Challenges for Mexican sheep production in the era of precision livestock farming and artificial intelligence
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Abstract
This narrative review discusses high-precision technologies applied to sheep production, with an emphasis on the use of computer vision and machine learning. It also reviews recent studies conducted in Mexico that have applied machine learning techniques to predict sheep body composition and image analysis methods to estimate body weight. These efforts have led to significant advances in the use of artificial intelligence models, such as You Only Look Once and Segment Anything, for monitoring and optimizing sheep production. In today’s interconnected world, decisions made in one context can immediately affect surrounding systems. Therefore, it is essential to consider individual animal welfare as a key factor in decision-making within production units, contributing to overall welfare. This article highlights emerging high-precision technologies in sheep farming, particularly those involving computer vision and machine learning.
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References
Vargas-Bello-Pérez E, López CA, Ruiz-Romero RA, Chay-Canul AJ, Lee-Rangel HA, Gonzalez-Ronquillo M, et al. A brief update on sheep production in Mexico: challenges and prospects. Tropical and Subtropical Agroecosystems. 2023;26(3). doi: 10.56369/tsaes.4872. DOI: https://doi.org/10.56369/tsaes.4872
Secretaría de Agricultura y Desarrollo Rural. Detrás de la ovinocultura: una mirada a la crianza de ovejas en México. 2024. https://www.gob.mx/agricultura/es/articulos/detras-de-la-ovinocultura-una-mirada-a-la-crianza-de-ovejas-en-mexico
Marino R, Petrera F, Abeni F. Scientific productions on precision livestock farming: an overview of the evolution and current state of research based on a bibliometric analysis. Animals. 2023;13(14):2280. doi: 10.3390/ani13142280. DOI: https://doi.org/10.3390/ani13142280
Morrone S, Dimauro C, Gambella F, Cappai MG. Industry 4.0 and precision livestock farming (PLF): an up to date overview across animal productions. Sensors. 2022;22(12):4319. doi: 10.3390/s22124319. DOI: https://doi.org/10.3390/s22124319
Tzounis A, Katsoulas N, Bartzanas T, Kittas C. Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering. 2017;164:31–48. doi: 10.1016/j.biosystemseng.2017.09.007. DOI: https://doi.org/10.1016/j.biosystemseng.2017.09.007
Tedeschi LO, Greenwood PL, Halachmi I. Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming. Journal of Animal Science. 2021;99(2):skab038. doi: 10.1093/jas/skab038.
Carabús A, Gispert M, Font-i-Furnols M. Imaging technologies to study the composition of live pigs: A review. Spanish Journal of Agricultural Research. 2016;14(3):e06R01. doi: 10.5424/sjar/2016143-8439. DOI: https://doi.org/10.5424/sjar/2016143-8439
Chay-Canul AJ, Tapia J, Canul-Solís J, Casanova-Lugo F, Piñeiro-Vázquez ÁT, Portillo-Salgado R, et al. Predictive biometrics of hair sheep through digital imaging. Veterinaria México OA. 2023;10. doi: 10.22201/fmvz.24486760e.2023.1150. DOI: https://doi.org/10.22201/fmvz.24486760e.2023.1150
Olsen E V., Christensen LB, Nielsen DB. A review of computed tomography and manual dissection for calibration of devices for pig carcass classification - Evaluation of uncertainty. Meat Science. 2017;123:35–44. doi: 10.1016/j.meatsci.2016.08.013. DOI: https://doi.org/10.1016/j.meatsci.2016.08.013
Kucha CT, Liu L, Ngadi MO. Non-destructive spectroscopic techniques and multivariate analysis for assessment of fat quality in pork and pork products: a review. Sensors. 2018;18(2):377. doi: 10.3390/s18020377. DOI: https://doi.org/10.3390/s18020377
Du C-J, Sun D-W. Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science & Technology. 2004;15(5):230–249. doi: 10.1016/j.tifs.2003.10.006. DOI: https://doi.org/10.1016/j.tifs.2003.10.006
Kongsro J. Estimation of pig weight using a Microsoft Kinect prototype imaging system. Computers and Electronics in Agriculture. 2014;109:32–35. doi: 10.1016/j.compag.2014.08.008. DOI: https://doi.org/10.1016/j.compag.2014.08.008
Gomes RA, Monteiro GR, Assis GJF, Busato KC, Ladeira MM, Chizzotti ML. Technical note: Estimating body weight and body composition of beef cattle trough digital image analysis. Journal of Animal Science. 2016;94(12):5414–5422. doi: 10.2527/jas.2016-0797. DOI: https://doi.org/10.2527/jas.2016-0797
Morota G, Ventura RV, Silva FF, Koyama M, Fernando SC. Big data analytics and precision animal agriculture symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science. 2018;96(4):1540–1550. doi: 10.1093/jas/sky014. DOI: https://doi.org/10.1093/jas/sky014
Vargas-Bello-Pérez E, Espinoza-Sandoval OR, Gonzalez M, Angeles JC, Chay-Canul AJ, Lee-Rangel HA, et al. The role of artificial intelligence in Latin American ruminant production systems. Animal Frontiers. 2024;14(6):23–32. doi: 10.1093/af/vfae034. DOI: https://doi.org/10.1093/af/vfae034
Camacho-Pérez E, Lugo-Quintal JM, Tirink C, Aguilar-Quiñonez JA, Gastelum-Delgado MA, Lee-Rangel HA, et al. Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods. Tropical Animal Health and Production. 2023;55(5):300. doi: 10.1007/s11250-023-03759-1. DOI: https://doi.org/10.1007/s11250-023-03759-1
