AI-assisted disease diagnosis and health monitoring in livestock: Current applications, challenges, and future prospects
AI-assisted livestock disease diagnosis
DOI:
https://doi.org/10.62310/liab.v6i2.436Keywords:
Artificial intelligence, Livestock health, Disease diagnosis, Machine learning, Deep learning, Precision livestock farmingAbstract
Diseases of livestock represent a significant constraint on livestock productivity, animal welfare and food security in many countries around the world, particularly in under-developed countries because of limited veterinary infrastructure and delayed disease diagnosis. Generally, conventional approaches of diagnostic tools have limited sensitivity, specificity, and scalability; and hence may fail to predict the disease risk at the herd level, leaving a significant void in the existing livestock health system. This article investigates the role of artificial intelligence (AI) in strengthening of the disease diagnosis in livestock, current capabilities, cross-species applications, and the most recent developments. A narrative literature review technique was utilized taking into consideration scientific studies, case studies, and evidence-based applications in species such as cattle, poultry, sheep, and swine. The key findings highlight the effective application of such AI techniques viz. deep learning, thermal imaging, gait analysis, audio monitoring, wearable biosensors, and GRU-based autoencoders in detection of mastitis, lameness, respiratory conditions, digestive problems, and reproductive events. The cross-regional comparison between Europe and South Asia revealed that the differences in AI adoption stem from imbalances in infrastructural, economic, and digital readiness. The study further outlines ethical concerns regarding data privacy, algorithmic bias, and animal monitoring. The integration of AI in livestock systems has the potential to revolutionize veterinary diagnostics and early detections of events, which would benefit the welfare of animals and ensure sustainable, precision livestock health management of animals across diverse farming contexts.
References
Abera D, Mossie T. (2023). A review on pneumonic pasteurellosis in small ruminants. Journal of Applied Animal Research 51(1): 1-10. https://doi.org/10.1080/09712119.2022.2146123
Aftab M, Shafiq A, Yousaf DMZ. (2024). Identification of non-invasive biomarkers for early detection of hepatocellular carcinoma in Pakistani patients: a correlation study. Trends in Animal and Plant Sciences 3: 91-94. https://doi.org/10.62324/TAPS/2024.039
Ahmed B, Nawaz MA. (2024). Evaluation of anti-bacterial efficacy of nanoparticles against major mastitis associated pathogens. Continental Veterinary Journal 4(2): 222-230. http://dx.doi.org/10.71081/cvj/2024.030
Ahmad S, Wassay M, Hussain MA. (2025). Cloud computing: empowering the next generation of agricultural research. Trends in Animal and Plant Sciences 5: 12-19. https://doi.org/10.62324/TAPS/2025.060
Ahmadzadeh M, Zahrai SM, Bitaraf M. (2025). An integrated deep neural network model combining 1D CNN and LSTM for structural health monitoring utilizing multisensor time-series data. Structural Health Monitoring 24(1): 447-465. https://doi.org/10.1177/14759217241239041
Alfifi A. (2025). Conventional and modern methods for the detection of foodborne pathogens of livestock origin. International Journal of Veterinary Science 14(6): 1345-1358. https://doi.org/10.47278/journal.ijvs/2025.134
Ali A, Murshidi GA, Aldawdahi NM, Almeraisi MM, Alshamsi SK. (2025a). Adoption of digital technologies in agriculture: analyzing awareness, utilization and adoption challenges among farmers in district Faisalabad. Scientific Records 2(1): 33-38. https://doi.org/10.62324/SR/2025.017
Ali U, Muhyuddin S, Hassan A, Saleem AR, Sahar A, Hassaan MS, Ullah H, Ullah A, Ullah N, Qaiser R, Abbas Q, Khalid H, Khan MK. (2025b). Hematological alterations in chronic kidney disease of humans and animals and evaluation of its inflammatory and hematological markers. Continental Veterinary Journal 5(1): 118-126. http://dx.doi.org/10.71081/cvj/2025.044
Alini M, Diwan AD, Erwin WM, Little CB, Melrose J. (2023). An update on animal models of intervertebral disc degeneration and low back pain: Exploring the potential of artificial intelligence to improve research analysis and development of prospective therapeutics. JOR Spine 6(1): e1230. https://doi.org/10.1002/jsp2.1230
