HEALTH MONITORING OF EQUINES OF THE MILITARY POLICE OF PARANÁ THROUGH THE USE OF ARTIFICIAL INTELLIGENCE
DOI:
https://doi.org/10.56238/levv16n51-009Keywords:
Police Sciences, Artificial Intelligence, Mounted Policing, Public SecurityAbstract
This article focused on evaluating a technological proposal aimed at developing a remote equine health monitoring system, mediated by Artificial Intelligence (AI). The results of the research led to the conclusion that this technology is fully feasible and will make it possible to monitor the health status of the corporation's animals distributed in the operational units in the capital and in the interior. The system is directly related to measuring the animal's body temperature, monitoring the feeding routine and lameness, where this information, captured by specific hardware distributed in paddocks and stalls, will allow the data to be provided to the AI, which will cross-reference it with the individual information of the monitored equine and, after carrying out the analysis and, if any alterations are identified, will immediately send an alert to those responsible, via SMS, messaging platforms or e-mail, informing them of the alterations found. Regardless of whether or not it finds any alterations in any equine, after the end of the 24-hour cycle, it will issue a report on the entire herd with the information received, thus enabling patterns to be identified and animal health to be monitored. As the system will monitor the animals on a daily basis, it will be possible to provide prompt veterinary intervention and thus improve the corporation's equine animal welfare practices in the health sphere, thereby making it possible to save financial and human resources.
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Andrew, N. G. (N.D.). Machine Learning. Unsupervised Learning. Coursera, Stanford University. Available At: Https://Www.Coursera.Org/Learn/Machine-Learning/Supplement/Nkvj0/Supervised-Learning. Accessed On June 4, 2023.
Andriluka, M., Pishchulin, L., & Gehler, P. 2d Human Pose Estimation: New Benchmark And State Of The Art Analysis. In Proceedings Of The Ieee Conference On Computer Vision And Pattern Recognition, Columbus, Oh, Usa June 23-28, 2014.
Back, W., & Clayton, H. Equine Locomotion. London: Saunders. 2001.
Barge, P., Et Al. (2012). Radio Frequency Identification Technologies For Livestock Management And Meat Supply Chain Traceability. Canadian Journal Of Animal Science, Https://Doi.Org/10.4141/Cjas2012-029, V 1, N 93 P. 23-33. 2012.
Boffi, F. M. (Ed.).Physiology Of Exercise In Horses. Buenos Aires: Inter-Médica.P. 307, 2007.
Boesing, K. Equine Farming, A Strong Arm Of Agribusiness. Available At: Http://2023.Https://Kbbadvocaciarural.Com.Br/2020/05/02/Equinocultura-Um-Braco-Forte-Do-Agronegocio/. Accessed On: June 8, 2023.
Bondaruk, R. L. Manual Of Community Mounted Policing. Curitiba: Avm. P. 129, 2005.
Byström, L. F. J., Et Al. Is Markerless More Or Less? Comparing A Smartphone Computer Vision Method For Equine Lameness Assessment To Multi-Camera Motion Capture. Animals, V 3, N 13, January 24, 2023. Available At: Https://Doi.Org/10.3390/Ani13030390. Accessed On: June 8, 2023.
Bowman, M. C., Et Al. Utilizing Body Temperature To Evaluate Ovulation In Mares. The Professional Animal Scientist, V 23, N 3. Available At: Https://Doi.Org/10.15232/S1080-7446(15)30972-4. Accessed On: June 8, 2023.
Britto, C. V. C. P., Et Al. A Brief Study On Artificial Intelligence Applied To Robotics In Times Of Covid-19. Revista Acadêmica - Ensino De Ciências E Tecnologias, N 8 Jan/Jun, 2021. Available At: Https://Intranet.Cbt.Ifsp.Edu.Br/Qualif/Volume08/Relato02_Ed_08.Pdf. Accessed On: October 22, 2022.
Chatterjee, J., & Dethlefs, N. Deep Learning With Knowledge Transfer For Explainable Anomaly Prediction In Wind Turbines. Wind Energy, V 23, N 8, P. 1693-1710, 2020.
Copeland, B. J. (N.D.). Artificial Intelligence History, Foundations, And Philosophical Issues. Philosophy Of Psychology And Cognitive Science, P. 429-482. Available At: Https://Www.Britannica.Com/Technology/Artificial-Intelligence. Accessed On: June 10, 2023.
Cunningham, J. G. Treatise On Veterinary Physiology (3rd Ed.). Rio De Janeiro: Guanabara Koogan. P. 579, 2004.
D'almeida, Et Al. Welfare In Police Horses In Curitiba. Ciência Rural, V 44, P. 1272-1276, Jul, 2014.
Dernoncourt, F. (N.D.). Introduction To Fuzzy Logic. Mit. Available At: Https://Www.Researchgate.Net/Publication/267041266_Introduction_To_Fuzzy_Logic/Link/54440b5c0cf2a6a049ab0747/Download. Accessed On: June 8, 2023.
