Evaluation of Digital Technology Usage Possibilities in Dairy Cattle with Bibliometric Analysis


DOI:
https://doi.org/10.5281/zenodo.15480839Keywords:
Bibliometric, dairy cattle, digital image processing, sensorAbstract
In recent years, technological developments have led to the shift from the traditional structure of dairy farms to digital technology. This study was conducted to reveal the current trend towards digitalization, and the keywords "dairy cattle", "sensor", "digital image processing" and "internet of things (IoT)" were searched in the Web of Science database between 1993 and January 2025. A total of 529 scientific papers obtained from this search were evaluated using bibliometric analysis. The results indicated that the keywords in studies related to dairy cattle have evolved over time from classical herd management systems to smart systems. Additionally, the keywords have been clustered into three categories: sensors, image processing, and IoT. When examining the main themes of the studies, keywords such as dairy cattle, mastitis, milk yield, feeding behavior, identification, risk factors, and herd management were observed. It was found that digital imaging methods and body condition scores served as a bridge between the main theme and niche topics in the research. Furthermore, there has been an increasing trend in studies aimed at detecting subclinical ketosis using biosensors in recent years. Technological advances are expected to bring major changes to the dairy cattle industry in the future. It is predicted that new practices will focus on improving animal health and welfare, increasing milk production, and reducing the need for labor.
References
Alkan, S., 2015. Türkiye’de süt sığırı ahırlarında karşılaşılan başlıca sorunlar. Akademik Ziraat Dergisi, 4(1): 43-48.
Altay, Y., Albayrak Delialioğlu, R., 2022. Diagnosing lameness with the Random Forest classification algorithm using thermal cameras and digital colour parameters. Mediterranean Agricultural Sciences, 35(1): 47-54.
Altay, Y., Kaplan, S., 2023. Bibliometric analyzes of some major effect genes associated with meat yield traits in livestock. Selcuk Journal of Agriculture and Food Sciences, 37(3): 608-617.
Aria, M., Cuccurullo, C. 2017. bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4): 959-975.
Bruyère, P., Hétreau, T., Ponsart, C., Gatien, J., Buff, S., Disenhaus, C., Giroud, O., Guérin, P., 2012. Can video cameras replace visual estrus detection in dairy cows? Theriogenology, 77(3): 525-530.
Chapinal, N., De Passille, A.M., Rushen, J., Wagner, S., 2010. Automated methods for detecting lameness and measuring analgesia in dairy cattle. Journal of Dairy Science, 93(5): 2007-2013.
Coşkun, G., Şahin, Ö., Delialioğlu, R.A., Altay, Y., Aytekin, İ., 2023. Diagnosis of lameness via data mining algorithm by using thermal camera and image processing method in Brown Swiss cows. Tropical Animal Health and Production, 55(1): 50.
Çelik, Ş., 2020. Bibliometrics Analysis and a Husbandry Practice. Journal of Multidisciplinary Engineering Science Studies, 6(12): 3632-3641.
Firk, R., Stamer, E., Junge, W., Krieter, J., 2002. Automation of oestrus detection in dairy cows: a review. Livestock Production Science, 75(3): 219-232.
Han, J., Kang, H.J., Kim, M., Kwon, G.H., 2020. Mapping the intellectual structure of research on surgery with mixed reality: Bibliometric network analysis 2000–2019. Journal of Biomedical Informatics, 109(2020): 103516.
Hogeveen, H., Heemskerk, K., Mathijs, E., 2004. Motivations of Dutch farmers to invest in an automatic milking system or a conventional milking parlour. In Automatic milking, a better understanding. Wageningen Academic, p. 56-61.
Kaplan, S., Altay, Y., 2023. Bibliometric analysis of next-generation sequence applications in livestock. Black Sea Journal of Agriculture, 6(5): 485-491.
Munksgaard, L., Reenen, C.G., Boyce, R., 2006. Automatic monitoring of lying, standing and walking behavior in dairy cattle. Journal of Animal Science, 84: 304.
Neethirajan, S., 2017. Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research, 12(2017): 15-29.
Neveux, S., Weary, D.M., Rushen, J., Von Keyserlingk, M.A.G., De Passillé, A.M., 2006. Hoof discomfort changes how dairy cattle distribute their body weight. Journal of Dairy Science, 89(7): 2503-2509.
Önder, H., Tırınk, C., 2022. Bibliometric analysis for genomic selection studies in animal science. Journal of the Institute of Science and Technology, 12(3): 1849-1856.
Pastell, M., Hänninen, L., de Passille, A.M., Rushen, J., 2010. Measures of weight distribution of dairy cows to detect lameness and the presence of hoof lesions. Journal of Dairy Science, 93(3): 954-960.
Poursaberi, A., Bahr, C., Pluk, A., Van Nuffel, A., Berckmans, D., 2010. Real-time automatic lameness detection based on back posture extraction in dairy cattle: Shape analysis of cow with image processing techniques. Computers and Electronics in Agriculture, 74(1): 110-119.
Rutten, C.J., Velthuis, A.G.J., Steeneveld, W., Hogeveen, H., 2013. Invited review: Sensors to support health management on dairy farms. Journal of Dairy Science, 96(4): 1928-1952.
Soriani, N., Trevisi, E., Calamari, L. 2012. Relationships between rumination time, metabolic conditions, and health status in dairy cows during the transition period. Journal of Animal Science, 90(12): 4544-4554.
Stygar, A.H., Gómez, Y., Berteselli, G.V., Dalla Costa, E., Canali, E., Niemi, J.K., Llonch, P., Pastell, M., 2021. A systematic review on commercially available and validated sensor technologies for welfare assessment of dairy cattle. Frontiers in Veterinary Science, 8: 634338.
Taneja, M., Byabazaire, J., Jalodia, N., Davy, A., Olariu, C., Malone, P., 2020. Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle. Computers and Electronics in Agriculture, 171(2020): 105286.
Thorup, V.M., Munksgaard, L., Robert, P.E., Erhard, H.W., Thomsen, P.T., Friggens, N.C., 2015. Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms. Animal, 9(10): 1704-1712.
Van Asseldonk, M.A.P.M., Jalvingh, A.W., Huirne, R.B.M., Dijkhuizen, A.A., 1999. Potential economic benefits from changes in management via information technology applications on Dutch dairy farms: a simulation study. Livestock Production Science, 60(1): 33-44.
Viazzi, S., Bahr, C., Schlageter-Tello, A., Van Hertem, T., Romanini, C.E.B., Pluk, A., Halachmi, I., Lokhorst, C., Berckmans, D., 2013. Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle. Journal of Dairy Science, 96(1): 257-266.
Wathes, C.M., Kristensen, H.H., Aerts, J.M., Berckmans, D. 2008. Is precision livestock farming an engineer’s daydream or nightmare, an animal’s friend or foe, and a farmer’s panacea or pitfall? Computers and Electronics in Agriculture, 64(1): 2-10.
Weng, X., Zhao, W., Neethirajan, S., Duffield, T. 2015. Microfluidic biosensor for β-Hydroxybutyrate βHBA determination of subclinical ketosis diagnosis. Journal of Nanobiotechnology, 13(2015): 1-8.
Zhang, M., Wang, X., Feng, H., Huang, Q., Xiao, X., Zhang, X. 2021. Wearable Internet of Things enabled precision livestock farming in smart farms: A review of technical solutions for precise perception, biocompatibility, and sustainability monitoring. Journal of Cleaner Production, 312(2021): 127712.
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