FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Sensors (Basel) / Machine Learning-Based Sensor Data Fusion for Animal Monitor...
Sensors (Basel, Switzerland)2023; 23(12); 5732; doi: 10.3390/s23125732

Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review.

Abstract: The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts.
Get Full Text
Publication Date: 2023-06-20 PubMed ID: 37420896PubMed Central: PMC10305307DOI: 10.3390/s23125732Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article
  • Review

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research review analyses how modern technologies like artificial intelligence and Internet of Things, which facilitate data collection through sensors, have been used in animal research. It categorizes and analyzes the application of sensor fusion algorithms in understanding animal behavior for improved welfare, productivity and conservation efforts.

Introduction

  • The research study on hand is a specialized ‘scoping review’, focusing on the applications of the fields of artificial intelligence and the Internet of Things (IoT) in animal conservation.
  • The primary technologies considered in the review are sensing devices enabled with AI and IoT, that can process large volumes of data to extract significant insights about animal behavior.
  • The focus is on how these technologies are used to understand behaviors related to illness detection, interpreting the emotional state of animals, and even recognizing individual animals.

Methodology

  • The study included English language articles published in a time frame of over a decade, from 2011 to 2022.
  • Out of 263 gathered articles, only 23 met the set inclusion criteria and were chosen for detailed analysis.

Classification and Analysis of Sensor Fusion Algorithms

  • The sensor fusion algorithms extracted from the eligible articles were categorized into three levels: Raw or low-level fusion (26%), Feature or medium-level fusion (39%), and Decision or high-level fusion (34%).
  • The algorithms were majorly used for detection of posture and activity in animals with cows (32%) and horses (12%) being the main target species in the three levels of fusion.
  • The accelerometer, a device that measures proper acceleration, was a common feature across all levels of fusion and data collection.

Conclusion and Future Scope

  • The review concludes that the exploration of sensor fusion in animal research is still in its initial stage and is ripe for further exploration and development.
  • There is a substantial potential for combining movement data with biometrics to develop comprehensive animal welfare applications.
  • The integration of sensor fusion and machine learning algorithms can significantly enhance the understanding of animal behavior, leading to beneficial outcomes for animal welfare, increasing production efficiency, and bolstering conservation efforts.

Cite This Article

APA
Aguilar-Lazcano CA, Espinosa-Curiel IE, Ríos-Martínez JA, Madera-Ramírez FA, Pérez-Espinosa H. (2023). Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review. Sensors (Basel), 23(12), 5732. https://doi.org/10.3390/s23125732

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 23
Issue: 12
PII: 5732

Researcher Affiliations

Aguilar-Lazcano, Carlos Alberto
  • CICESE-UT3, Tepic 63173, Mexico.
Espinosa-Curiel, Ismael Edrein
  • CICESE-UT3, Tepic 63173, Mexico.
Ríos-Martínez, Jorge Alberto
  • Computer Science Department, Faculty of Mathematics, Autonomous University of Yucatan, Merida 97000, Mexico.
Madera-Ramírez, Francisco Alejandro
  • Computer Science Department, Faculty of Mathematics, Autonomous University of Yucatan, Merida 97000, Mexico.
Pérez-Espinosa, Humberto
  • CICESE-UT3, Tepic 63173, Mexico.

MeSH Terms

  • Female
  • Cattle
  • Animals
  • Horses
  • Artificial Intelligence
  • Machine Learning
  • Algorithms
  • Movement
  • Biometry

