10.3758/BF03192802
  • Rankin, S. A., Bradley, R. L., Miller, G., & Mildenhall, K. B. (2017). A 100-year review: A century of dairy processing advancements—Pasteurization, cleaning and sanitation, and sanitary equipment design. Journal of Dairy Science, 100(12), 9903–9915. https://doi.org/10.3168/jds.2017-13187
  • Rutten, C. J., Velthuis, A. G. J., Steeneveld, W., & Hogeveen, H. (2013). Invited review: Sensors to support health management on dairy farms. Journal of Dairy Science, 96(4), 1928–1952. https://doi.org/10.3168/jds.2012-6107
  • Sorensen, L. P., Bjerring, M., & Lovendahl, P. (2016). Monitoring individual cow udder health in automated milking systems using online somatic cell counts. Journal of Dairy Science, 99, 608–620. https://doi.org/10.3168/jds.2014-8823
  • Sørensen, P. E., Van Den Broeck, W., Kiil, K., Jasinskyte, D., Moodley, A., Garmyn, A., ... & Butaye, P. (2020). New insights into the biodiversity of coliphages in the intestine of poultry. Scientific Reports, 10(1), 15220. https://doi.org/10.1038/s41598-020-72183-w
  • Sun, Z., Tao, W., Gao, M., Zhang, M., Song, S., & Wang, G. (2024). Broiler health monitoring technology based on sound features and random forest. Engineering Applications of Artificial Intelligence, 135, 108849. https://doi.org/10.1016/j.engappai.2024.108849
  • Tangorra, F. M., Buoio, E., Calcante, A., Bassi, A., & Costa, A. (2024). Internet of Things (IoT): Sensors Application in Dairy Cattle Farming. Animals, 14(21), 3071. https://doi.org/10.3390/ani14213071
  • Tse, C., Barkema, H. W., DeVries, T. J., Rushen, J., & Pajor, E. A. (2017). Effect of transitioning to automatic milking systems on producers' perceptions of farm management and cow health in the Canadian dairy industry. Journal of Dairy Science, 100(3), 2404–2414. https://doi.org/10.3168/jds.2016-11659
  • Vaintrub, M. O., Levit, H., Chincarini, M., Fusaro, I., Giammarco, M., & Vignola, G. (2021). Precision livestock farming, automats and new technologies: Possible applications in extensive dairy sheep farming. Animal, 15, 100143. https://doi.org/10.1016/j.animal.2020.100143
  • Wagner, N., Antoine, V., Richard, M.-M., Lardy, R., Silberberg, M., Koko, J., & Veissier, I. (2020). Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis. Computers and Electronics in Agriculture, 170, 105233. https://doi.org/10.1016/j.compag.2020.105233
  • Zambelis, A., Wolfe, T., & Vasseur, E. (2019). Technical note: Validation of an ear-tag accelerometer to identify feeding and activity behaviors of tiestall-housed dairy cattle. Journal of Dairy Science, 102, 4536–4540. https://doi.org/10.3168/jds.2018-15766
  • Zhou, D., Zhou, Y., He, P., Yu, L., Pan, J., Chai, L., & Lin, H. (2023). Development of an automatic weighing platform for monitoring bodyweight of broiler chickens in commercial production. Frontiers of Agricultural Science and Engineering, 10(3), 363–373. https://doi.org/10.15302/JFASE2023510

Robotic Livestock Breeding: A Historical and Technological Rewiew

Year 2025, Issue: 8, 76 - 94, 29.06.2025

Abstract

Robotik hayvancılık çiftçiliğinin gelişimi, yapay zeka (AI), otomasyon ve Nesnelerin İnterneti (IoT) teknolojilerinin modern tarıma hızla entegre edilmesini yansıtır. Bu inceleme, erken mekanizasyondan gelişmiş AI destekli yönetim araçlarına kadar önemli kilometre taşlarını vurgulayarak, hayvancılıkta robotik sistemlerin tarihsel evrimini araştırır. Robotik sağım, besleme, üreme yardımı, iklim kontrolü ve otonom sürü yönetimi dahil olmak üzere on iki önemli teknolojik alan açıklanmaktadır. İnceleme ayrıca robotik hayvancılık sistemlerinin avantajlarını, zorluklarını ve gelecekteki yönlerini değerlendirerek sürdürülebilirlik, üretkenlik ve hayvan refahını vurgular

