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Supplementary Material S1: list of 142 selected papers for analysis
Belongs to the paper for the Animal Journal
Tiltle: Invited review: Big Data in Precision Dairy Farming
Authors: C. Lokhorst, R.M. de Mol and C. Kamphuis
1994
Nielen M, Schukken YH and Hogeveen H 1994. Development of an on-line mastitis
detection system within an integrated knowledge-based system for dairy farm
management support. Veterinary research 25, 285-289.
1995
Lacroix R, Wade KM, Kok R and Hayes JF 1995. Prediction of cow performance with
a connectionist model. Transactions of the American Society of Agricultural
Engineers 38, 1573-1579.
McQueen RJ, Garner SR, Nevill-Manning CG and Witten IH 1995. Applying machine
learning to agricultural data. Computers and Electronics in Agriculture 12, 275-
293.
Nielen M, Schukken YH, Brand A, Deluyker HA and Maatje K 1995. Detection of
subclinical mastitis from on-line milking parlor data. Journal of Dairy science
78, 1039-1049.
Nielen M, Schukken YH, Brand A, Haring S and Ferwerda-van Zonneveld RT 1995.
Comparison of analysis techniques for on-line detection of clinical mastitis.
Journal of Dairy science 78, 1050-1061.
Nielen M, Spigt MH, Schukken YH, Deluyker HA, Maatje K and Brand A 1995.
Application of a neural network to analyse on-line milking parlour data for the
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detection of clinical mastitis in dairy cows. Preventive Veterinary Medicine 22,
15-28.
1996
Scott Mitchell R, Sherlock RA and Smith LA 1996. An investigation into the use of
machine learning for determining oestrus in cows. Computers and Electronics
in Agriculture 15, 195-213.
1997
Lacroix R, Salehi F, Yang XZ and Wade KM 1997. Effects of data preprocessing on
the performance of artificial neural networks for dairy yield prediction and cow
culling classification. Transactions of the American Society of Agricultural
Engineers 40, 839-846.
1998
Salehi F, Lacroix R and Wade KM 1998. Improving dairy yield predictions through
combined record classifiers and specialized artificial neural networks.
Computers and Electronics in Agriculture 20, 199-213.
1999
Yang XZ, Lacroix R and Wade KM 1999. Neural detection of mastitis from dairy herd
improvement records. Transactions of the American Society of Agricultural
Engineers 42, 1063-1071.
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2000
Heald CW, Kim T, Sischo WM, Cooper JB and Wolfgang DR 2000. A computerized
mastitis decision aid using farm-based records: An artificial neural network
approach. Journal of Dairy Science 83, 711-720.
Salehi F, Lacroix R and Wade KM 2000. Development of neuro-fuzzifiers for
qualitative analyses of milk yield. Computers and Electronics in Agriculture 28,
171-186.
Yang XZ, Lacroix R and Wade KM 2000. Investigation into the production and
conformation traits associated with clinical mastitis using artificial neural
networks. Canadian Journal of Animal Science 80, 415-426.
2001
Sanzogni L and Kerr D 2001. Milk production estimates using feed forward artificial
neural networks. Computers and Electronics in Agriculture 32, 21-30.
2002
Pietersma D, Lacroix R, Lefebvre D and Wade KM 2002. Machine-learning assisted
knowledge acquisition to filter lactation curve data. Transactions of the
American Society of Agricultural Engineers 45, 1637-1650.
2003
Grzesiak W, Lacroix R, Wójcik J and Błaszczyk P 2003. A comparison of neural
network and multiple regression predictions for 305-day lactation yield using
partial lactation records. Canadian Journal of Animal Science 83, 307-310.
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Pietersma D, Lacroix R, Lefebvre D and Wade KM 2003. Induction and evaluation of
decision trees for lactation curve analysis. Computers and Electronics in
Agriculture 38, 19-32.
Pietersma D, Lacroix R, Lefebvre D and Wade KM 2003. Performance analysis for
machine-learning experiments using small data sets. Computers and
Electronics in Agriculture 38, 1-17.
Sloth KHMN, Friggens NC, Løvendahl P, Andersen PH, Jensen J and Ingvartsen KL
2003. Potential for improving description of bovine udder health status by
combined analysis of milk parameters. Journal of Dairy Science 86, 1221-
1232.
2005
Brown-Brandl TM, Jones DD and Woldt WE 2005. Evaluating modelling techniques
for cattle heat stress prediction. Biosystems Engineering 91, 513-524.
Goyache F, Díez J, López S, Pajares G, Santos B, Fernandez I and Prieto M 2005.
Machine Learning as an aid to management decisions on high somatic cell
counts in dairy farms. Archiv fur Tierzucht 48, 138-148.
