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Classification of dairy cows’ behavior by energy-efficient sensor

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Abstract

Precision Livestock farming (PLF) is a combination of the use of biosensors and a data mining processes to ensure automatic and individual dairy cow management. It essentially allows a better monitoring of health status, morphology, reproduction, and welfare of dairy cows. Collecting behavioral activities of dairy cows has the potential to improve the productivity and the longevity. Behavioral monitoring with attached sensors helps to detect several diseases at an early stage before clinical signs appear and thus facilitates treatment. This paper proposes a new technique based on the time-driven embedded sensor to continuously measure and classify the behavior of dairy cows housed in free stall. This sensor is based on an Inertial Measurement Unit and is attached to the back of dairy cow. A machine learning model has been elaborated and implemented in the sensor. Univariate and multivariate models were used to define threshold values for the decision trees used to classify the behavior of dairy cows. The obtained results showed that our results in terms of rates classification and energy consumption are very promising compared to other research works.

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References

  1. Augusto JC, Coronato A (2015) Introduction to the inaugural issue of the Journal of Reliable Intelligent Environments. J Reliable Intell Environ 1:1–10

    Article  Google Scholar 

  2. Cacciagrano DR, Corradini F, Culmone R, Gorogiannis N, Mostarda L, Raimondi F et al (2018) Analysis and verification of ECA rules in intelligent environments. J Ambient Intell Smart Environ 10(3):261–273

    Article  Google Scholar 

  3. da Rosa Tavares JE, Victória Barbosa JL (2020) Apollo SignSound: an intelligent system applied to ubiquitous healthcare of deaf people. J Reliab Intell Environ 7:157–170

  4. Tawfik H, Hu J, Yuen KKF et al (2020) Special issue on security, usability and sustainability of smart cities. J Reliab Intell Environ 6:1

    Article  Google Scholar 

  5. Grover HS, Adarsh Kumar D (2020) Cryptanalysis and improvement of a three-factor user authentication scheme for smart grid environment. J Reliab Intell Environ 6:249–260

  6. Uma S, Eswari R (2021) Accident prevention and safety assistance using IOT and machine learning. J Reliable Intell Environ. https://doi.org/10.1007/s40860-021-00136-3

  7. Alfa AA, Alhassan JK, Olaniyi OM et al (2020) Blockchain technology in IoT systems: current trends, methodology, problems, applications, and future directions. J Reliab Intell Environ 7:115–14

  8. Shareef Z, Reddy SRN (2020) Deployment of sensor nodes for aquaculture in western Godavari delta: results, challenges and issues. J Reliab Intell Environ 6:153–167

    Article  Google Scholar 

  9. Achour B, Belkadi M, Filali I, Laghrouche M, Lahdir M (2020) Image analysis for individual identification and feeding behaviour monitoring of dairy cows based on Convolutional Neural Networks (CNN). Biosyst Eng 198:31–49

  10. Goel SS, Goel A, Kumar M et al (2021) A review of Internet of Things: qualifying technologies and boundless horizon. J Reliab Intell Environ 7:23–33

    Article  Google Scholar 

  11. Keserwani PK, Govil MC, Pilli ES et al (2021) A smart anomaly-based intrusion detection system for the Internet of Things (IoT) network using GWO-PSO-RF model. J Reliab Intell Environ 7:3–21

    Article  Google Scholar 

  12. Laghrouche M, Montes L, Boussey J, Meunier D, Ameur S, Adane A (2011) In situ calibration of wall shear stress sensor for micro fluidic application. Procedia Eng 25:1225–1228

  13. Onesimu BJA, Kadam A, Sagayam KM et al (2021) Internet of things based intelligent accident avoidance system for adverse weather and road conditions. J Reliab Intell Environ. https://doi.org/10.1007/s40860-021-00132-7

  14. Demongivert CD, Bouchard K, Gaboury S et al (2021) A distributable event-oriented architecture for activity recognition in smart homes. J Reliab Intell Environ. https://doi.org/10.1007/s40860-020-00125-y

  15. Halachmi I (2015) Precision livestock farming applications. Wageningen Academic Publishers, Wageningen

    Book  Google Scholar 

  16. Fogsgaard K, Røntved C, Sørensen P, Herskin M (2012) Sickness behavior in dairy cows during Escherichia coli mastitis. J Dairy Sci 95(2):630–638