Conflict of Interest Statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

This article includes 54 references
  1. Allam Z, Dhunny ZA. On big data, artificial intelligence and smart cities. Cities 2019;89:80–91.
    doi: 10.1016/j.cities.2019.01.032google scholar: lookup
  2. Andronie M, Lăzăroiu G, Iatagan M, Uță C, Ștefănescu R, Cocoșatu M. Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems. Electronics 2021;10:2497.
    doi: 10.3390/electronics10202497google scholar: lookup
  3. Chui KT, Lytras MD, Visvizi A. Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption. Energies 2018;11:2869.
    doi: 10.3390/en11112869google scholar: lookup
  4. Misra NN, Dixit Y, Al-Mallahi A, Bhullar MS, Upadhyay R, Martynenko A. IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry. IEEE Internet Things J 2022;9:6305–6324.
    doi: 10.1109/JIOT.2020.2998584google scholar: lookup
  5. Zhang Z, Wen F, Sun Z, Guo X, He T, Lee C. Artificial Intelligence-Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin. Adv. Intell. Syst. 2022;4:2100228.
    doi: 10.1002/aisy.202100228google scholar: lookup
  6. Cabezas J, Yubero R, Visitación B, Navarro-García J, Algar MJ, Cano EL, Ortega F. Analysis of Accelerometer and GPS Data for Cattle Behaviour Identification and Anomalous Events Detection.. Entropy (Basel) 2022 Feb 26;24(3).
    doi: 10.3390/e24030336pmc: PMC8947510pubmed: 35327847google scholar: lookup
  7. Kasnesis P, Doulgerakis V, Uzunidis D, Kogias DG, Funcia SI, González MB, Giannousis C, Patrikakis CZ. Deep Learning Empowered Wearable-Based Behavior Recognition for Search and Rescue Dogs.. Sensors (Basel) 2022 Jan 27;22(3).
    doi: 10.3390/s22030993pmc: PMC8840386pubmed: 35161741google scholar: lookup
  8. Castanedo F. A review of data fusion techniques.. ScientificWorldJournal 2013;2013:704504.
    doi: 10.1155/2013/704504pmc: PMC3826336pubmed: 24288502google scholar: lookup
  9. Alberdi A, Aztiria A, Basarab A. Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review.. J Biomed Inform 2016 Feb;59:49-75.
    doi: 10.1016/j.jbi.2015.11.007pubmed: 26621099google scholar: lookup
  10. Novak D, Riener R. A survey of sensor fusion methods in wearable robotics. Robot. Auton. Syst. 2015;73:155–170.
    doi: 10.1016/j.robot.2014.08.012google scholar: lookup
  11. Meng T, Jing X, Yan Z, Pedrycz W. A survey on machine learning for data fusion. Inf. Fusion 2020;57:115–129.
    doi: 10.1016/j.inffus.2019.12.001google scholar: lookup
  12. Elmenreich W. An Introduction to Sensor Fusion. Vienna University of Technology; Vienna, Austria: 2002; pp. 1–28.
  13. Feng L, Zhao Y, Sun Y, Zhao W, Tang J. Action Recognition Using a Spatial-Temporal Network for Wild Felines.. Animals (Basel) 2021 Feb 12;11(2).
    doi: 10.3390/ani11020485pmc: PMC7917733pubmed: 33673162google scholar: lookup
  14. Jin Z, Guo L, Shu H, Qi J, Li Y, Xu B, Zhang W, Wang K, Wang W. Behavior Classification and Analysis of Grazing Sheep on Pasture with Different Sward Surface Heights Using Machine Learning.. Animals (Basel) 2022 Jul 7;12(14).
    doi: 10.3390/ani12141744pmc: PMC9311692pubmed: 35883291google scholar: lookup
  15. Kaler J, Mitsch J, Vázquez-Diosdado JA, Bollard N, Dottorini T, Ellis KA. Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep.. R Soc Open Sci 2020 Jan;7(1):190824.
    doi: 10.1098/rsos.190824pmc: PMC7029909pubmed: 32218931google scholar: lookup