Amer MM, Amer AM. (2025). Artificial intelligence: Current and future role in veterinary and public medicine. Egyptian Journal of Veterinary Sciences 56(11): 2761-2772. https://doi.org/10.21608/ejvs.2024.293158.2126
Ankinakatte S, Norberg E, Løvendahl P, Edwards D, Højsgaard S. (2013). Predicting mastitis in dairy cows using neural networks and generalized additive models: A comparison. Computers and Electronics in Agriculture 99: 1-6. https://doi.org/10.1016/j.compag.2013.08.024
Antognoli V, Presutti L, Bovo M, Torreggiani D, Tassinari P. (2025). Computer vision in dairy farm management: a literature review of current applications and future perspectives. Animals 15(17): 2508. https://doi.org/10.3390/ani15172508
Anuta A, Wang X, Kinay P. (2025). The impacts of climate change on livestock: An interdisciplinary, scoping review of health, production, and adaptation strategies. Climate Smart Agriculture 2(4): 100082. https://doi.org/10.1016/j.csag.2025.100082
Arshad MF, Burrai GP, Varcasia A, Sini MF, Ahmed F, Lai G, Polinas M, Antuofermo E, Tamponi C, Cocco R, Corda A, Parpagila MLP. (2024). The groundbreaking impact of digitalization and artificial intelligence in sheep farming. Research in Veterinary Science 170: 105197. https://doi.org/10.1016/j.rvsc.2024.105197
Asmare B. (2022). A review of sensor technologies applicable for domestic livestock production and health management. Advances in Agriculture 2022(1): 1599190. https://doi.org/10.1155/2022/1599190
Assaad RH, Mohammadi M, Poudel O. (2025). Developing an intelligent IoT-enabled wearable multimodal biosensing device and cloud-based digital dashboard for real-time and comprehensive health, physiological, emotional, and cognitive monitoring using multi-sensor fusion technologies. Sensors and Actuators A: Physical 381: 116074. https://doi.org/10.1016/j.sna.2024.116074
Baltenweck I, Achandi EL, Bullock RM, Campbell ZA, Crane TA, Eldermire ERB, Gichuki L, de Haan N, Katz E, Njiru N, Mungai EN, Poole EJ, Galie A. (2024). Livestock as a pathway to women's empowerment in low and middle-income countries: A scoping review. The Journal of Development Studies 60(6): 813-830. https://doi.org/10.1080/00220388.2024.2319072
Bao J, Xie Q. (2022). Artificial intelligence in animal farming: A systematic literature review. Journal of Cleaner Production 331: 129956. https://doi.org/10.1016/j.jclepro.2021.129956
Borchers MR, Chang YM, Proudfoot KL, Wadsworth BA, Stone AE, Bewley JM. (2017). Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle. Journal of Dairy Science 100(7): 5664-5674. https://doi.org/10.3168/jds.2016-11526
Bouchemla F, Akchurin SV, Akchurina IV, Dyulger GP, Latynina ES, Grecheneva AV. (2023). Artificial intelligence feasibility in veterinary medicine: A systematic review. Veterinary World 16(10): 2143. https://doi.org/10.14202/vetworld.2023.2143-2149
Burnett TA, Kaur M, Polsky L, Cerri RLA. (2020). Rumen-reticular temperature during estrus and ovulation using automated activity monitors in dairy cows. Frontiers in Veterinary Science 7: 597512. https://doi.org/10.3389/fvets.2020.597512
Carroll JM. (2024). The Benefits of Rumen Boluses in Dairy Cows. Thesis. University of Veterinary Medicine Budapest Department of Obstetrics and Food Animal Medicine Clinic, Hungary. http://hdl.handle.net/10832/4212
Chen B, Chen S. (2026). Deep learning. In: Machine Vision Technology. Springer Nature. Pp. 273-297. https://doi.org/10.1007/978-981-95-1184-6_16
Chhetri KB. (2024). Applications of artificial intelligence and machine learning in food quality control and safety assessment. Food Engineering Reviews 16(1): 1-21. https://doi.org/10.1007/s12393-023-09363-1
Chung Y, Oh S, Lee J, Park D, Chang HH, Kim S. (2013). Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13(10): 12929-12942. https://doi.org/10.3390/s131012929
Congdon JV, Hosseini M, Gading EF, Masousi M, Franke M, MacDonald SE. (2022). The future of artificial intelligence in monitoring animal identification, health, and behaviour. Animals 12(13): 1711. https://doi.org/10.3390/ani12131711
Cowton J, Kyriazakis I, Plötz T, Bacardit J. (2018). A combined deep learning gru-autoencoder for the early detection of respiratory disease in pigs using multiple environmental sensors. Sensors 18(8): 2521. https://doi.org/10.3390/s18082521
