Precision Livestock Farming (PLF) leverages advanced digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) to enhance sustainable and resilient livestock production. Amid escalating pressures from climate change, resource scarcity, and ethical consumer demands, PLF offers transformative solutions by optimizing animal health, welfare, and environmental efficiency. This review synthesizes advancements in PLF tools, including wearable sensors, GPS tracking, automated feeding systems, and environmental monitors, across cattle, poultry, pig, and small ruminant production. AI-driven models (e.g., convolutional neural networks) improve disease detection and emission forecasting, while IoT-enabled devices like RFID tags and accelerometers enable real-time behavioural and physiological monitoring. In poultry, thermal imaging and robotic systems enhance flock management, whereas biometrics and automated feeders boost growth rates in pigs. For small ruminants, drone surveillance and electronic identification (EID) systems improve grazing management in extensive systems. Despite these innovations, challenges persist in data standardization, cost barriers, and adoption among small-scale farmers. Future directions emphasize integrated decision-support systems, policy frameworks, and affordable PLF solutions to ensure scalability. By bridging cutting-edge technology with practical farm applications, PLF can drive sustainable intensification, meeting global food security needs while addressing environmental and ethical concerns. This review underscores PLF’s potential to revolutionize livestock production, advocating for interdisciplinary collaboration to overcome implementation hurdles and achieve climate-resilient agriculture.
Precision livestock farming, Artificial intelligence, Sustainability, IoT, Animal welfare, Climate resilience
Ahmed, M., Ahmad, S., Waldrip, H. M., Ramin, M., & Raza, M. A. (2020). Whole farm modeling: A systems approach to understanding and managing livestock for greenhouse gas mitigation, economic viability and environmental quality. In Animal Manure Production, Characterization, Environmental Concerns and Management (Vol. 67, pp. 345–371). Elsevier. https://doi.org/10.1016/B978-0-12-818424-4.00017-5
Alvarenga, F., Borges, I., Palkovič, L., Rodina, J., Oddy, V., & 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.2016.05.019
AlZahal, O., Steele, M., Van Schaik, M., Kyriazakis, I., Duffield, T., McBride, B., & AlZahal, H. (2011). The use of a radiotelemetric ruminal bolus to detect body temperature changes in lactating dairy cattle. Journal of Dairy Science, 94(7), 3568–3574. https://doi.org/10.3168/jds.2010-3929
Arcidiacono, C., Porto, S. M. C., Mancino, M., & Cascone, G. (2018). A software tool for the automatic and real-time analysis of cow velocity data in free-stall barns: The case study of oestrus detection from Ultra-Wide-Band data. Biosystems Engineering, 173, 157–165. https://doi.org/10.1016/j.biosystemseng.2017.10.007
AWIN. (2015a). AWIN Welfare Assessment Protocol for Goats. ResearchGate. https://www.researchgate.net/publication/275341689_AWIN_welfare_assessment_protocol_for_goats
AWIN. (2015b). AWIN Welfare Assessment Protocol for Sheep. ResearchGate. https://www.researchgate.net/publication/275887069_AWIN_Welfare_Assessment_Protocol_for_Sheep
Baldi, D., & Gottardo, A. (2017). Livestock production to feed the planet: Animal protein: A forecast of global demand over the next years. Relations: Beyond Anthropocentrism, 5(1), 65–78. https://doi.org/10.7358/rela-2017-001-bald
Barbari, M., Conti, L., Koostra, B. K., Masi, G., Sorbetti Guerri, F., & Workman, S. R. (2006). The use of global positioning and geographical information systems in the management of extensive cattle grazing. Biosystems Engineering, 95(2), 271–280. https://doi.org/10.1016/j.biosystemseng.2006.06.012
Barwick, J., Lamb, D. W., Dobos, R., Welch, M., & Trotter, M. (2018). Categorising sheep activity using a tri-axial accelerometer. Computers and Electronics in Agriculture, 145, 289–297. https://doi.org/10.1016/j.compag.2017.12.034
Berckmans, D., & Guarino, M. (2017). From the editors: Precision livestock farming for the global livestock sector. Animal Frontiers, 7(1), 4–5. https://doi.org/10.2527/af.2017.0101
Bernabucci, U. (2019). Climate change: Impact on livestock and how can we adapt. Animal Frontiers, 9(1), 3.
Caja, G., Castro-Costa, A., Salama, A. A. K., Oliver, J., Baratta, M., Ferrer, C., & Knight, C. H. (2020). Sensing solutions for improving the performance, health and wellbeing of small ruminants. Journal of Dairy Research, 87(S1), 34–46.