  16. Leoni J, Tanelli M, Strada SC, Berger-Wolf T. Data-Driven Collaborative Intelligent System for Automatic Activities Monitoring of Wild Animals. Proceedings of the 2020 IEEE International Conference on Human-Machine Systems (ICHMS); Rome, Italy. 7–9 September 2020; pp. 1–6.
    doi: 10.1109/ICHMS49158.2020.9209350google scholar: lookup
  17. Neethirajan S. The role of sensors, big data and machine learning in modern animal farming. Sens. Bio-Sens. Res. 2020;29:100367.
    doi: 10.1016/j.sbsr.2020.100367google scholar: lookup
  18. Fuentes S, Gonzalez Viejo C, Tongson E, Dunshea FR. The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence.. Anim Health Res Rev 2022 Jun;23(1):59-71.
    doi: 10.1017/S1466252321000177pubmed: 35676797google scholar: lookup
  19. Isabelle DA, Westerlund M. A Review and Categorization of Artificial Intelligence-Based Opportunities in Wildlife, Ocean and Land Conservation. Sustainability 2022;14:1979.
    doi: 10.3390/sᐄ1979google scholar: lookup
  20. Neethirajan S. Recent advances in wearable sensors for animal health management. Sens. Bio-Sens. Res. 2017;12:15–29.
    doi: 10.1016/j.sbsr.2016.11.004google scholar: lookup
  21. Stygar AH, Gómez Y, Berteselli GV, Dalla Costa E, Canali E, Niemi JK, Llonch P, Pastell M. A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle.. Front Vet Sci 2021;8:634338.
    doi: 10.3389/fvets.2021.634338pmc: PMC8044875pubmed: 33869317google scholar: lookup
  22. Yaseer A, Chen H. A Review of Sensors and Machine Learning in Animal Farming. Proceedings of the 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER); Jiaxing, China. 27–31 July 2021; pp. 747–752.
    doi: 10.1109/CYBER53097.2021.9588295google scholar: lookup
  23. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, Moher D, Peters MDJ, Horsley T, Weeks L, Hempel S, Akl EA, Chang C, McGowan J, Stewart L, Hartling L, Aldcroft A, Wilson MG, Garritty C, Lewin S, Godfrey CM, Macdonald MT, Langlois EV, Soares-Weiser K, Moriarty J, Clifford T, Tunçalp Ö, Straus SE. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation.. Ann Intern Med 2018 Oct 2;169(7):467-473.
    doi: 10.7326/M18-0850pubmed: 30178033google scholar: lookup
  24. Kmet L, Lee R, Cook L. Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields. Institute of Health Economics; Dhaka, Bangladesh: 2004.
  25. Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press; Cambridge, MA, USA: 2016.
  26. Ray S. A Quick Review of Machine Learning Algorithms. Proceedings of the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon); Faridabad, India. 14–16 February 2019; pp. 35–39.
    doi: 10.1109/COMITCon.2019.8862451google scholar: lookup
  27. Aich S, Chakraborty S, Sim JS, Jang DJ, Kim HC. The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning. Appl. Sci. 2019;9:4938.
    doi: 10.3390/app9224938google scholar: lookup
  28. Arablouei R, Wang Z, Bishop-Hurley GJ, Liu J. Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data. arXiv 2022.
    doi: 10.48550/ARXIV.2206.12078google scholar: lookup
  29. Bocaj E, Uzunidis D, Kasnesis P, Patrikakis CZ. On the Benefits of Deep Convolutional Neural Networks on Animal Activity Recognition. Proceedings of the 2020 International Conference on Smart Systems and Technologies (SST); Osijek, Croatia. 14–16 October 2020; pp. 83–88.
    doi: 10.1109/SST49455.2020.9263702google scholar: lookup