    Article  Google Scholar 

  17. Siivonen J et al (2011) Impact of acute clinical mastitis on cow behaviour. Appl Anim Behav Sci 132(3–4):101–106

    Article  Google Scholar 

  18. Halasa T, Huijps K, Østerås O, Hogeveen H (2007) Economic effects of bovine mastitis and mastitis management: a review. Vet Q 29(1):18–31

    Article  Google Scholar 

  19. Pilar Sepulveda-Varas Kathryn L, Proudfoot Daniel M, Weary M, von Keyserlingk AG (2016) Changes in behaviour of dairy cows with clinical mastitis. Appl Anim Behav Sci 175:8–13

  20. Zebari H, Rutter S, Bleach E (2018) Characterizing changes in activity and feeding behaviour of lactating dairy cows during behavioural and silent oestrus. Appl Anim Behav Sci 206:12–17

    Article  Google Scholar 

  21. Stangaferro M, Wijma R, Caixeta L, Al-Abri M, Giordano J (2016) Use of rumination and activity monitoring for the identification of dairy cows with health disorders: part I. Metabolic and digestive disorders. J Dairy Sci 99:7395–7410

  22. Arcidiacono C, Porto S, Mancino M, Cascone G (2017) Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data. Comput Electron Agric 134:124–134

  23. Robert B, White B, Renter D, Larson R (2009) Evaluation of three-dimensional accelerometers to monitor and classify behavior patterns in cattle. Comput Electron Agric 67(1–2):80–84

    Article  Google Scholar 

  24. Dutta R, Smith D, Rawnsley R, Bishop-Hurley G, Hills J, Timms G, Henry D (2015) Dynamic cattle behavioral classification using supervised ensemble classifiers. J Comput Electron Agric 111:18–28

    Article  Google Scholar 

  25. Awasthi A, Awasthi A, Riordan D, Walsh J (2016) Non-invasive sensor technology for the development of a dairy cattle health monitoring system. Computers 5(4):23

    Article  Google Scholar 

  26. Benaissa S et al (2019) On the use of on-cow accelerometers for the classification of behaviours in dairy barns. Res Vet Sci 125:425–433

    Article  Google Scholar 

  27. Smith D et al (2016) Behavior classification of cows fitted with motion collars: Decomposing multi-class classification into a set of binary problems. Comput Electron Agric 131:40–50

    Article  Google Scholar 

  28. Abell K, Theurer M, Larson R, White B, Hardin D, Randle R (2017) Predicting bull behavior events in a multiple-sire pasture with video analysis, accelerometers, and classification algorithms. Comput Electron Agric 136:221–227

  29. Achour B, Belkadi M, Aoudjit R, Laghrouche M (2019) Unsupervised automated monitoring of dairy cows’ behavior based on Inertial Measurement Unit attached to their back. Comput Electron Agric 167:105068

  30. Behmann J, Hendriksen K, Müller U, Büscher W, Plümer L (2016) Support Vector machine and duration-aware conditional random field for identification of spatio-temporal activity patterns by combined indoor positioning and heart rate sensors. GeoInformatica 20(4):693–714

    Article  Google Scholar 

  31. Saint-Dizier M, Chastant-Maillard S (2012) Towards an automated detection of oestrus in dairy cattle. Reprod Domest Anim 47(6):1056–1061

    Article  Google Scholar 

  32. Metzner M, Sauter-Louis C, Seemueller A, Petzl W, Zerbe H (2015) Infrared thermography of the udder after experimentally induced Escherichia coli mastitis in cows. Vet J 204(3):360–362

    Article  Google Scholar 

  33. Mokhtari G, Aminikhanghahi S, Zhang Q et al (2018) Fall detection in smart home environments using UWB sensors and unsupervised change detection. J Reliab Intell Environ 4:131–139

    Article  Google Scholar 

  34. Alaskar H, Hussain AJ, Khan W et al (2020) A data science approach for reliable classification of neuro-degenerative diseases using gait patterns. J Reliab Intell Environ 6:233–247

    Article  Google Scholar 

  35. Borzì L, Varrecchia M, Olmo G et al (2019) Home monitoring of motor fluctuations in Parkinson’s disease patients. J Reliab Intell Environ 5:145–162

