PRODUCTION AND MANAGEMENT:Invited Review| Volume 39, ISSUE 1, P14-22, February 2023

Download started.


Invited Review: Examples and opportunities for artificial intelligence (AI) in dairy farms*

      This paper is only available as a PDF. To read, Please Download here.



      Artificial intelligence (AI) refers to the ability of a digital computer or computer-controlled robot to perform reasoning tasks commonly associated with intelligent beings. Powered by novel and affordable software and hardware capabilities, AI is finding its way into dairy farms. The purpose of this review is to give examples of research on AI for dairy farms and highlight some emerging opportunities. Many AI applications are based on machine learning, including the technique of deep learning.


      We used literature sources and our own experiences with AI applications in dairy farms.


      We found that machine-learning methods enable applications such as real-time analysis of video images to identify cattle, measure body condition and temperature, and detect changes in feed topography to measure feed availability and intake. Changes in behavior can be detected as early warning alerts for disease such as lameness or as an indication of estrus. Such AI applications can mimic human reasoning and enhance human tasks. Machine-learning methods may also be able to use heterogeneous data sets to predict future performance such as fertility. For example, predictions of conception rates may be improved by combining health events, changes in body energy reserves, genetic data, behavioral data, milk analyses, and environmental information. Perhaps AI methods could be used to monitor compliance of execution of protocols in dairy farms and inform training.

      Conclusions and Applications

      Artificial intelligence, made possible with advances in hardware and software, will make intelligent use of new big data a reality and will change the dairy sector by enabling improved work environments and removing or minimizing the need for manual human processing of repetitive tasks. A hurdle for development and application of some AI is the problem that various dairy data often exist in silos that are not connected.