  30. Byabazaire J, Olariu C, Taneja M, Davy A. Lameness Detection as a Service: Application of Machine Learning to an Internet of Cattle. Proceedings of the 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC); Las Vegas, NV, USA. 11–14 January 2019; pp. 1–6.
    doi: 10.1109/CCNC.2019.8651681google scholar: lookup
  31. Corcoran E, Denman S, Hanger J, Wilson B, Hamilton G. Automated detection of koalas using low-level aerial surveillance and machine learning.. Sci Rep 2019 Mar 1;9(1):3208.
    doi: 10.1038/s41598-019-39917-5pmc: PMC6397288pubmed: 30824795google scholar: lookup
  32. Dziak D, Gradolewski D, Witkowski S, Kaniecki D, Jaworski A, Skakuj M, Kulesza WJ. Airport Wildlife Hazard Management System. Elektron. Ir Elektrotechnika 2022;28:45–53.
    doi: 10.5755/j02.eie.31418google scholar: lookup
  33. Hou J, Strand-Amundsen R, Tronstad C, Høgetveit JO, Martinsen ØG, Tønnessen TI. Automatic Prediction of Ischemia-Reperfusion Injury of Small Intestine Using Convolutional Neural Networks: A Pilot Study.. Sensors (Basel) 2021 Oct 8;21(19).
    doi: 10.3390/s21196691pmc: PMC8512235pubmed: 34641009google scholar: lookup
  34. Huang K, Li C, Zhang J, Wang B. Cascade and Fusion: A Deep Learning Approach for Camouflaged Object Sensing.. Sensors (Basel) 2021 Aug 13;21(16).
    doi: 10.3390/s21165455pmc: PMC8400738pubmed: 34450897google scholar: lookup
  35. Luo Y, Zeng Z, Lu H, Lv E. Posture Detection of Individual Pigs Based on Lightweight Convolution Neural Networks and Efficient Channel-Wise Attention.. Sensors (Basel) 2021 Dec 15;21(24).
    doi: 10.3390/s21248369pmc: PMC8705977pubmed: 34960477google scholar: lookup
  36. Mao A, Huang E, Gan H, Parkes RSV, Xu W, Liu K. Cross-Modality Interaction Network for Equine Activity Recognition Using Imbalanced Multi-Modal Data.. Sensors (Basel) 2021 Aug 29;21(17).
    doi: 10.3390/s21175818pmc: PMC8434387pubmed: 34502709google scholar: lookup
  37. Rahman A, Smith DV, Little B, Ingham AB, Greenwood PL, Bishop-Hurley GJ. Cattle behaviour classification from collar, halter, and ear tag sensors. Inf. Process. Agric. 2018;5:124–133.
    doi: 10.1016/j.inpa.2017.10.001google scholar: lookup
  38. Ren K, Bernes G, Hetta M, Karlsson J. Tracking and analysing social interactions in dairy cattle with real-time locating system and machine learning. J. Syst. Archit. 2021;116:102139.
    doi: 10.1016/j.sysarc.2021.102139google scholar: lookup
  39. Rios-Navarro A, Dominguez-Morales JP, Tapiador-Morales R, Dominguez-Morales M, Jimenez-Fernandez A, Linares-Barranco A. A Sensor Fusion Horse Gait Classification by a Spiking Neural Network on SpiNNaker. In: Villa AEP, Masulli P, Pons Rivero AJ, editors. Proceedings of the Artificial Neural Networks and Machine Learning—ICANN 2016, Barcelona, Spain, 6–9 September 2016. Springer International Publishing; Cham, Switzerland: 2016. pp. 36–44.
  40. Schmeling L, Elmamooz G, Hoang PT, Kozar A, Nicklas D, Sünkel 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 (Basel) 2021 Sep 10;11(9).
    doi: 10.3390/ani11092660pmc: PMC8468529pubmed: 34573627google scholar: lookup
  41. Sturm V, Efrosinin D, Öhlschuster M, Gusterer E, Drillich M, Iwersen M. Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows.. Sensors (Basel) 2020 Mar 8;20(5).
    doi: 10.3390/s20051484pmc: PMC7085771pubmed: 32182701google scholar: lookup
  42. Tian F, Wang J, Xiong B, Jiang L, Song Z, Li F. Real-Time Behavioral Recognition in Dairy Cows Based on Geomagnetism and Acceleration Information. IEEE Access 2021;9:109497–109509.