    Article  Google Scholar 

  36. Wang W, Lu Z, Tsui CL (2019) A design for autonomous self-building blocks. J Reliab Intell Environ 5:115–128

    Article  Google Scholar 

  37. Patel A, Shah J (2020) Real-time human behaviour monitoring using hybrid ambient assisted living framework. J Reliab Intell Environ

  38. Berckmans D (2017) General introduction to precision livestock farming. Anim Front 7(1):6–11

    Article  Google Scholar 

  39. Banda G, Bommakanti CK, Mohan H (2016) One IoT: an IoT protocol and framework for OEMs to make IoT-enabled devices forward compatible. J Reliab Intell Environ 2:131–144

    Article  Google Scholar 

  40. Preuveneers D, Joosen W (2016) Semantic analysis and verification of context-driven adaptive applications in intelligent environments. J Reliab Intell Environ 2:53–73

    Article  Google Scholar 

  41. Laghrouche M, Saddaoui R, Mellal I, Nachef M, Ameur S (2016) Low-cost embedded spirometer based on commercial micro machined platinum thin film. Procedia Eng 168:1681–1684

  42. Dahmen J, Cook DJ, Wang X et al (2017) Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats. J Reliab Intell Environ 3:83–98

    Article  Google Scholar 

  43. Hao J, Bouzouane A, Gaboury S (2017) Complex behavioral pattern mining in non-intrusive sensor-based smart homes using an intelligent activity inference engine. J Reliab Intell Environ 3:99–116

    Article  Google Scholar 

  44. Thomas BL, Crandall AS, Cook DJ (2016) A Genetic Algorithm approach to motion sensor placement in smart environments. J Reliab Intell Environ 2:3–16

    Article  Google Scholar 

  45. Sasiwat Y, Buranapanichkit D, Chetpattananondh K et al (2019) Human movement effects on the performance of the RSSI-based trilateration method: adaptive filters for distance compensation. J Reliab Intell Environ

  46. Nielsen LR, Pedersen AR, Herskin MS, Munksgaard L (2010) Quantifying walking and standing behaviour of dairy cows using a moving average based on output from an accelerometer. Appl Anim Behav Sci 127

  47. Barwick J, Lamb D, Dobos R, Welch M, Trotter M (2018) Categorising sheep activity using a tri-axial accelerometer. Comput Electron Agric 145:289–297

    Article  Google Scholar 

  48. Burla JB, Ostertag A, Westerath HS, Hillmann E (2014) Gait determination and activity measurement in horses using an accelerometer. Comput Electron Agric 102: 127–133

  49. Guo L, Welch M, Dobos R, Kwan P, Wang W (2018) Comparison of grazing behaviour of sheep on pasture with different sward surface heights using an inertial measurement unit sensor. Comput Electron Agric 150:394–401

  50. White B, Coetzee J, Renter D, Babcock A, Thomson D, Andresen D (2008) Evaluation of two-dimensional accelerometers to monitor behavior of beef calves after castration. Am J Vet Res 69(8):1005–1012

    Article  Google Scholar 

  51. Andriamandroso A, Lebeau F, Beckers Y, Froidmont E, Dufrasne I, Heinesch B et al (2017) 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 139:126–137

    Article  Google Scholar 

  52. Benaissa S, Tuyttens FA, Plets D, Cattrysse H, Martens L, Vandaele L, Joseph W, Sonck B (2019) Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers. Appl Anim Behav Sci 211:9–16

  53. Barker ZE, Vázquez Diosdado JA, Codling EA, Bell NJ, Hodges HR, Croft DP, Amory JR (2018) Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle. J Dairy Sci 101(7):6310–6321

  54. Martiskainen P, Jarvinen M, Skon JP, Tiirikainen J, Kolehmainen M, Mononen J (2009) Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Appl Anim Behav Sci 119:32–38

  55. Shen W, Cheng F, Zhang Y, Wei X, Fu Q, Zhang Y(2019) Automatic recognition of ingestive-related behaviors of dairy cows based on triaxial acceleration. Inf Process Agric

  56. Kumar SAP, Bao S, Singh V et al (2019) Flooding disaster resilience information framework for smart and connected communities. J Reliab Intell Environ 5:3–15