Kim HT, Choi HL, Lee DW and Yoon YC 2005. Recognition of individual Holstein
cattle by imaging body patterns. Asian-Australasian Journal of Animal
Sciences 18, 1194-1198.
Wang E and Samarasinghe S 2005. On-line detection of mastitis in dairy herds using
artificial neural networks. In Proceedings of the International Congress on
Modelling and Simulation: Advances and Applications for Management and
Decision Making, 12-15 December 2005, Melbourne, Australia, pp. 273-278.
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Windig JJ, Calus MPL and Veerkamp RF 2005. Influence of herd environment on
health and fertility and their relationship with milk production. Journal of Dairy
Science 88, 335-347.
2006
Caraviello DZ, Weigel KA, Craven M, Gianola D, Cook NB, Nordlund KV, Fricke PM
and Wiltbank MC 2006. Analysis of reproductive performance of lactating
cows on large dairy farms using machine learning algorithms. Journal of Dairy
Science 89, 4703-4722.
Grzesiak W, Błaszczyk P and 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, 69-83.
Macciotta NPP, Vicario D and Cappio-Borlino A 2006. Use of multivariate analysis to
extract latent variables related to level of production and lactation persistency
in dairy cattle. Journal of Dairy Science 89, 3188-3194.
2007
Hosseinia P, Edrisi M, Edriss MA and Nilforooshan MA 2007. Prediction of second
parity milk yield and fat percentage of dairy cows based on first parity
information using neural network system. Journal of Applied Sciences 7, 3274-
3279.
Pastell ME and Kujalaf M 2007. A probabilistic neural network model for lameness
detection. Journal of Dairy Science 90, 2283-2292.
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Svensson C and Jensen MB 2007. Short communication: Identification of diseased
calves by use of data from automatic milk feeders. Journal of Dairy Science
90, 994-997.
2008
Cavero D, Tölle KH, Henze C, Buxadé C and Krieter J 2008. Mastitis detection in
dairy cows by application of neural networks. Livestock Science 114, 280-286.
Edriss MA, Hosseinnia P, Edrisi M, Rahmani HR and Nilforooshan MA 2008.
Prediction of second parity milk performance of dairy cows from first parity
information using artificial neural network and multiple linear regression
methods. Asian Journal of Animal and Veterinary Advances 3, 222-229.
Kamphuis C, Mollenhorst H, Feelders A and Hogeveen H 2008. Decision tree
induction for detection of clinical mastitis using data from six Dutch dairy herds
milking with an automatic milking system. In Proceedings of the international
Conference on Mastitis Control, 30 September - 2 October 2008, The Hague,
Netherlands, pp. 267-274.
Ortiz-Pelaez A and Pfeiffer DU 2008. Use of data mining techniques to investigate
disease risk classification as a proxy for compromised Biosecurity of cattle
herds in Wales. BMC Veterinary Research 4, 24-40.
2009
Hassan KJ, Samarasinghe S and Lopez-Benavides MG 2009. Use of neural
networks to detect minor and major pathogens that cause bovine mastitis.
Journal of Dairy Science 92, 1493-1499.
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Kamphuis C, Mollenhorst H, Feelders A and Hogeveen H 2009. Decision tree
induction shows potential for the detection of clinical mastitis. In Proceedings
of the 4th European Conference on Precision Livestock Farming, 6-8 July
2009, Wageningen, Netherlands, pp. 299-306.
Linker R and Etzion Y 2009. Potential and limitation of mid-infrared attenuated total
reflectance spectroscopy for real time analysis of raw milk in milking lines.
Journal of Dairy Research 76, 42-48.
Martiskainen P, Järvinen M, Skön JP, Tiirikainen J, Kolehmainen M and Mononen J
2009. Cow behaviour pattern recognition using a three-dimensional
accelerometer and support vector machines. Applied Animal Behaviour
Science 119, 32-38.
Njubi DM, Wakhungu J and Badamana MS 2009. Mating decision support system
using computer neural network model in Kenyan Holstein-Friesian dairy cattle.
Livestock Research for Rural Development 21, Article #45.
Njubi DM, Wakhungu J and Badamana MS 2009. Milk yield prediction in Kenyan
Holstein-Friesian cattle using computer neural networks system. Livestock
Research for Rural Development 21, Article #46.
2010
Gantner V, Jovanovac S, Raguz N, Solic D and Kuterovac K 2010. Nonlinear vs.
linear regression models in lactation curve prediction. Bulgarian Journal of
Agricultural Science 16, 794-800.
Grzesiak W, Zaborski D, Sablik P, Zukiewicz A, Dybus A and Szatkowska I 2010.
Detection of cows with insemination problems using selected classification
models. Computers and Electronics in Agriculture 74, 265-273.
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Kamphuis C, Mollenhorst H, Feelders A, Pietersma D and Hogeveen H 2010.
