behavior classification; inertial measurement units; machine learning; precision livestock farming; Animal Science and Zoology; Veterinary (all); General Veterinary
Abstract :
[en] The behavior of livestock on farms is the primary representation of animal welfare, health conditions, and social interactions to determine whether they are healthy or not. The objective of this study was to propose a framework based on inertial measurement unit (IMU) data from 10 dairy cows to classify unitary behaviors such as feeding, standing, lying, ruminating-standing, ruminating-lying, and walking, and identify movements during unitary behaviors. Classification performance was investigated for three machine learning algorithms (K-nearest neighbors (KNN), random forest (RF), and extreme boosting algorithm (XGBoost)) in four time windows (5, 10, 30, and 60 s). Furthermore, feed tossing, rolling biting, and chewing in the correctly classified feeding segments were analyzed by the magnitude of the acceleration. The results revealed that the XGBoost had the highest performance in the 60 s time window with an average F1 score of 94% for the six unitary behavior classes. The F1 score of movements is 78% (feed tossing), 87% (rolling biting), and 87% (chewing). This framework offers a possibility to explore more detailed movements based on the unitary behavior classification.
Disciplines :
Animal production & animal husbandry Computer science
Author, co-author :
Li, Yongfeng; Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China ; AgroBioChem/TERRA, Precision Livestock and Nutrition Unit, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
Shu, Hang ; Université de Liège - ULiège > TERRA Research Centre ; Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China
Bindelle, Jérôme ; Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition
Xu, Beibei; Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China
Zhang, Wenju; Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China
Jin, Zhongming; Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China
Guo, Leifeng; Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China
Wang, Wensheng; Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China
Language :
English
Title :
Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods.
Inner Mongolia Autonomous Region Science and Technology Major Project
Funding text :
Acknowledgments: I want to thank the financial support from the program of China Scholarships Council (202103250035).Funding: This research was funded by Inner Mongolia Autonomous Region Science and Technology Major Project (2020ZD0004), Science and technology innovation project of Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016-AII) and 03 Special Project from Jiangxi province (20204ABC03A09).
Andriamandroso, A.L.H.; Bindelle, J.; Mercatoris, B.; Lebeau, F. A review on the use of sensors to monitor cattle jaw movements and behavior when grazing. Biotechnol. Agron. Soc. Environ. 2016, 20, 273–286. [CrossRef]
Halachmi, I.; Guarino, M.; Bewley, J.; Pastell, M. Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production. Annu. Rev. Anim. Biosci. 2019, 7, 403–425. [CrossRef] [PubMed]
Zehner, N.; Umstätter, C.; Niederhauser, J.J.; Schick, M. System specification and validation of a noseband pressure sensor for measurement of ruminating and eating behavior in stable-fed cows. Comput. Electron. Agric. 2017, 136, 31–41. [CrossRef]
Chelotti, J.O.; Vanrell, S.R.; Galli, J.R.; Giovanini, L.L.; Rufiner, H.L. A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Comput. Electron. Agric. 2018, 145, 83–91. [CrossRef]
Rahman, A.; Smith, D.V.; Little, B.; Ingham, A.B.; Greenwood, P.L.; Bishop-Hurley, G.J. Cattle behaviour classification from collar, halter, and ear tag sensors. Inf. Process. Agric. 2018, 5, 124–133. [CrossRef]
Riaboff, L.; Poggi, S.; Madouasse, A.; Couvreur, S.; Aubin, S.; Bédère, N.; Goumand, E.; Chauvin, A.; Plantier, G. Development of a methodological framework for a robust prediction of the main behaviours of dairy cows using a combination of machine learning algorithms on accelerometer data. Comput. Electron. Agric. 2020, 169, 105179. [CrossRef]
Benaissa, S.; Tuyttens, F.A.M.; Plets, D.; Trogh, J.; Martens, L.; Vandaele, L.; Joseph, W.; Sonck, B. Calving and estrus detection in dairy cattle using a combination of indoor localization and accelerometer sensors. Comput. Electron. Agric. 2020, 168, 105153. [CrossRef]