Denholm SJ, Brand W, Mitchell AP, Wells AT, Krzyzelewski T, Smith SL, Wall E, Coffey MP. (2020). Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning. Journal of Dairy Science 103(10): 9355-9367. https://doi.org/10.3168/jds.2020-18328
Ding L, Zhang C, Yue Y, Yao C, Li Z, Hu Y, Yang B, Ma W, Yu L, Gao R. (2025). Wearable sensors-based intelligent sensing and application of animal behaviors: A comprehensive review. Sensors 25(14): 4515. https://doi.org/10.3390/s25144515
Dutton-Regester KJ, Barnes TS, Wright JD, Rabiee AR. (2020). Lameness in dairy cows: Farmer perceptions and automated detection technology. Journal of Dairy Research 87(S1): 67-71. https://doi.org/10.1017/S0022029920000497
Dwivedy B, Moharana M, Nayak M. (2026). Internet of Things over the last decade: a review of frameworks, architecture, services, and platforms. Smart Science 14(2): 314-349. https://doi.org/10.1080/23080477.2026.2640359
Džermeikaitė K, Krištolaitytė J, Antanaitis R. (2025). Application of machine learning models for the early detection of metritis in dairy cows based on physiological, behavioural and milk quality indicators. Animals 15(11): 1674. https://doi.org/10.3390/ani15111674
Ebrahimi M, Mohammadi-Dehcheshmeh M, Ebrahimie E, Petrovski KR. (2019). Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models. Computers in Biology and Medicine 114: 103456. https://doi.org/10.1016/j.compbiomed.2019.103456
Endo Y, Alaimo L, Catalano G, Chatzipanagiotou OP, Pawlik TM. (2024). Application of artificial intelligence to hepatobiliary cancer clinical outcomes research. Artificial Intelligence Surgery 4(2): 59-67. https://doi.org/10.20517/ais.2024.09
Fadul-Pacheco L, Delgado H, Cabrera VE. (2021). Exploring machine learning algorithms for early prediction of clinical mastitis. International Dairy Journal 119: 105051. https://doi.org/10.1016/j.idairyj.2021.105051
Özentürk U, Chen Z, Jamone L, Versace E. (2024). Robotics for poultry farming: Challenges and opportunities. Computers and Electronics in Agriculture 226: 109411. https://doi.org/10.1016/j.compag.2024.109411
Girdauskaitė A, Grigė S, Džermeikaitė K, Krištolaitytė J, Malašauskienė D, Televičius M, Šertvytytė G, Lembovičiūtė G, Antanaitis R. (2025). Supervised machine learning approaches for early detection of metabolic and udder health disorders in dairy cows using sensor-derived data. Frontiers in Veterinary Science 12: 1726719. https://doi.org/10.3389/fvets.2025.1726719
Gomes C, Coheur L, Tilley P. (2025). A review of multimodal AI in veterinary diagnosis: Current trends, challenges, and future directions. IEEE Access 13: 97846-97858. https://doi.org/10.1109/access.2025.3575343
González-García E, Alhamada M, Pradel J, Douls S, Parisot S, Bocquier F, Menassol JB, Llach I, González LA. (2018). A mobile and automated walk-over-weighing system for a close and remote monitoring of liveweight in sheep. Computers and Electronics in Agriculture 153: 226-238. https://doi.org/10.1016/j.compag.2018.08.022
Grace D, Cook E. (2023). Zoonoses and poverty: The multiple burdens of zoonoses in low- and middle-income countries. In: Sling A, editor, Zoonoses: Infections Affecting Humans and Animals. Springer Cham, International Publishing. Pp. 1685–1697. https://doi.org/10.1007/978-3-031-27164-9_46
Grzesiak W, Błaszczyk P, Lacroix R. (2006). Methods of predicting milk yield in dairy cows—Predictive capabilities of Wood's lactation curve and artificial neural networks (ANNs). Computers and Electronics in Agriculture 54(2): 69-83. https://doi.org/10.1016/j.compag.2006.08.004
Gulyukin AM, Shulyak AF, Velichko GN, Ovchinnikov RS, Gaynullina AG, Gorokhov DJ, Gorbatov AV, Ishkova TA, Belimenko VV. (2025). Pathogenic Viruses, Fungi and Bacteria Detected in Wild Animals of Different Habitats. International Journal of Veterinary Science 14(4): 648-653. https://doi.org/10.47278/journal.ijvs/2025.016
Guo Y, Aggrey SE, Wang P, Oladeinde A, Chai L. (2022). Monitoring behaviors of broiler chickens at different ages with deep learning. Animals 12(23): 3390. https://doi.org/10.3390/ani12233390
Hafsa. (2025). Integrating Remote Sensing, IoT, and Artificial Intelligence for Soil Microbiome Monitoring. Plant Crop Letters 3: 8-18. https://doi.org/10.65822/j.pcl/2025.13