Decision-tree induction to detect clinical mastitis with automatic milking.
Computers and Electronics in Agriculture 70, 60-68.
Kamphuis C, Mollenhorst H, Heesterbeek JAP and 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, 3616-3627.
Njubi DM, Wakhungu JW and Badamana MS 2010. Use of test-day records to predict
first lactation 305-day milk yield using artificial neural network in Kenyan
Holstein-Friesian dairy cows. Tropical Animal Health and Production 42, 639-
644.
Nohuddin PNE, Coenen F, Christley R and Setzkorn C 2010. Detecting temporal
pattern and cluster changes in social networks: A study focusing UK cattle
movement database. In Proceedings of the 6th International Conference on
Intelligent Information Processing, 13-16 October 2010, Manchester, United
Kingdom, pp. 163-172.
Sun Z, Samarasinghe S and Jago J 2010. Detection of mastitis and its stage of
progression by automatic milking systems using artificial neural networks.
Journal of Dairy Research 77, 168-175.
Wan L and Bao W 2010. Animal disease diagnoses expert system based on SVM. In
Proceedings of Computer and Computing Technologies in Agriculture III and
IFIP Advances in Information and Communication Technology, 14-17 October
2009, Beijing, China, pp.539-545.
Wei X, Qi G, Shen W and Jian S 2010. Using L-M BP algorithm forecase the 305
days production of first-breed dairy. In Proceedings of the Computer and
Computing Technologies in Agriculture III and IFIP Advances in Information
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and Communication Technology, 14-17 October 2009, Beijing, China, pp. 359-
363.
2011
Azzaro G, Caccamo M, Ferguson JD, Battiato S, Farinella GM, Guarnera GC, Puglisi
G, Petriglieri R and Licitra G 2011. Objective estimation of body condition
score by modeling cow body shape from digital images. Journal of Dairy
Science 94, 2126-2137.
González-Velasco HM, García-Orellana CJ, Macías-Macías M, Gallardo-Caballero R
and García-Manso A 2011. A morphological assessment system for 'show
quality' bovine livestock based on image analysis. Computers and Electronics
in Agriculture 78, 80-87.
González-Velasco HM, García-Orellana CJ, Macías-Macías M, Gallardo-Caballero R
and García-Manso A 2011. Application of neural networks to morphological
assessment in bovine livestock. In Proceedingfs of the 12th International
Conference on Engineering Applications of Neural Networks, IFIP Advances in
Information and Communication Technology, 15-18 September 2011, Corfu,
Greece, pp. 203-208.
Grzesiak W, Zaborski D, Sablik P and Pilarczyk R 2011. Detection of difficult
conceptions in dairy cows using selected data mining methods. Animal
Science Papers and Reports 29, 293-302.
Hokkanen AH, Hänninen L, Tiusanen J and Pastell M 2011. Predicting sleep and
lying time of calves with a support vector machine classifier using
accelerometer data. Applied Animal Behaviour Science 134, 10-15.
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Kamphuis C, Mollenhorst H and Hogeveen H 2011. Sensor measurements revealed:
Predicting the Gram-status of clinical mastitis causal pathogens. Computers
and Electronics in Agriculture 77, 86-94.
Lewis FI, Brülisauer F and Gunn GJ 2011. Structure discovery in Bayesian networks:
An analytical tool for analysing complex animal health data. Preventive
Veterinary Medicine 100, 109-115.
Njubi DM, Wakhungu JW and Badamana MS 2011. Prediction of second parity milk
yield of Kenyan Holstein-Friesian dairy cows on first parity information using
neural network system and multiple linear regression methods. Livestock
Research for Rural Development 23, Article #64.
Zaborski D and Grzesiak W 2011. Detection of difficult calvings in dairy cows using
neural classifier. Archiv fur Tierzucht 54, 477-489.
2012
Alsaaod M, Römer C, Kleinmanns J, Hendriksen K, Rose-Meierhöfer S, Plümer L
and Büscher W 2012. Electronic detection of lameness in dairy cows through
measuring pedometric activity and lying behavior. Applied Animal Behaviour
Science 142, 134-141.
Cole JB, Newman S, Foertter F, Aguilar I and Coffey M 2012. Breeding and genetics
symposium: Really big data: Processing and analysis of very large data sets.
Journal of Animal Science 90, 723-733.
Gargiulo GD, Shephard RW, Tapson J, McEwan AL, Bifulco P, Cesarelli M, Jin C, Al-
Ani A, Wang N and van Schaik A 2012. Pregnancy detection and monitoring in
cattle via combined foetus electrocardiogram and phonocardiogram signal
processing. BMC Veterinary Research 8, 164.
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Julio YFH, Yanagi Jr T, Pires MFÁ, Lopes MA and De Lima RR 2012. Prediction of
physiological responses of Holstein dairy cows. In Proceedings of the
American Society of Agricultural and Biological Engineers Annual International
Meeting 2012, 29 July - 1 August 2012, Dallas, Texas, United States, pp.
