Digital Sentience? Evaluating the Integration of AI-Driven Tools in Animal Welfare Assessment

Sara Platto

Animal Research and One Health ›› 2025, Vol. 3 ›› Issue (3) : 344 -347.

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Animal Research and One Health ›› 2025, Vol. 3 ›› Issue (3) : 344 -347. DOI: 10.1002/aro2.70018
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Digital Sentience? Evaluating the Integration of AI-Driven Tools in Animal Welfare Assessment

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Ai-driven tools / animal welfare / laboratory animals / livestock / pet animals / wild animals

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Sara Platto. Digital Sentience? Evaluating the Integration of AI-Driven Tools in Animal Welfare Assessment. Animal Research and One Health, 2025, 3(3): 344-347 DOI:10.1002/aro2.70018

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References

[1]

A. M. Harvey, N. J. Beausoleil, D. Ramp, and D. J. Mellor, “A Ten-Stage Protocol for Assessing the Welfare of Individual Non-captive Wild Animals: Free- Roaming Horses (Equus Ferus Caballus) as an Example,” Animals10, no. 1 (2020): 148, https://doi.org/10.3390/ani10010148.

[2]

D. J. Mellor, N. J. Beausoleil, K. E. Littlewood, et al., “The 2020 Five Domains Model: Including Human- Animal Interactions in Assessments of Animal Welfare,” Animals10, no. 10 (2020): 1870, https://doi.org/10.3390/ani10101870.

[3]

J. Bao and Q. Xie, “Artificial Intelligence in Animal Farming: A Systematic Literature Review,” Journal of Cleaner Production331 (2022): 129956, https://doi.org/10.1016/j.jclepro.2021.129956.

[4]

A. Michielon, P. Litta, F. Bonelli, et al., “Mind the Step: An Artificial Intelligence-Based Monitoring Platform for Animal Welfare,” Sensors24, no. 24 (2024): 8042, https://doi.org/10.3390/s24248042.

[5]

S. Gupta, “ AI Applications in Animal Behavior Analysis and Welfare,” in Agriculture 4.0 (CRC Press, 2024), 224-244.

[6]

M. Saar, Y. Edan, A. Godo, J. Lepar, Y. Parmet, and I. Halachmi, “A Machine Vision System to Predict Individual Cow Feed Intake of Different Feeds in a Cowshed,” Animal16, no. 1 (2022): 100432, https://doi.org/10.1016/j.animal.2021.100432.

[7]

T. L. G. Kaltenbach, Smart Wildlife Monitoring: Evaluating a Camera Trap Enabled with Artificial Intelligence. (Doctoral dissertation, Montana State University- Bozeman, College of Agriculture), 2024).

[8]

D. Tulasi, A. Granados, P. Gunawardane, A. Kashyap, Z. McDonald, and S. Thulasidasan, “ Smart Camera Traps: Enabling Energy-Efficient Edge-Ai for Remote Monitoring of Wildlife,” in Proceedings of the 1st ACM SIGSPATIAL International Workshop on AI-Driven Spatio-Temporal Data Analysis for Wildlife Conservation (2023), 9-16.

[9]

S. P. Tseng, S. E. Hsu, J. F. Wang, and I. F. Jen, “An Integrated Framework With ADD-LSTM and DeepLabCut for Dolphin Behavior Classification,” Journal of Marine Science and Engineering12, no. 4 (2024): 540, https://doi.org/10.3390/jmse12040540.

[10]

K. Aoki, Y. Watanabe, D. Inamori, N. Funasaka, and K. Q. Sakamoto, “Towards Non-invasive Heart Rate Monitoring in Free-Ranging Cetaceans: A Unipolar Suction Cup Tag Measured the Heart Rate of Trained Risso’s Dolphins,” Philosophical Transactions of the Royal Society B376, no. 1831 (2021): 20200225-20200310, https://doi.org/10.1098/rstb.2020.0225.

[11]

A. Mohan, R. D. Raju, and P. Janarthanan, “ Animal Disease Diagnosis Expert System Using Convolutional Neural Networks,” in 2019 International Conference on Intelligent Sustainable Systems (ICISS) (IEEE, 2019), 441-446.