Equine Business Association (Eba). (N.D.). Available At: Https://Equinebusinessassociation.Com/Equine-Industry-Statistics/. Accessed On: October 22, 2022.
Evans, D. L. Training And Fitness In Athletic Horses. Sydney: Rirdc 2000.
Ferrari, P. A. P., Et Al. (N.D.). Diagnostic Methods For Equine Lameness - Literature Review. Available At: Http://Faef.Revista.Inf.Br/Imagens_Arquivos/Arquivos_Destaque/Nkmzxocqrzoub4j_2013-6-26-11-0-47.Pdf. Accessed On June 8, 2023.
Feuser, A. K., Et Al. Artificial Intelligence For Lameness Detection In Horses-A Preliminary Study. Animals, N 12, P. 2804, 2022. Available At: Https://Doi.Org/10.3390/Ani12202804. Accessed On June 11, 2023.
Finkenzeller, K. Rfid Handbook: Radio-Frequency Identification Fundamentals And Applications. New York: Wiley, P 304, 1999.
Fraga, C. K. Peculiarities Of Military Police Work. Revista Virtual Textos & Contextos, Porto Alegre, December, 2006. Available At: Https://Revistaseletronicas.Pucrs.Br/Ojs/Index.Php/Fass/Article/Download/1033/812. Accessed On: June 8, 2023.
Fraser, D. (N.D.). Science, Values And Animal Welfare: Exploring The 'Inextricable Connection. Animal Welfare, V 4, N 2, P. 103-117. Available At: Https://Doi.Org/10.1017/S0962728600017516. Accessed On June 11, 2023.
Giacomantonio, C., Et Al. Making And Breaking Barriers: Assessing The Value Of Mounted Police Units In The Uk. Rand Corporation. Cambridge, 2014. Available At: Https://Www.Rand.Org/Content/Dam/Rand/Pubs/Research_Reports/Rr800/Rr830z1/Rand_Rr830z1.Pdf. Accessed On: June 11, 2023.
Granatyr, J. (N.D.). Symbolic Ai Vs. Connectionist Ai. Available At: Https://Iaexpert.Academy/2017/03/23/Ia-Simbolica-X-Ia-Conexionista/. Accessed On October 22, 2022.
Hickman, C. P. Integrated Principles Of Zoology (11th Ed.). Rio De Janeiro: Guanabara Koogan. P. 846, 2004.
Hines, M. T. Clinical Approach To Commonly Encountered Problems. In M. Reed, W. M. Bayly, & D. C. Sellon (Eds.), Equine Internal Medicine (4th Ed., P. 232-310). Usa: Elsevier, 2018
Brazilian Institute Of Geography And Statistics (Ibge). (N.D.). Available At: Https://Www.Ibge.Gov.Br/Explica/Producao-Agropecuaria/Equinos/Br. Accessed On: October 22, 2022.
Laboratory Of Public Policies And The Internet (Lapin). (N.D.). Available At: Https://Lapin.Org.Br/Wp-Content/Uploads/2021/02/Cartillha_Nocoes_Gerais_De_Inteligencia_Artificial.Pdf. Accessed On: October 22, 2022.
Iudith, I., Et Al. Genetic Fingerprint-Innovative Method For Animal Products Traceability In The Context Of Bio-Economy. Procedia Economics And Finance, N 8, P. 414-419, 2014. Available At: Https://Doi.Org/10.1016/S2212-5671(14)00108-7. Accessed On: June 11, 2023.
Kaplan, A., & Haenlein, M. Siri, Siri, In My Hand: Who's The Fairest In The Land? On The Interpretations, Illustrations, And Implications Of Artificial Intelligence. Kelley School Of Business, Indiana University. Published By Elsevier Inc. 2018.
Kamilaris, A., & Prenafeta-Boldú, F. X. Deep Learning In Agriculture: A Survey. Computers And Electronics In Agriculture, N 147, P. 70-90, 2018.
Kil, N., Et Al. Development And Validation Of An Automated Video Tracking Model For Stabled Horses. Animals, N 10, P. 2258, 2020.
Lawson, A. L., Et Al. Application Of An Equine Composite Pain Scale And Its Association With Plasma Adrenocorticotropic Hormone Concentrations And Serum Cortisol Concentrations In Horses With Colic. Equine Veterinary Education, V 32, P. 20-27, 2020.
Lee, J. A., Et Al. Effect Of Temperature-Season On Bovine Adrenal Cortical Function, Blood Cell Profile, And Milk Production. Journal Of Dairy Science, 59(1), 104-108, 1976. Available At: Https://Www.Journalofdairyscience.Org/Article/S0022-0302(76)84163-X/Pdf. Accessed On: October 22, 2022.
Mathis, A., Et Al. Deeplabcut: Markerless Pose Estimation Of User-Defined Body Parts With Deep Learning. Nature Neuroscience, N 21, P. 1281-1289, 2018.
Mathis, M. W., & Mathis, A. Deep Learning Tools For The Measurement Of Animal Behavior In Neuroscience. Current Opinion In Neurobiology, N 60, P. 1-11, 2020.