5719-5739.
Mokaram Ghotoorlar S, Mehdi Ghamsari S, Nowrouzian I and Shiry Ghidary S 2012.
Lameness scoring system for dairy cows using force plates and artificial
intelligence. Veterinary Record 170, 126.
Nadimi ES, Jørgensen RN, Blanes-Vidal V and Christensen S 2012. Monitoring and
classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor
networks and artificial neural networks. Computers and Electronics in
Agriculture 82, 44-54.
Piwczynski D and Sitkowska B 2012. Statistical modelling of somatic cell counts
using the classification tree technique. Archiv fur Tierzucht 55, 332-345.
Rodero E, González A, Luque M, Herrera M and Gutiérrez-Estrada JC 2012.
Classification of Spanish autochthonous bovine breeds. Morphometric study
using classical and heuristic techniques. Livestock Science 143, 226-232.
Sahadevan S, Hofmann-Apitius M, Schellander K, Tesfaye D, Fluck J and Friedrich
CM 2012. Text mining in livestock animal science: Introducing the potential of
text mining to animal sciences. Journal of Animal Science 90, 3666-3676.
Tahmoorespur M, Hosseinnia P, Teimurian M and Aslaminejad AA 2012. Predictions
of 305-day milk yield in Iranian Dairy cattle using test-day records by artificial
neural network. Indian Journal of Animal Sciences 82, 511-516.
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Ankinakatte S, Norberg E, Løvendahl P, Edwards D and 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.
Chung Y, Lee J, Oh S, Park D, Chang HH and Kim S 2013. Automatic detection of
cow's oestrus in audio surveillance system. Asian-Australasian Journal of
Animal Sciences 26, 1030-1037.
Garcia AB 2013. The use of data mining techniques to discover knowledge from
animal and food data: Examples related to the cattle industry. Trends in Food
Science and Technology 29, 151-157.
Graunke KL, Nürnberg G, Repsilber D, Puppe B and Langbein J 2013. Describing
Temperament in an Ungulate: A Multidimensional Approach. PLoS ONE 8,
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Kamphuis C, Frank E, Burke JK, Verkerk GA and Jago JG 2013. Applying additive
logistic regression to data derived from sensors monitoring behavioral and
physiological characteristics of dairy cows to detect lameness. Journal of Dairy
Science 96, 7043-7053.
Martinez-Ortiz C, Everson R and Mottram T 2013. Video tracking of dairy cows for
assessing mobility scores. In Proceedings of the 6th European Conference on
Precision Livestock Farming, 10-12 September 2013, Leuven, Belgium, pp.
154-162.
Melzer N, Wittenburg D, Hartwig S, Jakubowski S, Kesting U, Willmitzer L, Lisec J,
Reinsch N and Repsilber D 2013. Investigating associations between milk
metabolite profiles and milk traits of Holstein cows. Journal of Dairy Science
96, 1521-1534.
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Miekley B, Traulsen I and Krieter J 2013. Mastitis detection in dairy cows: The
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Miekley B, Traulsen I and Krieter J 2013. Principal component analysis for the early
detection of mastitis and lameness in dairy cows. Journal of Dairy Research
80, 335-343.
Piwczyński D, Nogalski Z and Sitkowska B 2013. Statistical modeling of calving ease
and stillbirths in dairy cattle using the classification tree technique. Livestock
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Romanini CEB, Viazzi S, Van Hertem T, Schlageter-Tello A, Halachmi I, Lokhorst K,
Bahr C and Berckmans D 2013. Video pre-processing for the improvement of
an automated lameness detection system for dairy cows. In Proceedings of
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Viazzi S, Van Hertem T, Schlageter-Tello A, Bahr C, Romanini CEB, Halachmi I,
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Shahinfar S, Kalantari AS, Cabrera V and Weigel K 2014. Short communication:
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Shahinfar S, Page D, Guenther J, Cabrera V, Fricke P and Weigel K 2014. Prediction
of insemination outcomes in Holstein dairy cattle using alternative machine
learning algorithms. Journal of Dairy Science 97, 731-742.
Yoder PS, St-Pierre NR and Weiss WP 2014. A statistical filtering procedure to
improve the accuracy of estimating population parameters in feed composition
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Zaborski D, Grzesiak W, Kotarska K, Szatkowska I and Jedrzejczak M 2014.
Detection of difficult Calvings in dairy cows using boosted classification trees.
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2015
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Fischer A, Luginbühl T, Delattre L, Delouard JM and Faverdin P 2015. Rear shape in
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Guzhva O, Ardö H, Herlin A, Nilsson M, Aström K and Bergsten C 2015. Automatic
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