Hajnal É, Kovács L, Vakulya G. (2022). Dairy cattle rumen bolus developments with special regard to the applicable artificial intelligence (AI) methods. Sensors 22(18): 6812. https://doi.org/10.3390/s22186812
Hamadani H, Hamadani A, Shabir S. (2024). Artificial intelligence in animal farms for management and breeding. In: Hamadani A, Ganai NA, Hamadani H, Bashir J, editors, A Biologist's Guide to Artificial Intelligence. Elsevier. Pp. 167-182. https://doi.org/10.1016/B978-0-443-24001-0.00011-7
Hempstalk K, McParland S, Berry DP. (2015). Machine learning algorithms for the prediction of conception success to a given insemination in lactating dairy cows. Journal of Dairy Science 98(8): 5262-5273. https://doi.org/10.3168/jds.2014-8984
Hernandez E, Llonch P, Turner PV. (2022). Applied animal ethics in industrial food animal production: exploring the role of the veterinarian. Animals 12(6): 678. https://doi.org/10.3390/ani12060678
Hong M, Ahn H, Atif O, Lee J, Park D, Chung Y. (2020). Field-applicable pig anomaly detection system using vocalization for embedded board implementations. Applied Sciences 10(19): 6991. https://doi.org/10.3390/app10196991
Imtaiz A. (2025). Breeding at the Speed of Climate Change: Accelerated Generation Technologies for Climate-Resilient Agriculture. Plant Crop Letters 1: 21-35. https://doi.org/10.65822/j.pcl/2025.04
Irfan M. (2024). Role of artificial intelligence in the production of cotton for sustainable agriculture. Scientific Records 1(1): 37-55. https://doi.org/10.62324/SR/2024.005
Islam MN, Edwards JL, Burns R, Qi H, Gan H. (2026). Computer Vision for Cattle Health and Welfare Monitoring: A Comprehensive Review of Methods, Applications, and Interdisciplinary Integration in Smart Agriculture. Sensors 26(13): 4271. https://doi.org/10.3390/s26134271
Issa AA, Majed S, Ameer A, Al-Jawahry HM. (2024). IoT and AI in livestock management: A game changer for farmers. E3S Web of Conferences 491: 02015. https://doi.org/10.1051/e3sconf/202449102015
Jawad A, GulM, Jauhar A, Wasiq M. (2024). Clinical and Pathological studies of blackleg disease in cattle, Cholistan Desert, Punjab, Pakistan. Continental Veterinary Journal 4(2): 164-168. http://dx.doi.org/10.71081/cvj/2024.021
Jeelani R, Jeelani R. (2019). Thermal imagery for monitoring livestocks. International Journal of Life Science and Applied Science 1(1): 58-69. https://www.ejmanager.com/fulltextpdf.php?mno=302644889
Jensen DB, Hogeveen H, De Vries A. (2016). Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis. Journal of Dairy Science 99(9): 7344-7361. https://doi.org/10.3168/jds.2015-10060
Jiang B, Tang W, Cui L, Deng X. (2023). Precision livestock farming research: A global scientometric review. Animals 13(13): 2096. https://doi.org/10.3390/ani13132096
Kajdanek A, Kluska M, Matusiak R, Kazimierczak J, Dastych J. (2024). A rapid and inexpensive PCR test for mastitis diagnosis based on NGS data. Pathogens 13(5): 423. https://doi.org/10.3390/pathogens13050423
Kamphuis C, Mollenhorst H, Heesterbeek JAP, Hogeveen H. (2010). Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction. Journal of Dairy Science 93(8): 3616-3627. https://doi.org/10.3168/jds.2010-3228
Kapetanidis P, Kalioras F, Tsakonas C, Tzamalis P, Kontogiannis G, Karamanidou T, Stavropoulos TG, Nikoletseas S. (2024). Respiratory diseases diagnosis using audio analysis and artificial intelligence: a systematic review. Sensors 24(4): 1173. https://doi.org/10.3390/s24041173
Kappes A, Tozooneyi T, Shakil G, Railey AF, McIntyre KM, Mayberry DE, Rushton J, Pendell DL, Marsh TL. (2023). Livestock health and disease economics: a scoping review of selected literature. Frontiers in Veterinary Science 10: 1168649. https://doi.org/10.3389/fvets.2023.1168649
Karaarslan S, Nazlıgül A. (2024). The use of infrared thermography in the identification of surface temperatures in fast-and slow-growing broiler chickens. Mediterranean Veterinary Journal 9(3): 395-402. https://doi.org/10.24880/meditvetj.1544695
Khatun M, Thomson PC, Kerrisk KL, Lyons NA, Clark CEF, Molfino J, García SC. (2018). Development of a new clinical mastitis detection method for automatic milking systems. Journal of Dairy Science 101(10): 9385-9395. https://doi.org/10.3168/jds.2017-14310