References

  • Alvarenga, F. A. P., Borges, I., Palkovič, L., Rodina, J., Oddy, V. H., & Dobos, R. (2016). Using a three-axis accelerometer to identify and classify sheep behaviour at pasture. Applied Animal Behaviour Science, 181, 91–99. https://doi.org/10.1016/j.applanim.2016.05.026
  • Banhazi, T. M., Lehr, H., Black, J. L., Crabtree, H., Schofield, C. P., Tscharke, M., & Berckmans, D. (2012). Precision livestock farming: An international review of scientific and commercial aspects. International Journal of Agricultural and Biological Engineering, 5(3), 1–9.
  • Berckmans, D. (2014). Precision livestock farming technologies for welfare management in intensive livestock systems. Revue Scientifique et Technique (International Office of Epizootics), 33(1), 189–196. https://doi.org/10.20506/rst.33.1.2273
  • Bhuiyan, M. R., & Wree, P. (2023). Animal behavior for chicken identification and monitoring the health condition using computer vision: A systematic review. IEEE Access, PP(99), 1–1. https://doi.org/10.1109/ACCESS.2023.3331092
  • Caja, G., Carné, S., Salama, A. A. K., Ait-Saidi, A., Rojas-Olivares, M. A., Rovai, M., Capote, J., Castro, N., Argüello, A., Ayadi, M., Aljumaah, R., & Alshaikh, M. A. (2014). State-of-the-art electronic identification techniques and applications in goats. Small Ruminant Research, 121, 42–50. https://doi.org/10.1016/j.smallrumres.2014.05.012
  • Caja, G., Castro-Costa, A., & Knight, C. H. (2016). Engineering to support wellbeing of dairy animals. Journal of Dairy Research, 83(2), 136–147. https://doi.org/10.1017/S0022029916000250
  • Campbell, M., Miller, P., Díaz-Chito, K., Hong, X., McLaughlin, N., Parvinzamir, F., Martinez-del-Rincon, J., & O'Connell, N. E. (2024). A computer vision approach to monitor activity in commercial broiler chickens using trajectory-based clustering analysis. Computers and Electronics in Agriculture, 217, 108591. https://doi.org/10.1016/j.compag.2023.108591
  • Cappai, M. G., Picciau, M., Nieddu, G., Bitti, M. P. L., & Pinna, W. (2014). Long term performance of RFID technology in the large scale identification of small ruminants through electronic ceramic boluses: Implications for animal welfare and regulation compliance. Small Ruminant Research, 117(2–3), 169–175. https://doi.org/10.1016/j.smallrumres.2013.12.022
  • Chaurasia, A. (2024). IoT-based smart pet feeder system for poultry farms. Vellore Institute of Technology University. https://doi.org/10.13140/RG.2.2.34066.57285
  • Chebli, Y., El Otmani, S., Hornick, J.-L., Bindelle, J., Cabaraux, J.-F., & Chentouf, M. (2022). Estimation of grazing activity of dairy goats using accelerometers and Global Positioning System. Sensors, 22(15), 5629. https://doi.org/10.3390/s22155629
  • Ciurea, A. V., & Bratu, B.-G. (2022). Leonardo da Vinci (1452–1519): Overview of neuroanatomy contribution. Revista Medico-Chirurgicală, December 2022. https://doi.org/10.22551/MSJ.2022.04.19
  • Cora, Ö. N., & Şahin, M. E. (2020). Turkish Journal of Electromechanics & Energy in its fifth year, and portrait of a pioneer in engineering: Al-Jazari. Turkish Journal of Electromechanics and Energy, 5(1), 1–2.
  • Dilaver, H., & Dilaver, K. F. (2024). Robotics systems and artificial intelligence applications in livestock farming. Journal of Animal Science and Economics, 3(2), 63–72. https://doi.org/10.5281/zenodo.12518170
  • Dos Santos, C. A., Landim, N., de Araújo, H. X., & Paim, T. (2022). Automated systems for estrous and calving detection in dairy cattle. AgriEngineering, 4(2), 475–482. https://doi.org/10.3390/agriengineering4020031