    doi: 10.1109/ACCESS.2021.3099212google scholar: lookup
  43. Wang G, Muhammad A, Liu C, Du L, Li D. Automatic Recognition of Fish Behavior with a Fusion of RGB and Optical Flow Data Based on Deep Learning.. Animals (Basel) 2021 Sep 23;11(10).
    doi: 10.3390/ani11102774pmc: PMC8532692pubmed: 34679796google scholar: lookup
  44. Xu H, Li S, Lee C, Ni W, Abbott D, Johnson M, Lea JM, Yuan J, Campbell DLM. Analysis of Cattle Social Transitional Behaviour: Attraction and Repulsion.. Sensors (Basel) 2020 Sep 18;20(18).
    doi: 10.3390/s20185340pmc: PMC7570944pubmed: 32961892google scholar: lookup
  45. Schollaert Uz S, Ames TJ, Memarsadeghi N, McDonnell SM, Blough NV, Mehta AV, McKay JR. Supporting Aquaculture in the Chesapeake Bay Using Artificial Intelligence to Detect Poor Water Quality with Remote Sensing. Proceedings of the IGARSS 2020—2020 IEEE International Geoscience and Remote Sensing Symposium; Waikoloa, HI, USA. 26 September–2 October 2020; pp. 3629–3632.
    doi: 10.1109/IGARSS39084.2020.9323465google scholar: lookup
  46. de Chaumont F, Ey E, Torquet N, Lagache T, Dallongeville S, Imbert A, Legou T, Le Sourd AM, Faure P, Bourgeron T, Olivo-Marin JC. Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning.. Nat Biomed Eng 2019 Nov;3(11):930-942.
    doi: 10.1038/s41551-019-0396-1pubmed: 31110290google scholar: lookup
  47. Narayanaswami R, Gandhe A, Tyurina A, Mehra RK. Sensor fusion and feature-based human/animal classification for Unattended Ground Sensors. Proceedings of the 2010 IEEE International Conference on Technologies for Homeland Security (HST); Waltham, MA, USA. 8–10 November 2010; pp. 344–350.
    doi: 10.1109/THS.2010.5655025google scholar: lookup
  48. Dick S, Bracho CC. Learning to Detect the Onset of Disease in Cattle from Feedlot Watering Behavior. Proceedings of the NAFIPS 2007—2007 Annual Meeting of the North American Fuzzy Information Processing Society; San Diego, CA. 24–27 June 2007; pp. 112–116.
    doi: 10.1109/NAFIPS.2007.383821google scholar: lookup
  49. Roberts PLD, Jaffe JS, Trivedi MM. Multiview, Broadband Acoustic Classification of Marine Fish: A Machine Learning Framework and Comparative Analysis. IEEE J. Ocean. Eng. 2011;36:90–104.
    doi: 10.1109/JOE.2010.2101235google scholar: lookup
  50. Jin X, Gupta S, Ray A, Damarla T. Multimodal sensor fusion for personnel detection. Proceedings of the 14th International Conference on Information Fusion; Chicago, IL, USA. 5–8 July 2011; pp. 1–8.
  51. Tian Q, Xia S, Cao M, Cheng K. Reliable Sensing Data Fusion Through Robust Multiview Prototype Learning. IEEE Trans. Ind. Inform. 2022;18:2665–2673.
    doi: 10.1109/TII.2021.3064358google scholar: lookup
  52. Jukan A, Masip-Bruin X, Amla N. Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review. ACM Comput. Surv. 2017;50:1–27.
    doi: 10.1145/3041960google scholar: lookup
  53. Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B, Mathis A, Mathis MW, van Langevelde F, Burghardt T, Kays R, Klinck H, Wikelski M, Couzin ID, van Horn G, Crofoot MC, Stewart CV, Berger-Wolf T. Perspectives in machine learning for wildlife conservation.. Nat Commun 2022 Feb 9;13(1):792.
    doi: 10.1038/s41467-022-27980-ypmc: PMC8828720pubmed: 35140206google scholar: lookup
  54. Asmare B. A Review of Sensor Technologies Applicable for Domestic Livestock Production and Health Management. Adv. Agric. 2022;2022:e1599190.
    doi: 10.1155/2022/1599190google scholar: lookup