[12]

Y. Xu, R. Wei, R. Mao, et al., “ Intelligent Pet Protection System Based on IoT Devices,” in 2022 IEEE International Conference on Mechatronics and Automation (ICMA) (IEEE, 2022), 629-634.

[13]

M. G. Diemar, C. A. Krul, M. Teunis, et al., “Report of the First ONTOX Hackathon: Hack to Save Lives and Avoid Animal Suffering. The Use of Artificial Intelligence in Toxicology—A Potential Driver for Reducing/Replacing Laboratory Animals in the Future,” Alternatives to Laboratory Animals53, no. 1 (2025): 42-61, https://doi.org/10.1177/02611929241305112.

[14]

S. Borah, S. Soren, J. Gogoi, and B. Borah, “The Significant Potential of Robotics in Animal Welfare,” Int. J. of Life Sciences13 (2025): 1.

[15]

L. Asher, “ Can Artificial Intelligence Contribute to Our Conceptual Understanding of Animal Welfare?,” in Measuring Behavior. Proceedings of the 13th International Conference on Methods and Techniques in Behavioral Research (Aberdeen, 2024).

[16]

N. Dolensek, D. A. Gehrlach, A. S. Klein, and N. Gogolla, “Facial Expressions of Emotion States and Their Neuronal Correlates in Mice,” Science368, no. 6486 (2020): 89-94, https://doi.org/10.1126/science.aaz9468.

[17]

I. Balki, A. Amirabadi, J. Levman, et al., “Sample-size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review,” Canadian Association of Radiologists Journal70, no. 4 (2019): 344-353, https://doi.org/10.1016/j.carj.2019.06.002.

[18]

J. M. Siegford, J. P. Steibel, J. Han, et al., “The Quest to Develop Automated Systems for Monitoring Animal Behavior,” Applied Animal Behaviour Science265 (2023): 106000, https://doi.org/10.1016/j.applanim.2023.106000.

[19]

A. Weersink, E. Fraser, D. Pannell, E. Duncan, and S. Rotz, “Opportunities and Challenges for Big Data in Agricultural and Environmental Analysis,” Annual Review of Resource Economics10, no. 1 (2018): 19-37, https://doi.org/10.1146/annurev-resource-100516-053654.

[20]

J. Cho, K. Lee, E. Shin, G. Choy, and S. Do, “How Much Data Is Needed to Train a Medical Image Deep Learning System to Achieve Necessary High Accuracy?,” arXiv preprint arXiv:1511.06348 (2015).

[21]

D. Rajput, W. J. Wang, and C. C. Chen, “Evaluation of a Decided Sample Size in Machine Learning Applications,” BMC Bioinformatics24, no. 1 (2023): 48, https://doi.org/10.1186/s12859-023-05156-9.

[22]

O. Guzhva, “ AI in Rose-Coloured Glasses: How Close to an Individual Animal Can (Should) We Come?. Measuring Behavior 2024,” in Proceedings of the 13th International Conference on Methods and Techniques in Behavioral Research (Aberdeen, 2024).

[23]

X. Zhao, R. Tanaka, A. S. Mandour, K. Shimada, and L. Hamabe, “Remote Vital Sensing in Clinical Veterinary Medicine: A Comprehensive Review of Recent Advances, Accomplishments, Challenges, and Future Perspectives,” Animals15, no. 7 (2025): 1033, https://doi.org/10.3390/ani15071033.

[24]

I. Camerlink, “ Interdisciplinary Research to Advance Animal Welfare Science: An Introduction,” in Bridging Research Disciplines to Advance Animal Welfare Science: A Practical Guide. GB (CABI, 2021), 1-16.

[25]

K. Kampourakis, “On the Meaning of Concepts in Science Education,” Science Education27, no. 8 (2018): 591-592, https://doi.org/10.1007/s11191-018-0004-x.

[26]

M. F. Giersberg, J. E. Bolhuis, T. B. Rodenburg, and F. L. B. Meijboom, “ How Smart Should Resilience Be? on the Need of a Transdisciplinary Approach to Transform Pig Production Systems,” in Transforming Food Systems: Ethics, Innovation and Responsibility (EurSafe 2022) D. Bruce and A. Bruce, eds. (Wageningen Academic Publishers, 2022), 513-518, https://doi.org/10.3920/978-90-8686-939-8_80.

