A Glimpse into Artificial Intelligence in Animal Physiology and Allied Sciences
DOI:
https://doi.org/10.48165/aru.2022.2104Keywords:
Artificial intelligence, History, Application, Data analytical Software, SimulatorsAbstract
Artificial Intelligence (AI) has developed as an interdisciplinary science based on computers and is concerned with building machines and equipment which use human intelligence to perform a particular task. The role of AI is manifold in our day-to-day lives. With high penetration amongst people in different societies, AI has transformed the way of living and has the potential to act as a vehicle to disseminate information regarding animal health, production, and reproduction aspects. AI has already made an immense contribution in veterinary and allied sciences by helping in devising various applications used in research and simulation aids. In addition, it has been put in to use efficiently in the field of veterinary sciences thereby hastening diagnosis, treatment, and prognosis of various animal diseases. The history of AI, its applications as software packages in statistics, bioinformatics, simulation apps, and a list of various equipment used for analytical, clinical, and livestock farm purposes are elaborated in this article. Despite playing a vital role, AI has to be further refined in such a way to target the rural livestock farmers to improve animal health and production in developing countries that are in dire need of meeting food security requirements amidst the current scenario of population explosion.
References
Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ. 2019;7:e7702. doi: 10.7717/peerj.7702.
Anonymous. What is artificial intelligence and how does AI work? https://builtin.com/artificial-intelligence, 2021 accessed on 28/03/2021.
Baillie S. Utilization of simulators in veterinary training. Cattle Practice, 2007; 15(3): 224.
Bisen VS. How AI can help in agriculture-five applications and use cases. Tech news info updates and free tips. 2019.
Borchers MR, Chang YM, Proudfoot KL, Wadsworth BA, Stone AE, Bewley JM. Machine-learning-based calving predic¬tion from activity, lying, and ruminating behaviors in dairy cattle. J Dairy Sci. 2017;100(7):5664-5674. doi: 10.3168/ jds.2016-11526.
Cho Y, Julier SJ, Bianchi-Berthouze N. Instant stress: Detection of perceived mental stress through smartphone photopleth¬ysmography and thermal imaging. JMIR Ment Health. 2019;6(4):e10140. doi: 10.2196/10140.
Coward LA. Brain anatomy and artificial intelligence. In International Conference on Artificial General Intelligence, 2011: Springer, Berlin, Heidelberg, Pp. 225-268.
Crossan A. The design and evaluation of a haptic veterinary palpation training simulator. PhD Thesis, University of Glasgow, 2004; pp. 241.
Dhoble AS, Ryan KT, Lahiri P, Chen M, Pang X, Cardoso FC, Bhalerao KD. Cytometric fingerprinting and machine learning (CFML): A novel label-free, objective method for routine mastitis screening. Comput Electron Agric. 201; 162: 505-513.
Dutta R, Smith D, Rawnsley R, Bishop-Hurley G, Hills J, Timms G, Henry D. Dynamic cattle behavioral classification using supervised ensemble classifiers. Comput Electron Agric. 201; 111: 18-28.
Garbey M, Sun N, Merla A, Pavlidis I. Contact-free measurement of cardiac pulse based on the analysis of thermal imagery. IEEE Trans Biomed Eng. 2007;54(8):1418-26. doi: 10.1109/ TBME.2007.891930.
Gardner H. Multiple intelligences. 1983; Accessed from https:// www.howardgardner.com/multiple-intelligences/
Gianola D, Okut H, Weigel KA, Rosa GJ. Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat. BMC Genet. 2011;12:87. doi: 10.1186/1471-2156-12-87.
Ishaq MA. Artificial intelligence in animal health and veterinary sciences. 2020; https://vhive.buzz/artificial-intelligence-an¬imal-veterinary-sciences/
Kuroda Y, Nakao M, Kuroda T, Oyama H, Komori M. Interaction model between elastic objects for haptic feed¬back considering collisions of soft tissue. Comput Methods Programs Biomed. 2005;80(3):216-24. doi: 10.1016/j. cmpb.2005.09.001.
Lustgarten JL, Zehnder A, Shipman W, Gancher E, Webb TL. Veterinary informatics: forging the future between vet¬erinary medicine, human medicine, and One Health ini¬tiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA). JAMIA Open. 2020;3(2):306-317. doi: 10.1093/ jamiaopen/ooaa005.
Matthews SG, Miller AL, PlÖtz T, Kyriazakis I. Automated track¬ing to measure behavioural changes in pigs for health and welfare monitoring. Sci Rep. 2017;7(1):17582. doi: 10.1038/ s41598-017-17451-6.
McLennan KM, Rebelo CJB, Corke MJ, Holmes MA, Leach, MC, Constantino-Casas F. Development of a facial expres¬sion scale using footrot and mastitis as models of pain in sheep. Appl Anim Behav Sci. 2016; 176: 19-26.
Neethirajan S. The role of sensors, big data and machine learn¬ing in modern animal farming. Sensing and Bio-sensing Research, 2020; 29: 100367. Doi: 10.1016/j.sbsr.2020.100367.
Noah M. How artificial intelligence is changing the veterinary industry. 2020; https://www.vetport.com/artificial-intelli¬gence-in-veterinary-medicine.
Pavlidis I, Tsiamyrtzis P, Shastri D, Wesley A, Zhou Y, Lindner P, Buddharaju P, Joseph R, Mandapati A, Dunkin B, Bass B. Fast by nature - how stress patterns define human experience and performance in dexterous tasks. Sci Rep. 2012;2:305. doi: 10.1038/srep00305.
Pegorini V, Karam LZ, Pitta CS, Cardoso R, da Silva JC, Kalinowski HJ, Ribeiro R, Bertotti FL, Assmann TS. In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning. Sensors (Basel). 2015;15(11):28456-71. doi: 10.3390/s151128456.
Riaboff L, Poggi S, Madouasse A, Couvreur S, Aubin S, Bédère N, Goumand E, Chauvin A, Plantier G. Development of a methodological framework for a robust prediction of the main behaviors of dairy cows using a combination of machine learning algorithms on accelerometer data. Computers and Electronics in Agriculture, 2020; 169: 105179. doi: 10.1016/j.compag.2019.105179.
Sarma GP, Reinertsen E; ML4CVD Group. Physiology as a lngua franca for clinical machine learning. Patterns (NY). 2020;1(2):100017. doi: 10.1016/j.patter.2020.100017.
Shahinfar S, Page D, Guenther J, Cabrera V, Fricke P, Weigel K. Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms. J Dairy Sci. 2014;97(2):731-42. doi: 10.3168/jds.2013-6693.
Shuaib A, Arian H, Shuaib A. The increasing role of artificial intelligence in health care: Will robots replace doctors in the future? Int J Gen Med. 2020;13:891-896. doi: 10.2147/ IJGM.S268093.
Suresh KP, Hemadri D, Kruli R, Dheeraj R, Roy P. Application of artificial intelligence for livestock disease prediction. Indian Farming, 2019; 69(3): 60-62.
Williams RL, Srivastava M, Howell JN, Conatser RR, Eland DC, Burns JM, Chila AG. The virtual haptic back for palpatory training. In Sixth International Conference on Multimodal Interfaces, State College, PA, USA, 2004; Pp.191-197.
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