      Key words


        • Bobbo T.
        • Biffani S.
        • Taccioli C.
        • Penasa M.
        • Cassandro M.
        Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows.
        Sci. Rep. 2021; 11: 13642
        • Borchers M.R.
        • Chang Y.M.
        • Proudfoot K.L.
        • Wadsworth B.A.
        • Stone A.E.
        • Bewley J.M.
        Machine-learning-based calv- ing prediction from activity, lying, and ruminating behaviors in dairy cattle.
        J. Dairy Sci. 2017; 100: 5664-5674
        • Cabrera V.E.
        Invited review: Helping dairy farmers to improve economic performance utilizing data-driving decision support tools.
        Animal. 2018; 12: 134-144
        • Calsamiglia S.
        • Espinosa G.
        • Vera G.
        • Ferret A.
        • Castillejos L.
        A virtual dairy herd as a tool to teach dairy production and management.
        J. Dairy Sci. 2020; 103: 2896-2905
        • Cockburn M.
        Review: Application and prospective discussion of machine learning for the management of dairy farms.
        Animals (Basel). 2020; 10: 1690
        • De Vries A.
        • Conlin B.J.
        Comparison of neural net data models to estimate herd average milk.
        J. Dairy Sci. 1996; 79: 287
        • Denholm S.J.
        • Brand W.
        • Mitchell A.P.
        • Wells A.T.
        • Krzyzelewski T.
        • Smith S.L.
        • Wall E.
        • Coffey M.P.
        Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning.
        J. Dairy Sci. 2020; 103: 9355-9367
        • Duerr I.
        • Merrill H.R.
        • Wang C.
        • Bai R.
        • Boyer M.
        • Dukes M.D.
        • Bliznyuk N.
        Forecasting urban household water demand with statistical and machine learning methods using large space-time data: A comparative study.
        Environ. Model. Softw. 2018; 102: 29-38
        • Ebrahimi M.
        • Mohammadi-Dehcheshmeh M.
        • Ebrahimie E.
        • Petrovski K.R.
        Comprehensive analysis of machine learning mod- els for prediction of sub-clinical mastitis: Deep Learning and Gra- dient-Boosted Trees outperform other models.
        Comput. Biol. Med. 2019; 114103456
        • Eckelkamp E.A.
        Invited Review: Current state of wearable precision dairy technologies in disease detection.
        Appl. Anim. Sci. 2019; 35: 209-220
        • Fadul-Pacheco L.
        • Wangen S.R.
        • da Silva T.E.
        • Cabrera V.E.
        Addressing data bottlenecks in the dairy farm industry.
        Animals (Basel). 2022; 12: 721
        • García R.
        • Aguilar J.
        • Toro M.
        • Pinto A.
        • Rodríguez P.
        A systematic literature review on the use of machine learning in preci- sion livestock farming.
        Comput. Electron. Agric. 2020; 179105826
        • Grzesiak W.
        • Błaszczyk P.
        • Lacroix R.
        Methods of predict- ing milk yield in dairy cows—Predictive capabilities of Wood’s lacta- tion curve and artificial neural networks (ANNs).
        Comput. Electron. Agric. 2006; 54: 69-83
        • Hastie T.
        • Tibshirani R.
        • Friedman J.H.
        The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
        2. Springer, 2009: 1-758
        • Hempstalk K.
        • McParland S.
        • Berry D.P.
        Machine learn- ing algorithms for the prediction of conception success to a given insemination in lactating dairy cows.
        J. Dairy Sci. 2015; 98: 5262-5273
        • Hesse A.
        • Ospina P.
        • Wieland M.
        • Yepes F.A.L.
        • Nguyen B.
        • Heuwieser W.
        Short communication: Microlearning courses are effective at increasing the feelings of confidence and accuracy in the work of dairy personnel.
        J. Dairy Sci. 2019; 102: 9505-9511
        • Houston G.
        How Artificial Intelligence could change the dairy farming industry.
        (Accessed Aug. 4, 2022.)
        • Jensen D.B.
        • Hogeveen H.
        • De Vries A.
        Bayesian integra- tion of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis.
        J. Dairy Sci. 2016; 99: 7344-7361
        • Lassen J.
        • Thomasen J.R.
        • Hansen R.H.
        • Nielsen G.G.B.
        • Olsen E.
        • Stentebjerg P.R.B.
        • Hansen N.W.
        • Borchersen S.
        Individual measure of feed intake on in-house commercial dairy cattle using 3D camera system.
        in: Proc. 11th World Congr. Genetics Appl. Livest. Prod. Al Rae Center for Genetics and Breeding, Massey University, 2018: 635-640
        • Liseune A.
        • Salamone M.
        • Van den Poel D.
        • van Ranst B.
        • Hostens M.
        Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning.
        Comp. Electron. Ag- ric. 2021; 180105904
        • Mahmud M.S.
        • Zahid A.
        • Das A.K.
        • Muzammil M.
        • Khan M.U.
        A systematic literature review on deep learning applications for precision cattle farming.
        Comput. Electron. Agric. 2021; 187106313
        • Martin M.J.
        • Dórea J.R.R.
        • Borchers M.R.
        • Wallace R.L.
        • Bertics S.J.
        • DeNise S.K.
        • Weigel K.A.
        • White H.M.
        Compari- son of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables.
        J. Dairy Sci. 2021; 104: 8765-8782
        • McKay K.J.
        • Li C.
        • Sotomayor-Castillo C.
        • Ferguson P.E.
        • Wyer M.
        • Shaban R.Z.
        Health care workers’ experiences of video- based monitoring of hand hygiene behaviors: A qualitative study.
        Am. J. Infect. Control, In Press. 2022;
        • Mollenhorst H.
        • Rijkaart L.J.
        • Hogeveen H.
        Mastitis alert preferences of farmers milking with automatic milking systems.
        J. Dairy Sci. 2012; 95: 2523-2530
        • Mullins I.L.
        • Truman C.M.
        • Campler M.R.
        • Bewley J.M.
        • Costa J.H.C.
        Validation of a commercial automated body condition scoring system on a commercial dairy farm.
        Animals (Basel). 2019; 9: 287
        • Nielen M.
        • Spigt M.H.
        • Schukken Y.H.
        • Deluyker H.A.
        • Maatje K.
        • Brand A.
        Application of a neural network to analyse on- line milking parlour data for the detection of clinical mastitis in dairy cows.
        Prev. Vet. Med. 1995; 22: 15-28
        • Pinedo P.
        • Manríquez D.
        • Azocar J.
        • Klug B.R.
        • De Vries A.
        Dynamics of automatically generated body condition scores during early lactation and pregnancy at first artificial insemination of Holstein cows.
        J. Dairy Sci. 2022; 105: 4547-4564
        • Pugliese R.
        • Regondi S.
        • Marini R.
        Machine learning-based approach: Global trends, research directions, and regulatory stand- points.
        Data Sci. Manage. 2021; 4: 19-29
        • Radianti J.
        • Majchrzak T.A.
        • Fromm J.
        • Wohlgenannt I.
        A systematic review of immersive virtual reality applications for high- er education: Design elements, lessons learned, and research agenda.
        Comput. Educ. 2020; 147103778
        • Saar M.
        • Edan Y.
        • Godo A.
        • Lepar J.
        • Parmet Y.
        • Halachmi I.
        A machine vision system to predict individual cow feed intake of different feeds in a cowshed.
        Animal. 2022; 16100432
        • 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: 731-742
        • Shalf J.
        The future of computing beyond Moore’s Law.
        Philos. Trans.-Royal Soc., Math. Phys. Eng. Sci. 2020; 37820190061
        • Shine P.
        • Murphy M.D.
        Over 20 years of machine learning applications on dairy farms: A comprehensive mapping study.
        Sensors (Basel). 2021; 22: 52
        • Slob N.
        • Catal C.
        • Kassahun A.
        Application of machine learning to improve dairy farm management: A systematic litera- ture review.
        Prev. Vet. Med. 2021; 187105237
        • Smith T.R.
        The potential application of expert systems in dairy extension education.
        J. Dairy Sci. 1989; 72: 2760-2766
        • Spahr S.L.
        • Jones L.R.
        • Dill D.E.
        Expert systems—Their use in dairy herd management.
        J. Dairy Sci. 1988; 71: 879-885
        • Sreenu G.
        • Saleem Durai M.A.
        Intelligent video surveil- lance: A review through deep learning techniques for crowd analysis.
        J. Big Data. 2019; 6: 48
        • van Klompenburg T.
        • Kassahun A.
        • Catal C.
        Crop yield prediction using machine learning: A systematic literature review.
        Comput. Electron. Agric. 2020; 177105709
        • Wang H.
        • Wu H.
        • He Z.
        • Huang L.
        • Church K.W.
        Progress in machine translation.
        Engineering (Beijing). 2021; (In press)
        • Wang J.
        • Zhang Y.
        • Wang J.
        • Zhao K.
        • Li X.
        • Liu B.
        Using machine-learning technique for estrus onset detection in dairy cows from acceleration and location data acquired by a neck-tag.
        Biosyst. Eng. 2022; 214: 193-206
        • Wood P.D.P.
        Algebraic model of the lactation curve in cattle.
        Nature. 1967; 216: 164-165