Kopler I, Marchaim U, Tikász IE, Opaliński S, Kokin E, Mallinger K, Neubauer T, Gunnarsson S, Soerensen C, Phillips CJC. (2023). Farmers' perspectives of the benefits and risks in precision livestock farming in the EU pig and poultry sectors. Animals 13(18): 2868. https://doi.org/10.3390/ani13182868
Korelidou V, Simitzis P, Massouras T, Gelasakis AI. (2024). Infrared thermography as a diagnostic tool for the assessment of mastitis in dairy ruminants. Animals 14(18): 2691. https://doi.org/10.3390/ani14182691
Lagua EB, Mun HS, Ampode KMB, Kim YH, Yang CJ. (2023). Artificial intelligence for automatic monitoring of respiratory health conditions in smart swine farming. Animals 13(11): 1860. https://doi.org/10.3390/ani13111860
Lane JK, Kelly T, Bird B, Chenais E, Roug A, Vidal G, Gallardo R, Zhou H, VanHoy G, Smith W. (2025). A one health approach to reducing livestock disease prevalence in developing countries: advances, challenges, and prospects. Annual Review of Animal Biosciences 13(1): 277-302. https://doi.org/10.1146/annurev-animal-111523-102133
Latif M, MS Arshad, M Usman, M Mobashar, Z Wali, H Ashraf, T Ahmad, MAS Mughal. (2024). Advancement in diagnostic imaging techniques in veterinary medicine. Continental Veterinary Journal 4(1): 120-127. http://dx.doi.org/10.71081/cvj/2024.015
Lassen J, Thomasen JR, Hansen RH, Nielsen GGB, Olsen EV, Stentebjerg PRB, Hansen NW, Borchersen S. (2018). Individual measure of feed intake on in-house commercial dairy cattle using 3D camera system. Proceedings of the World Congress on Genetics Applied to Livestock Production, Auckland, New Zealand, February 11, 2018. http://www.wcgalp.org/proceedings/2018/individual-measure-feed-intake-house-commercial-dairy-cattle-using-3d-camera
Leach KA, Dippel S, Huber J, March S, Winckler C, Whay HR. (2009). Assessing lameness in cows kept in tie-stalls. Journal of Dairy Science 92(4): 1567-1574. https://doi.org/10.3168/jds.2008-1648
Lem M. (2019). Barriers to accessible veterinary care. The Canadian Veterinary Journal 60(8): 891-893. https://pmc.ncbi.nlm.nih.gov/articles/PMC6625179/
Liseune A, Salamone M, Van den Poel D, Van Ranst B, Hostens M. (2021). Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning. Computers and Electronics in Agriculture 180: 105904. https://doi.org/10.1016/j.compag.2020.105904
Liu Y, Ma X, Shu L, Hancke GP, Abu-Mahfouz AM. (2020). From industry 4.0 to agriculture 4.0: Current status, enabling technologies, and research challenges. IEEE Transactions on Industrial Informatics 17(6): 4322-4334. https://doi.org/10.1109/TII.2020.3003910
Low KTA, Peh WCG. (2017). Radiography limitations and pitfalls. In: Peh WCG, editor, Pitfalls in Musculoskeletal Radiology. Springer. Pp. 3-32. https://doi.org/10.1007/978-3-319-53496-1_1
Luo W, Dong Q, Feng Y. (2023). Risk prediction model of clinical mastitis in lactating dairy cows based on machine learning algorithms. Preventive Veterinary Medicine 221: 106059. https://doi.org/10.1016/j.prevetmed.2023.106059
Mahato S, Neethirajan S. (2024). Integrating artificial intelligence in dairy farm management− biometric facial recognition for cows. Information Processing in Agriculture 12(3): 312-325. https://doi.org/10.1016/j.inpa.2024.10.001
Marcano VC, Ireifej S, Nayee S. (2026). The Connection Between Access to Veterinary Care and Access to the Veterinary Profession. Veterinary Ophthalmology 29(2): e70095. https://doi.org/10.1111/vop.70095
Mbelwa H, Mbelwa J, Machuve D. (2021). Deep convolutional neural network for chicken diseases detection. International Journal of Advanced Computer Science and Applications 12(2): 95. https://doi.org/10.14569/IJACSA.2021.0120295
McManus R, Boden LA, Weir W, Viora L, Barker R, Kim Y, McBride P, Yang S. (2022). Thermography for disease detection in livestock: A scoping review. Frontiers in Veterinary Science 9: 965622. https://doi.org/10.3389/fvets.2022.965622
Melak A, Aseged T, Shitaw T. (2024). The influence of artificial intelligence technology on the management of livestock farms. International Journal of Distributed Sensor Networks 2024(1): 8929748. https://doi.org/10.1155/2024/8929748
Mia N, Sarker T, Halim MA, Alam AMMN, Ali MS, Rahman MM, Hashem MA. (2025). Machine learning overview and its application in the livestock industry. Meat Research 5(1): 1-10. https://doi.org/10.55002/mr.5.1.109