Thorup, V.M.; Munksgaard, L.; Robert, P.E.; Erhard, H.W.; Thomsen, P.T.; Friggens, N.C. Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms. Animal 2015, 9, 1704–1712. [CrossRef] [PubMed]
Gusterer, E.; Kanz, P.; Krieger, S.; Schweinzer, V.; Süss, D.; Lidauer, L.; Kickinger, F.; Öhlschuster, M.; Auer, W.; Drillich, M.; et al. Sensor technology to support herd health monitoring: Using rumination duration and activity measures as unspecific variables for the early detection of dairy cows with health deviations. Theriogenology 2020, 157, 61–69. [CrossRef]
Wang, J.; He, Z.; Ji, J.; Zhao, K.; Zhang, H. IoT-based measurement system for classifying cow behavior from tri-axial accelerometer. Ciência Rural 2019, 49. [CrossRef]
Achour, B.; Belkadi, M.; Aoudjit, R.; Laghrouche, M. Unsupervised automated monitoring of dairy cows’ behavior based on Inertial Measurement Unit attached to their back. Comput. Electron. Agric. 2019, 167, 105068. [CrossRef]
Smith, D.; Rahman, A.; Bishop-Hurley, G.J.; Hills, J.; Shahriar, S.; Henry, D.; Rawnsley, R. Behavior classification of cows fitted with motion collars: Decomposing multi-class classification into a set of binary problems. Comput. Electron. Agric. 2016, 131, 40–50. [CrossRef]
Zambelis, A.; Wolfe, T.; Vasseur, E. Validation of an ear-tag accelerometer to identify feeding and activity behaviors of tiestallhoused dairy cattle. J. Dairy Sci. 2019, 102, 4536–4540. [CrossRef] [PubMed]
Krieger, S.; Sattlecker, G.; Kickinger, F.; Auer, W.; Drillich, M.; Iwersen, M. Prediction of calving in dairy cows using a tail-mounted tri-axial accelerometer: A pilot study. Biosyst. Eng. 2018, 173, 79–84. [CrossRef]
Peng, Y.; Kondo, N.; Fujiura, T.; Suzuki, T.; Yoshioka, H.; Itoyama, E. Classification of multiple cattle behavior patterns using a recurrent neural network with long short-term memory and inertial measurement units. Comput. Electron. Agric. 2019, 157, 247–253. [CrossRef]
Riaboff, L.; Aubin, S.; Bédère, N.; Couvreur, S.; Madouasse, A.; Goumand, E.; Chauvin, A.; Plantier, G. Evaluation of pre-processing methods for the prediction of cattle behaviour from accelerometer data. Comput. Electron. Agric. 2019, 165, 104961. [CrossRef]
Redbo, I. The influence of restraint on the occurrence of oral stereotypies in dairy cows. Appl. Anim. Behav. Sci. 1992, 35, 115–123. [CrossRef]
Albright, J. Feeding behavior of dairy cattle. J. Dairy Sci. 1993, 76, 485–498. [CrossRef]
Schäfer, E.M.; Sambraus, H.H. [Fodder flinging in cattle]. Tierarztl Prax Ausg G Grosstiere Nutztiere 1998, 26, 141–147.
Gibb, M. Animal grazing/intake terminology and definitions. Pasture Ecol. Anim. Intake 1998, 3, 21–37. [CrossRef]
Andriamandroso, A.L.H.; Lebeau, F.; Beckers, Y.; Froidmont, E.; Dufrasne, I.; Heinesch, B.; Dumortier, P.; Blanchy, G.; Blaise, Y.; Bindelle, J. Development of an open-source algorithm based on inertial measurement units (IMU) of a smartphone to detect cattle grass intake and ruminating behaviors. Comput. Electron. Agric. 2017, 139, 126–137. [CrossRef]
Chelotti, J.O.; Vanrell, S.R.; Martinez Rau, L.S.; Galli, J.R.; Planisich, A.M.; Utsumi, S.A.; Milone, D.H.; Giovanini, L.L.; Rufiner, H.L. An online method for estimating grazing and rumination bouts using acoustic signals in grazing cattle. Comput. Electron. Agric. 2020, 173, 105443. [CrossRef]
Shen, W.; Zhang, A.; Zhang, Y.; Wei, X.; Sun, J. Rumination recognition method of dairy cows based on the change of noseband pressure. Inf. Processing Agric. 2020, 7, 479–490. [CrossRef]
Pahl, C.; Hartung, E.; Grothmann, A.; Mahlkow-Nerge, K.; Haeussermann, A. Suitability of feeding and chewing time for estimation of feed intake in dairy cows. Animal 2016, 10, 1507–1512. [CrossRef]
Barwick, J.; Lamb, D.W.; Dobos, R.; Welch, M.; Trotter, M. Categorising sheep activity using a tri-axial accelerometer. Comput. Electron. Agric. 2018, 145, 289–297. [CrossRef]
Vázquez Diosdado, J.A.; Barker, Z.E.; Hodges, H.R.; Amory, J.R.; Croft, D.P.; Bell, N.J.; Codling, E.A. Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system. Anim. Biotelem. 2015, 3, 15. [CrossRef]
Guo, G.; Wang, H.; Bell, D.; Bi, Y.; Greer, K. KNN model-based approach in classification. In Proceedings of the OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”, Catania, Italy, 3–7 November 2003; pp. 986–996.
Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [CrossRef]
Dietterich, T.G. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Mach. Learn. 2000, 40, 139–157. [CrossRef]
Chen, T.; He, T.; Benesty, M.; Khotilovich, V.; Tang, Y.; Cho, H. Xgboost: Extreme Gradient Boosting. R Package Version 0.4-2. 2015. Available online: https://cran.r-project.org/src/contrib/Archive/xgboost/(accessed on 17 December 2021).
Brennan, J.; Johnson, P.; Olson, K. Classifying season long livestock grazing behavior with the use of a low-cost GPS and accelerometer. Comput. Electron. Agric. 2021, 181, 105957. [CrossRef]
Tamura, T.; Okubo, Y.; Deguchi, Y.; Koshikawa, S.; Takahashi, M.; Chida, Y.; Okada, K. Dairy cattle behavior classifications based on decision tree learning using 3-axis neck-mounted accelerometers. Anim. Sci. J. 2019, 90, 589–596. [CrossRef] [PubMed]
Uenishi, S.; Oishi, K.; Kojima, T.; Kitajima, K.; Yasunaka, Y.; Sakai, K.; Sonoda, Y.; Kumagai, H.; Hirooka, H. A novel accelerometry approach combining information on classified behaviors and quantified physical activity for assessing health status of cattle: A preliminary study. Appl. Anim. Behav. Sci. 2021, 235, 105220. [CrossRef]
Eerdekens, A.; Deruyck, M.; Fontaine, J.; Martens, L.; De Poorter, E.; Joseph, W. Automatic equine activity detection by convolutional neural networks using accelerometer data. Comput. Electron. Agric. 2020, 168, 105139. [CrossRef]
Bersch, S.D.; Azzi, D.; Khusainov, R.; Achumba, I.E.; Ries, J. Sensor data acquisition and processing parameters for human activity classification. Sensors 2014, 14, 4239–4270. [CrossRef]
Barwick, J.; Lamb, D.W.; Dobos, R.; Welch, M.; Schneider, D.; Trotter, M. Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model. Remote Sens. 2020, 12, 646. [CrossRef]
Hu, S.; Ingham, A.; Schmoelzl, S.; McNally, J.; Little, B.; Smith, D.; Bishop-Hurley, G.; Wang, Y.-G.; Li, Y. Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours. Comput. Electron. Agric. 2020, 179, 105857. [CrossRef]
González, L.A.; Bishop-Hurley, G.J.; Handcock, R.N.; Crossman, C. Behavioral classification of data from collars containing motion sensors in grazing cattle. Comput. Electron. Agric. 2015, 110, 91–102. [CrossRef]
Riaboff, L.; Shalloo, L.; Smeaton, A.F.; Couvreur, S.; Madouasse, A.; Keane, M.T. Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data. Comput. Electron. Agric. 2022, 192, 106610. [CrossRef]
Phillips, C. (Ed.) Behavioural Adaptation to Inadequate Environments. In Cattle Behaviour & Welfare; John Wiley & Sons: Hoboken, NJ, USA, 2002; pp. 208–216. [CrossRef]