Carpentier, L., Berckmans, D., Youssef, A., van Waterschoot, T., Johnston, D., & Norton, T. (2018). Automatic cough detection for bovine respiratory disease in a calf house. Biosystems Engineering, 173, 45–56. https://doi.org/10.1016/j.biosystemseng.2018.06.018
Castro, N., Gómez-González, L. A., Earley, B., & Argüello, A. (2018). Use of clinic refractometer at farm as a tool to estimate the IgG content in goat colostrum. Journal of Applied Animal Research, 46(1), 1505–1508.
De Wet, L., Vranken, E., Chedad, A., Aerts, J.-M., Ceunen, J., & Berckmans, D. (2003). Computer-assisted image analysis to quantify daily growth rates of broiler chickens. British Poultry Science, 44(4), 524–532. https://doi.org/10.1080/0007166031000085516
Down to Earth. (2024, November 10). COP26: How India is paving the way towards climate action. https://www.downtoearth.org.in/blog/climate-change/cop26-how-india-is-paving-the-way-towards-climate-action-80072
Fogarty, E. S., Cronin, G., Swain, D., Moraes, L., & Trotter, M. (2019). Development of a predictive model to identify the day of lambing in extensive sheep systems using autonomous GNSS. In Precision Livestock Farming '19: Proceedings of the 9th European Conference on Precision Livestock Farming (pp. 84–87). University College Cork.
Franzo, G., Legnardi, M., Faustini, G., Tucciarone, C. M., & Cecchinato, M. (2023). When everything becomes bigger: Big data for big poultry production. Animals, 13(11), 1804.
Gernand, E., König, S., & Kipp, C. (2019). Influence of on-farm measurements for heat stress indicators on dairy cow productivity, female fertility and health. Journal of Dairy Science, 102(7), 6660–6671. https://doi.org/10.3168/jds.2018-16011
Gonçalves, P., Nóbrega, L., Monteiro, A., Pedreiras, P., Rodrigues, P., & Esteves, F. (2021). SheepIT, an E-Shepherd system for weed control in vineyards: Experimental results and lessons learned. Animals, 11(9), 2625. https://doi.org/10.3390/ani11092625
Gurule, S. C., Tobin, C. T., Bailey, D. W., & Gifford, J. A. H. (2021). Evaluation of the tri-axial accelerometer to identify and predict parturition-related activities of Debouillet ewes in an intensive setting. Applied Animal Behaviour Science, 237, 105296. https://doi.org/10.1016/j.applanim.2021.105296
Halachmi, I., & Guarino, M. (2016). Editorial: Precision livestock farming: A ‘per animal’ approach using advanced monitoring technologies. Animal, 10(10), 1482–1483.
Hartung, H., Lehr, H., Rosés, D., Mergeay, M., & van den Bossche, J. (2019). ChickenBoy: A farmer assistance system for better animal welfare, health and farm productivity. In B. O’Brien, D. Hennessy, & L. Shalloo (Eds.), Precision Livestock Farming’19: Proceedings of the 9th European Conference on Precision Livestock Farming (pp. 529–536). University College Cork.
Haykin, S. (n.d.). Redes neurais: Princípios e prática. Bookman.
Hempel, S., Adolphs, J., Landwehr, N., Willink, D., Janke, D., & Amon, T. (2020). Supervised machine learning to assess methane emissions of a dairy building with natural ventilation. Applied Sciences, 10(19), 6938.
Herlin, A., Brunberg, E., Hultgren, J., Högberg, N., Rydberg, A., & Skarin, A. (2021). Animal welfare implications of digital tools for monitoring and management of cattle and sheep on pasture. Animals, 11(3), 829.
Hernandez, R. O., Sánchez, J. A., & Romero, M. H. (2020). Iceberg indicators for animal welfare in rural sheep farms using the five domains model approach. Animals, 10(12), 2273.
Higgins, K. T. (2003). Engineering RandD: Temperature readings by remote control. Food Engineering Magazine.
Hill, P. R., Kumar, A., Temimi, M., & Bull, D. R. (2020). HABNet: Machine learning, remote sensing-based detection of harmful algal blooms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3229–3239.
Hostettor, J. (2003, February 16). Animal-tracking chips now let you in on how Fido is feeling. USA Today.
Johnson, P. L., Welsh, A., Knowler, K., & Pletnykov, P. (2021). Investigating the potential for global positioning satellite data to provide information on ewe behaviour around the time of lambing. New Zealand Journal of Animal Science and Production, 81, 29–34.
Kashiha, M., Bahr, C., Hempel, S., Sharifi, A. R., & Ogilvie, S. (2014). Automatic weight estimation of individual pigs using image analysis. Computers and Electronics in Agriculture, 107, 182–189.
Komlatskiy, V., & Smolkin, R. (n.d.). Precision technologies in pig farming. Kuban State Agrarian University / Big Dutchman, 12, 12–17.
Kuip, A. (1987). Animal identification. In Third Symposium on Automation in Dairying. Wageningen, The Netherlands.