Mishra H, Mishra D. (2023). Artificial intelligence and machine learning in agriculture: Transforming farming systems. Research Trends in Agriculture Science 1: 1-16. https://doi.org/10.5281/zenodo.15157536
Mitsunaga TM, Nery Garcia BL, Pereira LBR, Costa YCB, da Silva RF, Delbem ACB, Dos Santos MV. (2024). Current trends in artificial intelligence and bovine mastitis research: A bibliometric review approach. Animals 14(14): 2023. https://doi.org/10.3390/ani14142023
Modak M, Pritom MM, Banik SC, Rabbi MS. (2025). Internet of Things‐Based Health Surveillance Systems for Livestock: A Review of Recent Advances and Challenges. IET Wireless Sensor Systems 15(1): e70013. https://doi.org/10.1049/wss2.70013
Myint BB, Onizuka T, Tin P, Aikawa M, Kobayashi I, Zin TT. (2024). Development of a real-time cattle lameness detection system using a single side-view camera. Scientific Reports 14(1): 13734. https://doi.org/10.1038/s41598-024-64664-7
Naqvi SZH, Maqbool B, Arshad MI, Aderibigbe A and Gul ST. (2025). Food security and livestock: a comprehensive review of sustainability, challenges and innovative solutions. Agrobiological Records 20: 50-58. https://doi.org/10.47278/journal.abr/2025.019
Neethirajan S, Kemp B. (2021). Digital twins in livestock farming. Animals 11(4): 1008. https://doi.org/10.3390/ani11041008
Neethirajan S. (2024). Artificial intelligence and sensor innovations: enhancing livestock welfare with a human-centric approach. Human-Centric Intelligent Systems 4(1): 77-92. https://doi.org/10.1007/s44230-023-00050-2
Ngo HQT, Nguyen TP, Nguyen H. (2020). Research on a low-cost, open-source, and remote monitoring data collector to predict livestock's habits based on location and auditory information: a case study from Vietnam. Agriculture 10(5): 180. https://doi.org/10.3390/agriculture10050180
Ochwo S, Perez AM, Pérez Aguirreburualde MS. (2023). Beyond accuracy: leveraging ASSURED criteria for field evaluation of point-of-care tests for food animal diseases. Frontiers in Veterinary Science 10: 1239111. https://doi.org/10.3389/fvets.2023.1239111
Papakonstantinou GI, Voulgarakis N, Terzidou G, Fotos L, Giamouri E, Papatsiros VG. (2024). Precision livestock farming technology: applications and challenges of animal welfare and climate change. Agriculture 14(4): 620. https://doi.org/10.3390/agriculture14040620
Pasteur K, Diana A, Yatcilla JK, Barnard S, Croney CC. (2024). Access to veterinary care: evaluating working definitions, barriers, and implications for animal welfare. Frontiers in Veterinary Science 11: 1335410. https://doi.org/10.3389/fvets.2024.1335410
Pinedo P, Manríquez D, Azocar J, Klug BR, De Vries A. (2022). Dynamics of automatically generated body condition scores during early lactation and pregnancy at first artificial insemination of Holstein cows. Journal of Dairy Science 105(5): 4547-4564. https://doi.org/10.3168/jds.2021-21501
Prakash S. (2024). Impact of implementing ai in medical field. Interantional Journal of Scientific Research in Engineering and Management 8(03): 1-5. https://doi.org/10.55041/IJSREM29760
Pratelli A, Cirone F, Mountricha M, Padalino B. (2024). Respiratory diseases and management in livestock. Frontiers in Veterinary Science 11: 1367128. https://doi.org/10.3389/fvets.2024.1367128
Qamar SW, Shahzad I, Irshad MA, Nasim I, Nawaz R. (2025). A review on climate change and occupational health: adapting workplaces to extreme weather events. Medicinal and Plant Letters 1: 14-25. https://doi.org/10.65822/j.mpl/2025.002
Qayyum A, Shafique S, Ghori MT, Rao QT, Aslam Z, Mohammed AH, Iqbal R, Ahmadova K, Yasin MN, Ahmad T, Javed M, Shahzadi E. (2025). Evaluation of clinico-hematological and biochemical changes in camels with trypanosomiasis in the Cholistan region, Bahawalpur. International Journal of Veterinary Science 14(6): 1162-1169. https://doi.org/10.47278/journal.ijvs/2025.059a
Rafique MN, Akram W, Aslam MA, Ather AS, Rehman M, Zahid A, Ahmed Z, Ullah N, Saeed M, Mehnaz S. (2024). Uncovering strategies for the detection of Babesia species. Trends in Animal and Plant Sciences 4: 1-7. https://doi.org/10.62324/TAPS/2024.041
Vaishnavi T, Rahul D. (2024). Embedded sensor belt for cattle stomach measurement monitoring with automated alert functionality. i-manager's Journal on Embedded Systems 13(1): 23-27. https://doi.org/10.26634/jes.13.1.21090