Nazareno, A. C., Et Al. Electronic Animal Identification: What Are The Applications Of These Methods In Meat Production? Journal Of Animal Behaviour And Biometeorology, 2(4), 142-150, 2014. Available At: Http://Dx.Doi.Org/10.14269/2318-1265/Jabb.V2n4p142-150. Accessed On: October 22, 2022.
Neethirajan, S. The Role Of Sensors, Big Data And Machine Learning In Modern Animal Farming. Sensors And Bio-Sensing Research, P. 29, 2020.
Oliveira, C. A. A., Et Al. Hematological And Blood Gas Parameters' Response To Treadmill Exercise Test In Eventing Horses Fed Different Protein Levels. Journal Of Equine Veterinary Science, V 34, P. 1279-1285, 2014.
Oliveira, K. P. Síndrome Cólica Em Equinos: Revisão De Literatura. Fernandópolis: Universidade Brasil, 2022.
Okut, H. (N.D.). Deep Learning: Long-Short Term Memory. Intechopen. Available At: Https://Www.Researchgate.Net/Publication/352383391_Deep_Learning_Long-Short_Term_Memory. Accessed On: October 22, 2022.
Pedroso, A. C. B. R., Sousa, G. C., & Neves, M. D. (N.D.). Tetanus In A Foal Attended By The Service Of Sanitary Control And Clinical-Surgical Care Of Carriage Horses - Veterinary Hospital. Available At: Http://Serex2012.Proec.Ufg.Br/Uploads/399/Original_Ana_Carolina_Barros_Da_Rosa_Pedroso.Pdf. Accessed On June 8, 2023.
Reiners, K., Hegger, A., Hessel, E. F., Böck, S., Wendl, G., & Van Den Weghe, H. F. A. Application Of Rfid Technology Using Passive Hf Transponders For The Individual Identification Of Weaned Piglets At The Feed Trough. Computers And Electronics In Agriculture, 68, 178-184, 2009.
Robertshaw, D. Fisiologia Dos Animais Domésticos (12th Ed.). Rio De Janeiro: Guanabara Koogan. 2006.
Said, N. C., Geraldo, P., & Paulo, F. D. Glanders In Horses And Biosecurity In Agribusiness. Tekhne E Logos, V 7, N 3, December, 2016. Available At: Http://Revista.Fatecbt.Edu.Br/Index.Php/Tl/Article/Viewfile/404/279. Accessed On: June 8, 2023.
Samuel, A. L. Some Studies In Machine-Learning Using The Game Of Checkers. Ibm Journal, V 3, N 3, P. 535-554, 1959. Available At: Https://Ieeexplore.Ieee.Org/Stamp/Stamp.Jsp?Tp=&Ar-Number=5392560. Accessed On June 11, 2023.
Senna, D., Et Al. Evaluation Of The Physiological Parameters Of Pantaneira Breed Horses Submitted To Tourist Horseback Riding. Veterinária E Zootecnia, V 1, N 24, Mar. 2017.
Silanikove, N. Effects Of Heat Stress On The Welfare Of Extensively Managed Domestic Ruminants. Livestock Production Science, N 67, P.1-18, 2000. Available At: Https://Www.Sciencedirect.Com/Science/Article/Abs/Pii/S0301622600001627. Accessed On June 8, 2023.
Speirs, V. C. Clinical Examination Of Horses. Porto Alegre: Artmed, 1999.
Srinivasa, M. A. Recommendation Systems Using Reinforcement Learning. Medium, 13 Apr. 2020. Available At: Https://Medium.Com/Inside-Machine-Learning/Recommendation-Systems-Using-Reinforcement-Learning-De6379eecfde. Accessed On October 22, 2022.
Stashak, T. S. Claudication In Horses According To Adams (4th Ed.). Editora Rocca, São Paulo, 1994.
Swenson, M. J. Physiology Of Domestic Animals (10th Ed), Guanabara Koogan, Rio De Janeiro, 1988.
Tannenbaum, J. Ethics And Animal Welfare: The Inextricable Connection. Journal Of The American Veterinary Medical Association, V 8, N 198, P. 1360-1376, 1991. Available At: Https://Pubmed.Ncbi.Nlm.Nih.Gov/2061152/. Accessed On: June 11, 2023.
Villalobos, E. M. C., Mori, E., Lara, M. C. C. S. H., & Al., E. Isolation, Sequencing And Phylogenetic Analysis Of Equine Influenza Virus Causing The 2012 Outbreak In São Paulo, Brazil. Virus Reviews And Research. In Xxiv Congresso Brasileiro De Virologia, Viii Encontro De Virologia Do Mercosul. Vol 18. Porto Seguro, Bahia, Brazil, Proceedings, 2013.
Zappa, V., & Francisco, L. S. Tetanus In Horses - Literature Review. Revista Científica Eletrônica De Medicina Veterinária, N 21, P. 1-7, 2013.
