Lambing event detection using deep learning from accelerometer data
Tài liệu tham khảo
Alvarenga, 2016, Using a three-axis accelerometer to identify and classify sheep behaviour at pasture, Appl. Anim. Behav. Sci., 181, 91, 10.1016/j.applanim.2016.05.026
Arnold, 1975, Behaviour of the ewe and lamb at lambing and its relationship to lamb mortality, Appl. Anim. Ethol., 2, 25, 10.1016/0304-3762(75)90063-2
Bareham, 1976, The behaviour of lambs on the first day after birth, Br. Vet. J., 132, 152, 10.1016/S0007-1935(17)34737-1
Barwick, 2018, Categorising sheep activity using a tri-axial accelerometer, Comput. Electron. Agric., 145, 289, 10.1016/j.compag.2018.01.007
Biau, 2016, A random forest guided tour, TEST, 25, 197, 10.1007/s11749-016-0481-7
Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324
Brown, 2013, Observing the unwatchable through acceleration logging of animal behavior, Anim. Biotelem., 1, 20, 10.1186/2050-3385-1-20
Bruce, 2021, The impact of lamb and ewe mortality associated with dystocia on Australian and New Zealand sheep farms: A systematic review, meta-analysis and bio-economic model, Prevent. Vet. Med., 196, 10.1016/j.prevetmed.2021.105478
Cervantes, 2020, A comprehensive survey on support vector machine classification: Applications, challenges and trends, Neurocomputing, 408, 189, 10.1016/j.neucom.2019.10.118
Chawla, 2002, SMOTE: synthetic minority over-sampling technique, J. Artificial Intelligence Res., 16, 321, 10.1613/jair.953
Dobos, 2014, The use of GNSS technology to identify lambing behaviour in pregnant grazing Merino ewes, Anim. Prod. Sci., 54, 1722, 10.1071/AN14297
Dwyer, 1996, Effect of ewe and lamb genotype on gestation length, lambing ease and neonatal behaviour of lambs, Reprod. Fertil. Dev., 8, 1123, 10.1071/RD9961123
Fogarty, 2021, Developing a simulated online model that integrates GNSS, accelerometer and weather data to detect parturition events in grazing sheep: a machine learning approach, Animals, 11, 303, 10.3390/ani11020303
Fogarty, 2020, Can accelerometer ear tags identify behavioural changes in sheep associated with parturition?, Anim. Reprod. Sci., 216, 10.1016/j.anireprosci.2020.106345
Frost, 1997, A review of livestock monitoring and the need for integrated systems, Comput. Electron. Agric., 17, 139, 10.1016/S0168-1699(96)01301-4
Gu, 2017
Gurule, 2021, Evaluation of the tri-axial accelerometer to identify and predict parturition-related activities of Debouillet ewes in an intensive setting, Appl. Anim. Behav. Sci., 237, 10.1016/j.applanim.2021.105296
Hinch, 2014, Lamb survival in Australian flocks: A review, Anim. Prod. Sci., 54, 656, 10.1071/AN13236
Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735
Jacobson, 2020, A review of dystocia in sheep, Small Ruminant Res., 192, 10.1016/j.smallrumres.2020.106209
Kiranyaz, 2021, 1D convolutional neural networks and applications: A survey, Mech. Syst. Signal Process., 151, 10.1016/j.ymssp.2020.107398
Kleanthous, 2022, Deep transfer learning in sheep activity recognition using accelerometer data, Expert Syst. Appl., 207, 10.1016/j.eswa.2022.117925
Kovács, 2019, An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets, Appl. Soft Comput., 83, 10.1016/j.asoc.2019.105662
LeCun, 1989, Handwritten digit recognition with a back-propagation network, Adv. Neural Inf. Process. Syst., 2
Lockwood, 2019, Mob size of single-bearing or twin-bearing Merino ewes at lambing may not influence lamb survival when feed-on-offer is high, Animal, 13, 1311, 10.1017/S175173111800280X
Nowak, 1996, Neonatal survival: Contributions from behavioural studies in sheep, Appl. Anim. Behav. Sci., 49, 61, 10.1016/0168-1591(95)00668-0
Nowak, 2006, From birth to colostrum: Early steps leading to lamb survival, Reprod. Nutr. Dev., 46, 431, 10.1051/rnd:2006023
Refshauge, 2015, Neonatal lamb mortality: Factors associated with the death of Australian lambs, Anim. Prod. Sci., 56, 726, 10.1071/AN15121
Regueiro, 2021, Duration of phase II of labour negatively affects maternal behaviour and lamb viability in wool-type primiparous ewes under extensive rearing, Appl. Anim. Behav. Sci., 234, 10.1016/j.applanim.2020.105207
Smith, 2020, Automatic detection of parturition in pregnant ewes using a three-axis accelerometer, Comput. Electron. Agric., 173, 10.1016/j.compag.2020.105392
Sohi, 2022, Determination of ewe behaviour around lambing time and prediction of parturition 7days prior to lambing by tri-axial accelerometer sensors in an extensive farming system, Anim. Prod. Sci., 10.1071/AN21460
Turner, 2023, Deep learning based classification of sheep behaviour from accelerometer data with imbalance, Inf. Process. Agric.
Tyralis, 2019, A brief review of random forests for water scientists and practitioners and their recent history in water resources, Water, 11, 910, 10.3390/w11050910
Vapnik, 1999, An overview of statistical learning theory, IEEE Trans. Neural Netw., 10, 988, 10.1109/72.788640
Ziegler, 2014, Mining data with random forests: Current options for real-world applications, WIREs Data Min. Knowl. Discov., 4, 55, 10.1002/widm.1114