  • Džermeikaitė, K., Bačėninaitė, D., & Antanaitis, R. (2023). Innovations in cattle farming: Application of innovative technologies and sensors in the diagnosis of diseases. Animals, 13(5), 780. https://doi.org/10.3390/ani13050780
  • Ebertz, P., Krommweh, M. S., & Büscher, W. (2019). Feasibility study: Improving floor cleanliness by using a robot scraper in group-housed pregnant sows and their reactions on the new device. Animals, 9(4), 185. https://doi.org/10.3390/ani9040185
  • Elischer, M. F., Arceo, M. E., Karcher, E. L., & Siegford, J. M. (2013). Validating the accuracy of activity and rumination monitor data from dairy cows housed in a pasture-based automatic milking system. Journal of Dairy Science, 96, 6412–6422. https://doi.org/10.3168/jds.2013-6790
  • Gabel, J. (2018, February 13). Fitbits for animals: New data could revolutionise Aussie farming. La Trobe University News. https://www.latrobe.edu.au/news/articles/2018/release/fitbits-for-animals
  • Grinter, L. N., Campler, M. R., & Costa, J. H. C. (2019). Technical note: Validation of a behavior-monitoring collar’s precision and accuracy to measure rumination, feeding, and resting time of lactating dairy cows. Journal of Dairy Science, 102, 3487–3494. https://doi.org/10.3168/jds.2018-15563
  • Hofmann, W., Neal, M., Woodward, S., & O'Neill, T. (2022). GPS technology as a tool to aid pasture management on dairy farms. Journal of New Zealand Grasslands, 84, Article 3561. https://doi.org/10.33584/jnzg.2022.84.3561
  • Kamboj, A., Ji, T., & Driggs-Campbell, K. (2022). Examining audio communication mechanisms for supervising fleets of agricultural robots. arXiv. https://doi.org/10.48550/arXiv.2208.10455
  • Long, N. K., Sammut, K., Sgarioto, D., Garratt, M., & Abbass, H. (2020). A comprehensive review of shepherding as a bio-inspired swarm-robotics guidance approach (arXiv:1912.07796v4). arXiv. https://arxiv.org/pdf/1912.07796
  • Løvendahl, P., & Sørensen, L. P. (2016). Frequently recorded sensor data may correctly provide health status of cows if data are handled carefully and errors are filtered away. Biotechnology, Agronomy, Society and Environment, 20(1), 3–12. https://doi.org/10.25518/1780-4507.12562
  • Meijer, N., Filter, M., Józwiak, Á., Willems, D., Frewer, L., Fischer, A., Liu, N., Bouzembrak, Y., Valentin, L., Fuhrmann, M., Mylord, T., Kerekes, K., Farkas, Z., Hadjigeorgiou, E., Clark, B., Coles, D., Comber, R., Simpson, E., & Marvin, H. (2020). Determination and metrics for emerging risks identification (DEMETER): Final report (EFSA Supporting Publication 2020:EN-1889). European Food Safety Authority. https://doi.org/10.2903/sp.efsa.2020.EN-1889
  • Nielsen, P. P., Fontana, I., Sloth, K. H., Guarino, M., & Blokhuis, H. (2018). Technical note: Validation and comparison of 2 commercially available activity loggers. Journal of Dairy Science, 101, 5449–5453. https://doi.org/10.3168/jds.2017-13784
  • Ozentürk, U., Chen, Z., Jamone, L., & Versace, E. (2023). Robotics for poultry farming: Challenges and opportunities. arXiv preprint. https://arxiv.org/abs/2311.05069
  • Ozentürk, U., Chen, Z., Jamone, L., & Versace, E. (2024). Robotics for poultry farming: Challenges and opportunities. Computers and Electronics in Agriculture, 226, 109411. https://doi.org/10.1016/j.compag.2024.109411
  • Pastell, M., Aisla, A. M., Hautala, M., Poikalainen, V., Praks, J., Veermäe, I., & Ahokas, J. (2006). Contactless measurement of cow behavior in a milking robot. Behavior Research Methods, 38(3), 479–486. https://doi.org/10.3758/BF03192802