[27]

R. B. D'Eath, Precision Livestock Farming Technologies for Pig Welfare - Policy Spotlight. (Policy Spotlight; No. 10) (SRUC's Rural Policy Centre, 2022).

[28]

A. L. Fogel and J. C. Kvedar, “Artificial Intelligence Powers Digital Medicine,” npj Digital Medicine1, no. 1 (2018): 5, https://doi.org/10.1038/s41746-017-0012-2.

[29]

K. Wurtz, I. Camerlink, R. B. D’Eath, et al., “Recording Behaviour of Indoor-Housed Farm Animals Automatically Using Machine Vision Technology: A Systematic Review,” PLoS One14, no. 12 (2019): e0226669, https://doi.org/10.1371/journal.pone.0226669.

[30]

A. Rokem, Y. Wu, and A. Lee, “Assessment of the Need for Separate Test Set and Number of Medical Images Necessary for Deep Learning: A Sub-Sampling Study,” bioRxiv (2017): 196659.

[31]

Y. Gómez, A. H. Stygar, I. J. M. M. Boumans, et al., “A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and its Potential to Assess Animal Welfare,” Frontiers in Veterinary Science8 (2021): 660565, https://doi.org/10.3389/fvets.2021.660565.

[32]

A. Michielon, P. Litta, F. Bonelli, et al., “Mind the Step: An Artificial Intelligence-Based Monitoring Platform for Animal Welfare,” Sensors24 (2024): 8042, https://doi.org/10.3390/s24248042.

[33]

E. Fazzari, D. Romano, F. Falchi, and C. Stefanini, Animal Behavior Analysis Methods Using Deep Learning: A Survey (2024): arXiv preprint arXiv:2405.14002.

[34]

M. F. Giersberg and F. L. Meijboom, “Smart Technologies Lead to Smart Answers? on the Claim of Smart Sensing Technologies to Tackle Animal Related Societal Concerns in Europe over Current Pig Husbandry Systems,” Frontiers in Veterinary Science7 (2021): 588214, https://doi.org/10.3389/fvets.2020.588214.

[35]

J. Schillings, R. Bennett, and D. C. Rose, “Animal Welfare and Other Ethical Implications of Precision Livestock Farming Technology,” CABI Agriculture and Bioscience2, no. 1 (2021): 1-4, https://doi.org/10.1186/s43170-021-00037-8.

[36]

J. Kaler, J. Mitsch, J. A. Vázquez-Diosdado, N. Bollard, T. Dottorini, and K. A. Ellis, “Automated Detection of Lameness in Sheep Using Machine Learning Approaches: Novel Insights into Behavioural Differences Among Lame and Non-lame Sheep,” Royal Society Open Science7, no. 1 (2020): 190824, https://doi.org/10.1098/rsos.190824.

[37]

A. Waterhouse, J. P. Holland, A. McLaren, et al., “ Opportunities and Challenges for Real-Time Management (RTM) in Extensive Livestock Systems,” in The European Conference on Precision Livestock Farming (2019), 20-26.

[38]

J. M. Bos, B. Bovenkerk, P. H. Feindt, and Y. K. Van Dam, “The Quantified Animal: Precision Livestock Farming and the Ethical Implications of Objectification,” Food Ethics2, no. 1 (2018): 77-92, https://doi.org/10.1007/s41055-018-00029-x.

[39]

F. A. Tuyttens, C. F. Molento, and S. Benaissa, “Twelve Threats of Precision Livestock Farming (PLF) for Animal Welfare,” Frontiers in Veterinary Science9 (2022): 889623, https://doi.org/10.3389/fvets.2022.889623.

[40]

R. G. Pinillos, ed, One Welfare: A Framework to Improve Animal Welfare and Human Well-Being (CAB international, 2018).

[41]

A. M. Tarazona, M. C. Ceballos, and D. M. Broom, “Human Relationships With Domestic and Other Animals: One Health, One Welfare, One Biology,” Animals10, no. 1 (2019): 43, https://doi.org/10.3390/ani10010043.

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2025 The Author(s). Animal Research and One Health published by John Wiley & Sons Australia, Ltd on behalf of Institute of Animal Science, Chinese Academy of Agricultural Sciences.

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