Li, N., Ren, Z., Li, D., & Zeng, L. (2020). Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: Towards the goal of precision livestock farming. Animal, 14(3), 617–625.
Lu, M. Z., He, J., Chen, C., Okinda, C., Shen, M., Liu, L., Deng, Y. Z., & Berckmans, D. (2018). An automatic ear base temperature extraction method for top view piglet thermal image. Computers and Electronics in Agriculture, 155, 339–347.
Maselyne, J., Adriaens, I., Huybrechts, T., De Ketelaere, B., Millet, S., Vangeyte, J., Van Nuffel, A., & Saeys, W. (2016). Measuring the drinking behaviour of individual pigs housed in group using radio frequency identification (RFID). Animal, 10(9), 1557–1566.
McManus, C., Tanure, B., Peripolli, V., Seixas, L., Fischer, V., Gabbi, A. M., de Almeida, F. R. P., & Dias, E. (2016). Infrared thermography in animal production: An overview. Computers and Electronics in Agriculture, 123, 10–16.
Mellor, D. J. (2016). Updating animal welfare thinking: Moving beyond the “Five Freedoms” towards “A Life Worth Living”. Animals, 6(3), 21.
Mellor, D. J. (2017). Operational details of the five domains model and its key applications to the assessment and management of animal welfare. Animals, 7(8), 60.
Mellor, D. J., & Beausoleil, N. J. (2015). Extending the ‘Five Domains’ model for animal welfare assessment to incorporate positive welfare states. Animal Welfare, 24(3), 241–253.
Mellor, D. J., Beausoleil, N. J., Littlewood, K. E., McLean, A. N., McGreevy, P. D., Jones, B., & Wilkins, C. (2020). The 2020 Five Domains Model: Including human–animal interactions in assessments of animal welfare. Animals, 10(10), 1870.
Meunier, B., Pradel, P., Sloth, K. H., Cirié, C., Delval, E., Mialon, M. M., & Veissier, I. (2018). Image analysis to refine measurements of dairy cow behaviour from a real-time location system. Biosystems Engineering, 173, 32–44.
Meyer, M. M., Johnson, A. K., & Bobeck, E. A. (2020). A novel environmental enrichment device increased physical activity and walking distance in broilers. Poultry Science, 99(1), 48–60.
Moran, D., & Blair, K. J. (2021). Sustainable livestock systems: Anticipating demand-side challenges. Animal, 15(S1), 100288.
Morris, J. E., Cronin, G. M., & Bush, R. D. (2012). Improving sheep production and welfare in extensive systems through precision sheep management. Animal Production Science, 52(8), 665–670.
Munoz, C. A., Campbell, A., Hemsworth, P. H., & Doyle, R. E. (2019). Evaluating the welfare of extensively managed sheep. PLoS ONE, 14(6), e0218603.
Neethirajan, S., & Kemp, B. (2021). Digital livestock farming. Sensing and Bio-Sensing Research, 32, 100403.
Ochs, D. S., Wolf, C. A., Widmar, N. J., & Bir, C. (2018). Consumer perceptions of egg-laying hen housing systems. Poultry Science, 97(10), 3390–3396. https://doi.org/10.3382/ps/pey205
Olejnik, K., Popiela, E., & Opaliński, S. (2022). Emerging precision management methods in poultry sector. Agriculture, 12(5), 718. https://doi.org/10.3390/agriculture12050718
Olugbenga, S. O., Abayomi, O. O., Oluseye, A. A., & Taiwo, A. T. (2015). Optimized nutrients diet formulation of broiler poultry rations in Nigeria using linear programming. Journal of Nutrition and Food Sciences, 14(1).
Park, J. H., & Han, M. H. (2023). Enhancing livestock management with IoT-based wireless sensor networks: A comprehensive approach for health monitoring, location tracking, behavior analysis and environmental optimization. Journal of Sustainable Urban Futures, 13(6), 34–46.
Peterson, K. T., Sagan, V., & Sloan, J. J. (2020). Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. GIScience and Remote Sensing, 57(4), 510–525. https://doi.org/10.1080/15481603.2020.1768212
Pyo, J., Duan, H., Baek, S., Kim, M. S., Jeon, T., Kwon, Y. S., & Cho, K. H. (2019). A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery. Remote Sensing of Environment, 233, 111350. https://doi.org/10.1016/j.rse.2019.111350
Reis, M. P., Gous, R. M., Hauschild, L., & Sakomura, N. K. (2023). Evaluation of a mechanistic model that estimates feed intake, growth and body composition, nutrient requirements and optimum economic response of broilers. Animal, 17(2), 100612. https://doi.org/10.1016/j.animal.2023.100612
Richeson, J. T., Lawrence, T., & White, B. J. (2018). Using advanced technologies to quantify beef cattle behavior. Translational Animal Science, 2(4), 223–229. https://doi.org/10.1093/tas/txy036
Roberts, S. J., Cain, R., & Dawkins, M. S. (2012). Prediction of welfare outcomes for broiler chickens using Bayesian regression on continuous optical flow data. Journal of the Royal Society Interface, 9(75), 3436–3443.