  • Rankin, S. A., Bradley, R. L., Miller, G., & Mildenhall, K. B. (2017). A 100-year review: A century of dairy processing advancements—Pasteurization, cleaning and sanitation, and sanitary equipment design. Journal of Dairy Science, 100(12), 9903–9915. https://doi.org/10.3168/jds.2017-13187
  • Rutten, C. J., Velthuis, A. G. J., Steeneveld, W., & Hogeveen, H. (2013). Invited review: Sensors to support health management on dairy farms. Journal of Dairy Science, 96(4), 1928–1952. https://doi.org/10.3168/jds.2012-6107
  • Sorensen, L. P., Bjerring, M., & Lovendahl, P. (2016). Monitoring individual cow udder health in automated milking systems using online somatic cell counts. Journal of Dairy Science, 99, 608–620. https://doi.org/10.3168/jds.2014-8823
  • Sørensen, P. E., Van Den Broeck, W., Kiil, K., Jasinskyte, D., Moodley, A., Garmyn, A., ... & Butaye, P. (2020). New insights into the biodiversity of coliphages in the intestine of poultry. Scientific Reports, 10(1), 15220. https://doi.org/10.1038/s41598-020-72183-w
  • Sun, Z., Tao, W., Gao, M., Zhang, M., Song, S., & Wang, G. (2024). Broiler health monitoring technology based on sound features and random forest. Engineering Applications of Artificial Intelligence, 135, 108849. https://doi.org/10.1016/j.engappai.2024.108849
  • Tangorra, F. M., Buoio, E., Calcante, A., Bassi, A., & Costa, A. (2024). Internet of Things (IoT): Sensors Application in Dairy Cattle Farming. Animals, 14(21), 3071. https://doi.org/10.3390/ani14213071
  • Tse, C., Barkema, H. W., DeVries, T. J., Rushen, J., & Pajor, E. A. (2017). Effect of transitioning to automatic milking systems on producers' perceptions of farm management and cow health in the Canadian dairy industry. Journal of Dairy Science, 100(3), 2404–2414. https://doi.org/10.3168/jds.2016-11659
  • Vaintrub, M. O., Levit, H., Chincarini, M., Fusaro, I., Giammarco, M., & Vignola, G. (2021). Precision livestock farming, automats and new technologies: Possible applications in extensive dairy sheep farming. Animal, 15, 100143. https://doi.org/10.1016/j.animal.2020.100143
  • Wagner, N., Antoine, V., Richard, M.-M., Lardy, R., Silberberg, M., Koko, J., & Veissier, I. (2020). Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis. Computers and Electronics in Agriculture, 170, 105233. https://doi.org/10.1016/j.compag.2020.105233
  • Zambelis, A., Wolfe, T., & Vasseur, E. (2019). Technical note: Validation of an ear-tag accelerometer to identify feeding and activity behaviors of tiestall-housed dairy cattle. Journal of Dairy Science, 102, 4536–4540. https://doi.org/10.3168/jds.2018-15766
  • Zhou, D., Zhou, Y., He, P., Yu, L., Pan, J., Chai, L., & Lin, H. (2023). Development of an automatic weighing platform for monitoring bodyweight of broiler chickens in commercial production. Frontiers of Agricultural Science and Engineering, 10(3), 363–373. https://doi.org/10.15302/JFASE2023510
There are 39 citations in total.

Details

Primary Language English
Subjects Agricultural Engineering (Other)
Journal Section Articles
Authors

Ayşe Özge Demir 0000-0001-7203-4734

Sinan Hakan 0009-0001-0468-2091

Publication Date June 29, 2025
Submission Date April 18, 2025
Acceptance Date June 22, 2025
Published in Issue Year 2025 Issue: 8

Cite

APA Demir, A. Ö., & Hakan, S. (2025). Robotic Livestock Breeding: A Historical and Technological Rewiew. Şırnak Üniversitesi Fen Bilimleri Dergisi(8), 76-94.

Sirnak University Journal of Science licensed under a Creative Commons Attribution-NonCommercial-Non-Derivatives 4.0 International Licence (CC BY-NC-ND 4.0).