Classification of dairy cow excretory events using a tail-mounted accelerometer

Computers and Electronics in Agriculture - Tập 199 - Trang 107187 - 2022
Manod Williams1, Shu Zhan Lai1
1Institute of Biological, Environmental and Rural Science, Aberystwyth University, Penglais Campus, Ceredigion, SY23 3DA, United Kingdom

Tài liệu tham khảo

AHDB, 2021a. Mobility Scoring. Available at: https://ahdb.org.uk/knowledge-library/mobility-scoring-how-to-score-your-cows. Accessed June 7, 2021. AHDB, 2021b. Body condition scoring. Available at: https://ahdb.org.uk/knowledge-library/body-condition-scoring. Accessed June 7, 2021. Balasso, 2021, Machine learning to detect posture and behavior in dairy cows: information from an accelerometer on the Animal’s left flank, Animals, 11, 2972, 10.3390/ani11102972 Barwick, 2018, Categorising sheep activity using a tri-axial accelerometer, Comput. Electron. Agric., 145, 289, 10.1016/j.compag.2018.01.007 Barwick, 2020, Identifying sheep activity from tri-axial acceleration signals using a moving window classification model, Remote Sensing, 12, 646, 10.3390/rs12040646 Benaissa, 2019, On the use of on-cow accelerometers for the classification of behaviours in dairy barns, Res. Vet. Sci., 125, 425, 10.1016/j.rvsc.2017.10.005 Betteridge, 2010, Sensors for detecting and logging spatial distribution of urine patches of grazing female sheep and cattle, Comput. Electron. Agric., 73, 66, 10.1016/j.compag.2010.04.005 Chadwick, 2018, The contribution of cattle urine and dung to nitrous oxide emissions: Quantification of country specific emission factors and implications for national inventories, Sci. Total Environ., 635, 607, 10.1016/j.scitotenv.2018.04.152 Demšar, 2006, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7, 1 Di, 2002, Nitrate leaching in temperate agroecosystems: sources, factors and mitigating strategies, Nutr. Cycl. Agroecosyst., 64, 237, 10.1023/A:1021471531188 Diosdado, 2015, Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system, Anim. Biotelem., 3, 1 Eerdekens, 2020, Automatic equine activity detection by convolutional neural networks using accelerometer data, Comput. Electron. Agric., 168, 10.1016/j.compag.2019.105139 Ferdinandy, B., Gerencsér, L., Corrieri, L., Perez, P., Újváry, D., Csizmadia, G. and Miklósi, Á., 2020. Challenges of machine learning model validation using correlated behaviour data: evaluation of cross-validation strategies and accuracy measures. PloS One, 15(7), p.e0236092. https://doi.org/10.1371/journal.pone.0236092. Fogarty, 2020, Can accelerometer ear tags identify behavioural changes in sheep associated with parturition?, Anim. Reproduct. Sci., 216 González, 2015, Behavioral classification of data from collars containing motion sensors in grazing cattle, Comput. Electron. Agric., 110, 91, 10.1016/j.compag.2014.10.018 Hall, 2009, The WEKA data mining software: an update, SIGKDD Explor., 11, 10, 10.1145/1656274.1656278 Haynes, 1993, Nutrient cycling and soil fertility in the grazed pasture ecosystem, Adv. Agron., 49, 119, 10.1016/S0065-2113(08)60794-4 Kohavi, 1995, August. A study of cross-validation and bootstrap for accuracy estimation and model selection, In Ijcai, 14, 1137 Ledgard, 2001, Nitrogen cycling in low input legume-based agriculture, with emphasis on legume/grass pastures, Plant Soil, 228, 43, 10.1023/A:1004810620983 Ledgerwood, 2010, Evaluation of data loggers, sampling intervals, and editing techniques for measuring the lying behavior of dairy cattle, J. Dairy Sci., 93, 5129, 10.3168/jds.2009-2945 Lush, 2018, Classification of sheep urination events using accelerometers to aid improved measurements of livestock contributions to nitrous oxide emissions, Comput. Electron. Agric., 150, 170, 10.1016/j.compag.2018.04.018 Marshall, 2021, Evaluation of PEETER V1. 0 urine sensors for measuring individual urination behavior of dairy cows. JDS, Communications, 2, 27 Misselbrook, 2016, Automated monitoring of urination events from grazing cattle, Agric. Ecosyst. Environ., 230, 191, 10.1016/j.agee.2016.06.006 Noda, 2014, Animal-mounted gyroscope/accelerometer/magnetometer: In situ measurement of the movement performance of fast-start behaviour in fish, J. Exp. Mar. Biol. Ecol., 451, 55, 10.1016/j.jembe.2013.10.031 Noor, 2017, Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer, Pervasive Mob. Comput., 38, 41, 10.1016/j.pmcj.2016.09.009 Probst, 2019, Hyperparameters and tuning strategies for random forest, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9, e1301 Rahman, 2018, Cattle behaviour classification from collar, halter, and ear tag sensors, Inform. Process. Agric., 5, 124 Riaboff, 2019, Evaluation of pre-processing methods for the prediction of cattle behaviour from accelerometer data, Comput. Electron. Agric., 165, 10496, 10.1016/j.compag.2019.104961 R Core Team, 2019 Sakai, 2019, Behavior classification of goats using 9-axis multi sensors: The effect of imbalanced datasets on classification performance, Comput. Electron. Agric., 166, 10.1016/j.compag.2019.105027 Shepherd, 2017, Evaluation of urine excretion from dairy cows under two farm systems using urine sensors, Agric. Ecosyst. Environ., 236, 285, 10.1016/j.agee.2016.12.017 Shorten, 2021, Assessment of a non-invasive acoustic sensor for detecting cattle urination events, Biosyst. Eng., 207, 177, 10.1016/j.biosystemseng.2021.05.003 Shorten, 2022, Assessment of a non-invasive accelerometer for detecting cattle urination and defecation events, Smart Agric. Technol., 2 Smith, 2014, Correcting for optimistic prediction in small data sets, Am. J. Epidemiol., 180, 318, 10.1093/aje/kwu140 UBC Animal Welfare Program: SOP-HOBO Data Loggers. Available at: http://lfs-awp.sites.olt.ubc.ca/ files/2013/11/SOP-HOBO-Datalogger-november-2013.pdf 2013. Accessed June 7, 2021 University of British Columbia Vancouver, Canada. Van der Heide, 2019, Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle, J. Dairy Sci., 102, 9409, 10.3168/jds.2019-16295 Velthof, 2015, Nitrogen excretion factors of livestock in the European Union: a review, J. Sci. Food Agric., 95, 3004, 10.1002/jsfa.7248 Walton, 2018, Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour, R. Soc. Open Sci., 5, 10.1098/rsos.171442 Watanabe, 2019, Detection of steer defecation events using an accelerometer, Japan, Agric. Res. Quart.: JARQ, 53, 311, 10.6090/jarq.53.311 Williams, 2016, A novel behavioral model of the pasture-based dairy cow from GPS data using data mining and machine learning techniques, J. Dairy Sci., 99, 2063, 10.3168/jds.2015-10254 Williams, 2021, Lying behaviour of housed and outdoor-managed pregnant sheep, Appl. Anim. Behav. Sci., 241, 10.1016/j.applanim.2021.105370