Citations

This article has been cited 11 times.
  1. Condotta ICFS, Tedeschi LO. ASAS-NANP Symposium: Mathematical modeling in animal nutrition: revolutionizing animal farming with artificial intelligence: trends, challenges, and opportunities. J Anim Sci 2026 Jan 8;104.
    doi: 10.1093/jas/skaf441pubmed: 41410516google scholar: lookup
  2. Jobarteh B, Mincu-Iorga M, Gavojdian D, Neethirajan S. Integrating multi-modal data fusion approaches for analysis of dairy cattle vocalizations. Front Vet Sci 2025;12:1704031.
    doi: 10.3389/fvets.2025.1704031pubmed: 41334225google scholar: lookup
  3. Hashimoto Y, Zin TT, Tin P, Kobayashi I, Hama H. Generating Accurate Activity Patterns for Cattle Farm Management Using MCMC Simulation of Multiple-Sensor Data System. Sensors (Basel) 2025 Nov 5;25(21).
    doi: 10.3390/s25216781pubmed: 41229004google scholar: lookup
  4. Raza A, Hanif F, Mohammed HA. Analyzing the enhancement of CNN-YOLO and transformer based architectures for real-time animal detection in complex ecological environments. Sci Rep 2025 Nov 7;15(1):39142.
    doi: 10.1038/s41598-025-26645-2pubmed: 41203810google scholar: lookup
  5. Li G, Komi S, Sorensen JF, Berg RW. A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis. Sensors (Basel) 2025 Jul 9;25(14).
    doi: 10.3390/s25144289pubmed: 40732417google scholar: lookup
  6. Liao S, Shu Y, Tian F, Zhou Y, Li G, Zhang C, Yao C, Wang Z, Che L. Multi-Modality Sheep Face Recognition Based on Deep Learning. Animals (Basel) 2025 Apr 11;15(8).
    doi: 10.3390/ani15081111pubmed: 40281945google scholar: lookup
  7. Gao G, Ma Y, Wang J, Li Z, Wang Y, Bai H. CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement. Sensors (Basel) 2025 Feb 11;25(4).
    doi: 10.3390/s25041084pubmed: 40006313google scholar: lookup
  8. Janitri V, ArulJothi KN, Ravi Mythili VM, Singh SK, Prasher P, Gupta G, Dua K, Hanumanthappa R, Karthikeyan K, Anand K. The roles of patient-derived xenograft models and artificial intelligence toward precision medicine. MedComm (2020) 2024 Oct;5(10):e745.
    doi: 10.1002/mco2.745pubmed: 39329017google scholar: lookup
  9. Sadrzadeh N, Foris B, Krahn J, von Keyserlingk MAG, Weary DM. Automated monitoring of brush use in dairy cattle. PLoS One 2024;19(6):e0305671.
    doi: 10.1371/journal.pone.0305671pubmed: 38917231google scholar: lookup
  10. Imada J, Arango-Sabogal JC, Bauman C, Roche S, Kelton D. Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat Milk Tests. Animals (Basel) 2024 Apr 5;14(7).
    doi: 10.3390/ani14071113pubmed: 38612352google scholar: lookup
  11. Hu S, Reverter A, Arablouei R, Bishop-Hurley G, McNally J, Alvarenga F, Ingham A. Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data. Animals (Basel) 2024 Jan 18;14(2).
    doi: 10.3390/ani14020301pubmed: 38254470google scholar: lookup
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.