Ramos Niño JN, Sousa FCD, Oliveira CEA, Coelho ALDF, Hernandez RO, Barbari M. (2025). Systematic review of acoustic monitoring in livestock farming: Vocalization patterns and sound source analysis. Applied Sciences 15(18): 9910. https://doi.org/10.3390/app15189910
Rasul G, Nepal AK, Hussain A, Maharjan A, Joshi S, Lama A, Gurung P, Ahmad F, Mishra A, Sharma E. (2021). Socio-economic implications of COVID-19 pandemic in South Asia: emerging risks and growing challenges. Frontiers in Sociology 6: 629693. https://doi.org/10.3389/fsoc.2021.629693
Raza A, Abdullah FNU, Wahid A. (2025). Assessment of digital technologies in agriculture with particular reference to Digital Dera Initiative in Punjab, Pakistan. Agrobiological Records 20: 18-26. https://doi.org/10.47278/journal.abr/2025.016
Raza A, Shahid MA, Safdar M, Zaman M, Abdur M, Tariq R, Hassan MU. (2023). Artificial Intelligence-Enabled Precision Agriculture: A Review of Applications and Challenges. 2nd International Online Conference on Agriculture 1 November – 15 November. https://doi.org/10.3390/IOCAG2023-16878
Razzaq K, Shah M. (2025). Machine learning and deep learning paradigms: From techniques to practical applications and research frontiers. Computers 14(3): 93. https://doi.org/10.3390/computers14030093
Reza MN, Ali MR, Haque MA, Jin H, Kyoung H, Choi YK, Kim G, Chung SO. (2025). A review of sound-based pig monitoring for enhanced precision production. Journal of Animal Science and Technology 67(2): 277-302. https://doi.org/10.5187/jast.2024.e113
Rizqan, Susanty H, Roza E, Arief, Yellita Y, Suhartati L. (2025). Antimicrobial content and sensitivity test of citrus peel eco-enzyme as natural teat dipping solution in dairy cattle. International Journal of Agriculture and Biosciences 14(6): 1323-1328. https://doi.org/10.47278/journal.ijab/2025.117
Saar M, Edan Y, Godo A, Lepar J, Parmet Y, Halachmi I. (2022). A machine vision system to predict individual cow feed intake of different feeds in a cowshed. Animal 16(1): 100432. https://doi.org/10.1016/j.animal.2021.100432
Saida A, Ramchander A, Reddy BSB, Chandra MAR, Sagar AR, Rajani D. (2025). AI-enhanced livestock health and disease prediction in dairy and poultry farms. In: Buddi T, Kandakatla, Kotkunde NR, Ramamurthy U, Perveen A, editor, Multi-Disciplinary Research and Sustainable Development. CRC Press. Pp. 191-198. https://doi.org/10.1201/9781003675242-28
Sarwar MZ, Nomi ZA, Awais M, Shahbakht RM, Jamil M, Mussawar M, Yasin I, Hafsa, Quratulain, Abbas Q, Yousuf H. (2025). Effect of climate change on transmission of livestock diseases. Agrobiological Records 19: 1-11. https://doi.org/10.47278/journal.abr/2025.001
Schrobback P, Dennis G, Li Y, Mayberry D, Shaw A, Knight-Jones T, Marsh TL, Pendell DL, Torgerson PR, Gilbert W, Huntington B, Raymond K, Stacey DA, Bernardo T, Bruce M, McIntyre KM, Rushton J, Herrero M. (2023). Approximating the global economic (market) value of farmed animals. Global Food Security 39: 100722. https://doi.org/10.1016/j.gfs.2023.100722
Senthilkumar S, Manivannan C, Sureshkannan S. (2021). Development of mobile based technology transfer application system to empower the small ruminant farmers. The Indian Journal of Animal Sciences 91(4): 321-324. https://doi.org/10.56093/ijans.v91i4.114342
Shafiq A, Aftab M, Ali MM. (2024). Artificial intelligence in invasive species management: transforming detection and response. Trends in Animal and Plant Sciences 4: 82-96. https://doi.org/10.62324/TAPS/2024.050
Shah P, Kumar Y, Bhowmik T. (2026). Deep learning techniques for the diagnosis and detection of orthopedic conditions: a systematic review of recent advances and challenges. Archives of Computational Methods in Engineering 33(1): 1061-1087. https://doi.org/10.1007/s11831-025-10330-8
Shahinfar S, Page D, Guenther J, Cabrera V, Fricke P, Weigel K. (2014). Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms. Journal of Dairy Science 97(2): 731-742. https://doi.org/10.3168/jds.2013-6693
Suarez VH, Martinez GM, Wirsh S. (2025). Impact of lameness on production, reproduction and health of dairy cows. RIA. Revista de Investigaciones Agropecuarias 51(2): 97-105. https://doi.org/10.58149/ndw3-6a22