Muñoz-Osorio GA, Tırınk C, Tyasi TL, Ramirez-Bautista MA, Cruz-Tamayo AA, Dzib-Cauich DA, et al. Using fat thickness and longissimus thoracis traits real-time ultrasound measurements in Black Belly ewe lambs to predict carcass tissue composition through multiresponse multivariate adaptive regression splines algorithm. Meat Science. 2024;207:109369. doi: 10.1016/j.meatsci.2023.109369. DOI: https://doi.org/10.1016/j.meatsci.2023.109369
Chay-Canul AJ, Pineda JJ, Olivares-Pérez J, Ríos-Rincón FG, García-Herrera R, Piñeiro-Vázquez ÁT, et al. Prediction of carcass characteristics of discarded Pelibuey ewes by ultrasound measurements. Revista Mexicana de Ciencias Pecuarias. 2019;10(2):473–481. doi: 10.22319/rmcp.v10i2.4551. DOI: https://doi.org/10.22319/rmcp.v10i2.4551
Morales-Martinez MA, Arce-Recinos C, Mendoza-Taco MM, Luna-Palomera C, Ramirez-Bautista MA, Piñeiro-Vazquez ÁT, et al. Developing equations for predicting internal body fat in Pelibuey sheep using ultrasound measurements. Small Ruminant Research. 2020;183:106031. doi: 10.1016/j.smallrumres.2019.106031. DOI: https://doi.org/10.1016/j.smallrumres.2019.106031
Shahinfar S, Al-Mamun HA, Park B, Kim S, Gondro C. Prediction of marbling score and carcass traits in Korean Hanwoo beef cattle using machine learning methods and synthetic minority oversampling technique. Meat Science. 2020;161:107997. doi: 10.1016/j.meatsci.2019.107997. DOI: https://doi.org/10.1016/j.meatsci.2019.107997
Aguilar-Quiñonez JA, Tırınk C, Gastelum-Delgado MA, Camacho-Pérez E, Tyasi TL, Herrera-Camacho J, et al. Prediction of carcass tissues composition using the neck and shoulder traits in hair lambs with multiresponse multivariate adaptive regression splines. Small Ruminant Research. 2023;227:107090. doi: 10.1016/j.smallrumres.2023.107090. DOI: https://doi.org/10.1016/j.smallrumres.2023.107090
Fan N, Liu G, Zhang C, Zhang J, Yu J, Sun Y. Predictability of carcass traits in live Tan sheep by real‐time ultrasound technology with least‐squares support vector machines. Animal Science Journal. 2022;93(1):e13733. doi: 10.1111/asj.13733 DOI: https://doi.org/10.1111/asj.13733
Tedeschi LO, Greenwood PL, Halachmi I. Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming. Journal of Animal Science. 2021;99(2):skab038. doi: 10.1093/jas/skab038. DOI: https://doi.org/10.1093/jas/skab038
Tedeschi LO. ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: The progression of data analytics and artificial intelligence in support of sustainable development in animal science. Journal of Animal Science. 2022;100(6):skac111. doi: 10.1093/jas/skac111. DOI: https://doi.org/10.1093/jas/skac111
Torres-Chable OM, Tırınk, C, Parra-Cortés RI, Delgado MÁG, Martínez IV, Gomez-Vazquez A, et al. Classification of FAMACHA© scores with support vector machine algorithm from body condition score and hematological parameters in Pelibuey sheep. Animals. 2025;15(5):737. doi: 10.3390/ani15050737. DOI: https://doi.org/10.3390/ani15050737
Hossein-Zadeh NG. Artificial intelligence in veterinary and animal science: applications, challenges, and future prospects. Computers and Electronics in Agriculture. 2025;235:110395. doi: 10.1016/j.compag.2025.110395. DOI: https://doi.org/10.1016/j.compag.2025.110395
Tırınk C. Comparison of bayesian regularized neural network, random forest regression, support vector regression and multivariate adaptive regression splines algorithms to predict body weight from biometrical measurements in Thalli sheep. Kafkas Üniversitesi Veteriner Fakültesi Dergisi. 2022;28(3):411-419. doi: 10.9775/kvfd.2022.27164. DOI: https://doi.org/10.9775/kvfd.2022.27164
Vázquez-Martínez I, Tırınk C, Salazar-Cuytun R, Mezo-Solis JA, Garcia RA, Orzuna-Orzuna JF, et al. Predicting body weight through biometric measurements in growing hair sheep using data mining and machine learning algorithms. Tropical Animal Health and Production. 2023;55(5):307. doi: 10.1007/s11250-023-03717-x. DOI: https://doi.org/10.1007/s11250-023-03717-x
Camacho-Pérez E, Chay-Canul AJ, Garcia-Guendulain JM, Rodríguez-Abreo O. Towards the estimation of body weight in sheep using metaheuristic algorithms from biometric parameters in microsystems. Micromachines. 2022;13(8):1325. doi: 10.3390/mi13081325. DOI: https://doi.org/10.3390/mi13081325
Chay-Canul AJ, Camacho-Pérez E, Casanova-Lugo F, Rodríguez-Abreo O, Cruz-Fernández M, Rodríguez-Reséndiz J. Neural network-based body weight prediction in Pelibuey sheep through biometric measurements. Technologies. 2024;12(5):59. doi: 10.3390/technologies12050059. DOI: https://doi.org/10.3390/technologies12050059
Badgujar CM, Poulose A, Gan H. Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review. Computers and Electronics in Agriculture. 2024;223:109090. doi: 10.1016/j.compag.2024.109090. DOI: https://doi.org/10.1016/j.compag.2024.109090
Castro DP, Diniz FA, Lima FA, Silva TS, Silva APSE, Nolêto RMA, et al. Artificial intelligence applied to animal production. Ciência Rural. 2025;55(7):e20230520. doi: 10.1590/0103-8478cr20230520. DOI: https://doi.org/10.1590/0103-8478cr20230520
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