Sagan, V., Peterson, K. T., Maimaitijiang, M., Sidike, P., Sloan, J., Greeling, B. A., Maalouf, S., & Adams, C. (2020). Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning and cloud computing. Earth-Science Reviews, 205, 103187.
Salem, A., & Mohamed, M. (2024). Modeling livestock procedures toward precision and sustainable livestock farm in era of virtual technologies: Lessons, opportunities, avenues of digitalization. In Smart Agriculture and Precision Farming (Vol. 1, pp. 41–57). Springer.
Schaefer, A. L., Cook, N. J., Tessaro, S., Deregt, D., Desroches, G., Dubeski, P. L., Tong, A. K. W., & Godson, D. L. (2004). Early detection and prediction of infection using infrared thermography. Canadian Journal of Animal Science, 84(1), 73–80.
Sejian, V., Silpa, M., Nair, M. R., Devaraj, C., Krishnan, G., Bagath, M., Chauhan, S., Suganthi, R., Fonseca, V., & König, S. (2021). Heat stress and goat welfare: Adaptation and production considerations. Animals, 11(4), 1021.
Shi, R., Irfan, M., Liu, G., Yang, X., & Su, X. (2022). Analysis of the impact of livestock structure on carbon emissions of animal husbandry: A sustainable way to improving public health and green environment. Frontiers in Public Health, 10, 835210. https://doi.org/10.3389/fpubh.2022.835210
Shine, P., Huth, I., O’Brien, B., Murphy, J. P., Healy, M. G., & Humphreys, J. (2018). Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture-based dairy farms. Computers and Electronics in Agriculture, 150, 74–87. https://doi.org/10.1016/j.compag.2018.03.023
Thumba, D. A., Lazarova-Molnar, S., & Niloofar, P. (2020). Data-driven decision support tools for reducing GHG emissions from livestock production systems: Overview and challenges. In Proceedings of the 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS) (pp. 1–8). IEEE. https://doi.org/10.1109/IOTSMS52051.2020.9340161
Vaintrub, M. O., Levit, H., Chincarini, M., Fusaro, I., Giammarco, M., & Vignola, G. (2020). Precision livestock farming, automats and new technologies: Possible applications in extensive dairy sheep farming. Animal, 15(S1), 100143.
Van Hertem, T., Alchanatis, V., Antler, A., Maltz, E., Halachmi, I., Schlageter-Tello, A., Viazzi, S., Bahr, C., & Berckmans, D. (2013). Comparison of segmentation algorithms for cow contour extraction from natural barn background in side view images. Computers and Electronics in Agriculture, 91, 65–74. https://doi.org/10.1016/j.compag.2012.12.003
Wada, N., Shinya, M., & Shiraishi, M. (2013). Pig face recognition using eigenspace method. ITE Transactions on Media Technology and Applications, 1(4), 328–332.
Wang, K., Pan, J., Rao, X., Yang, Y., Wang, F., Zheng, R., & Ying, Y. (2018). An image-assisted rod-platform weighing system for weight information sampling of broilers. Transactions of the ASABE, 61(2), 631–640.
Wu, Y., & Takács-György, K. (2022). Comparison of consuming habits on organic food—Is it the same? Hungary versus China. Sustainability, 14(13), 7800.
Xin, H., & Liu, K. (2017). Precision livestock farming in egg production. Animal Frontiers, 7(1), 24–31.
Xin, H., Berry, I. L., Barton, T. L., & Tabler, G. T. (1994). Feed and water consumption, growth and mortality of male broilers. Poultry Science, 73(4), 610–616.
Yang, Q., Xiao, D., & Lin, S. (2018). Feeding behavior recognition for group-housed pigs with the faster R-CNN. Computers and Electronics in Agriculture, 155, 453–460. https://doi.org/10.1016/j.compag.2018.11.002
Yaxley, K. J., Joiner, K. F., & Abbass, H. (2021). Drone approach parameters leading to lower stress sheep flocking and movement: Sky shepherding. Scientific Reports, 11(1), 7803.
Zhang, F., Zhou, W., Shen, W., Yang, Q., & Wang, W. (2016). An automatic model configuration and optimization system for milk production forecasting. Computers and Electronics in Agriculture, 128, 100–111. https://doi.org/10.1016/j.compag.2016.08.016
Zheng, C., Zhu, X. M., Yang, X., Wang, L., Tu, S., & Xue, Y. (2018). Automatic recognition of lactating sow postures from depth images by deep learning detector. Computers and Electronics in Agriculture, 147, 51–63. https://doi.org/10.1016/j.compag.2018.01.023