Susanty H, Arief, Hellyward J, Ratni E, Alzahra W, Sopiah S, Ananda. (2025). Prevalence and microbial profiles of subclinical mastitis in dairy goats and their impact on milk quality in West Sumatra, Indonesia. International Journal of Veterinary Science 14(6): 1251-1256. https://doi.org/10.47278/journal.ijvs/2025.087
Syamsu JA, Natsir A, Yamin AA, Zain M, Tanuwiria UH, Yunilas, Amal I. (2024). In Vitro rumen fermentability and physical properties of Bali cattle ration based on agro-industrial byproduct with different ratio of rumen degradable protein and non-fiber carbohydrate. International Journal of Agriculture and Biosciences 13(3): 488-493. https://doi.org/10.47278/journal.ijab/2024.158
Taleb HM, Mahrose K, Abdel-Halim AA, Kasem H, Ramadan GS, Fouad AM, Khafaga AF, Khalifa NE, Kamal M, Salem HM. (2025). Using artificial intelligence to improve poultry productivity–a review. Annals of Animal Science 25(1): 23-33. https://doi.org/10.2478/aoas-2024-0039
Tariq S. (2025). Artificial Intelligence in Phytomicrobiome Analysis: A New Frontier for Crop Improvement. Plant Crop Letters 1: 46-57. https://doi.org/10.65822/j.pcl/2025.06
Vâtcă A. (2021). Artificial intelligence, a possible solution for agriculture and animal husbandry sector? Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Animal and Biotechnologies 78(2): 1-5. https://doi.org/10.15835/buasvmcn-asb:2021.0004
Vlaicu PA, Gras MA, Untea AE, Lefter NA, Rotar MC. (2024). Advancing livestock technology: intelligent systemization for enhanced productivity, welfare, and sustainability. AgriEngineering 6(2): 1479-1496. https://doi.org/10.3390/agriengineering6020084
Wang C, Liu M, Wang Z, Li S, Deng Y, He N. (2021). Point-of-care diagnostics for infectious diseases: From methods to devices. Nano Today 37: 101092. https://doi.org/10.1016/j.nantod.2021.101092
Wang Y, Kang X, He Z, Feng Y, Liu G. (2022a). Accurate detection of dairy cow mastitis with deep learning technology: A new and comprehensive detection method based on infrared thermal images. animal 16(10): 100646. https://doi.org/10.1016/j.animal.2022.100646
Wang J, Zhang Y, Wang J, Zhao K, Li X, Liu B. (2022b). Using machine-learning technique for estrus onset detection in dairy cows from acceleration and location data acquired by a neck-tag. Biosystems Engineering 214: 193-206. https://doi.org/10.1016/j.biosystemseng.2021.12.025
Wang H, Wu H, He Z, Huang L, Church KW. (2022c). Progress in machine translation. Engineering 18: 143-153. https://doi.org/10.1016/j.eng.2021.03.023
Wen H, Shi H, Yu J, Fan Z, Dai H, Jiang L, Song Q. (2025). An intelligent diagnostic method for porcine gastrointestinal infectious diseases based on multimodal AI and large language model. Frontiers in Veterinary Science 12: 1660745. https://doi.org/10.3389/fvets.2025.1660745
Wu P, He X, Dai W, Zhou J, Shang Y, Fan Y, Hu T. (2025). A Review on Research and Application of Al-based Image Analysis in the field of Computer Vision. IEEE Access 13: 76684-76702. https://doi.org/10.1109/ACCESS.2025.3565300
Xiao S, Dhand NK, Wang Z, Hu K, Thomson PCC, House JK, Khatkar MSS. (2025). Review of applications of deep learning in veterinary diagnostics and animal health. Frontiers in Veterinary Science 12: 1511522. https://doi.org/10.3389/fvets.2025.1511522
Zeng D, Chen Z, Jiang Y, Xue F, Li B. (2016). Advances and challenges in viability detection of foodborne pathogens. Frontiers in Microbiology 7: 1833. https://doi.org/10.3389/fmicb.2016.01833
Zhao Y, Zhang L, Wang A, Zhou D. (2025). Biosensor technology: advances and applications in livestock infectious disease diagnosis. Veterinary Sciences 12(1): 23. https://doi.org/10.3390/vetsci12010023
Zheng Z, Zhang X, Qin L, Yue S, Zeng P. (2023). Cows' legs tracking and lameness detection in dairy cattle using video analysis and Siamese neural networks. Computers and Electronics in Agriculture 205: 107618. https://doi.org/10.1016/j.compag.2023.107618
Zhuang X, Bi M, Guo J, Wu S, Zhang T. (2018). Development of an early warning algorithm to detect sick broilers. Computers and Electronics in Agriculture 144: 102-113. https://doi.org/10.1016/j.compag.2017.11.032
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Muhammad Shahzad Shafiq , Ragil Angga Prastiya, Junaid Iqbal, Muhammad Talha Tariq, Zinayyera Subhani , Sobia Amir Chughtai, Ammar Younas
This work is licensed under a Creative Commons Attribution 4.0 International License.
Accepted 2026-10-20
Published 2026-10-30