    Article  Google Scholar 

  57. Shahriar MS, Smith D, Rahman A, Freeman M, Hills J, Rawnsley R, Henry D, Bishop-Hurley G (2016) Detecting heat events in dairy cows using accelerometers and unsupervised learning. Comput Electron Agric 128:20–26

  58. Hammond T, Springthorpe D, Walsh R, Berg-Kirkpatrick T (2016) Using accelerometers to remotely and automatically characterize behavior in small animals. J Exp Biol 219(11):1618–1624

    Google Scholar 

  59. Schmidtke HR (2018) A survey on verification strategies for intelligent transportation systems. J Reliab Intell Environ 4:211–224

    Article  Google Scholar 

  60. Rudd-Orthner RNM, Mihaylova L (2020) Repeatable determinism using non-random weight initialisations in smart city applications of deep learning. J Reliab Intell Environ 6:31–49

    Article  Google Scholar 

  61. Cuzzocrea A, Martinelli F, Mercaldo F et al (2018) Experimenting and assessing machine learning tools for detecting and analyzing malicious behaviors in complex environments. J Reliab Intell Environ 4:225–245

    Article  Google Scholar 

  62. Mishra BK, Thakker D, Mazumdar S et al (2020) A novel application of deep learning with image cropping: a smart city use case for flood monitoring. J Reliab Intell Environ 6:51–61

    Article  Google Scholar 

  63. Gonzãlez LA, Bishop-Hurley GJ, Handcock RN, Crossman C (2015) Behavioral classification of data from collars containing motion sensors in grazing cattle. J Comput Electron Agric 110:91–102

    Article  Google Scholar 

  64. Diosdado V, Barker JA, Hodges ZE, Amory HR, Croft JR, Bell DP, Codling NJ, Edward A (2015) Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system. Anim Biotelemetry 3(15)

  65. Pastell M, Frondelius L (2018) A hidden Markov model to estimate the time dairy cows spend in feeder based on indoor positioning data. Comput Electron Agric 152

  66. Williams ML, James WP, Rose MT (2017) Fixed-time data segmentation and behavior classification of pasture-based cattle: Enhancing performance using a hidden Markov model. Comput Electron Agric 142(B)

  67. Yedle B, Shrivastava G, Kumar A, Mishra AK, Mishra TK (2021) A survey: security issues and challenges in internet of things. In: Tripathy A, Sarkar M, Sahoo J, Li KC, Chinara S (eds) Advances in distributed computing and machine learning. Lecture notes in networks and systems, vol 127 Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_8

  68. Hendriks SJ, Phyn CVC, Huzzey JM, Mueller KR, Turner S-A, Donaghy DJ, Roche JR (2020) Graduate student literature review: evaluating the appropriate use of wearable accelerometers in research to monitor lying behaviors of dairy cows. Jo Dairy Sci 103(12)

  69. Kiani F (2018) Animal behavior management by energy-efficient wireless sensor networks. Comput Electron Agric 151:478–484

    Article  Google Scholar 

  70. Ledgerwood DN, Winckler C, Tucker CB (2010) Evaluation of data loggers, sampling intervals, and editing techniques for measuring the lying behavior of dairy cattle. J Dairy Sci 93(11):5129–5139

  71. Mattachini G, Riva E, Bisaglia C, Pompe JCAM, Provolo G (2013) Methodology for quantifying the behavioral activity of dairy cows in freestall barns. J Anim Sci 91:4899–4907

  72. Darr M, Epperson W (2009) Embedded sensor technology for real time determination of animal lying time. Comput Electron Agric 66(1):106–111

    Article  Google Scholar 

  73. Yang F, Zhang L (2017) Real-time human activity classification by accelerometer embedded wearable devices. In: 4th International Conference on Systems and Informatics (ICSAI), Hangzhou, China, 11–13 November 2017. IEEE, Piscataway, pp 469–473

  74. Bisio I, Lavagetto F, Márchese M, Sciarrone A (2014) Comparison of situation awareness algorithms for remote health monitoring with smartphones. IEEE Global Communications Conference, Austin, pp 2454–2459

  75. Shoaran M, Haghi BA, Taghavi M, Farivar M, Emami-Neyestanak A (2018) Energy-efficient classification for resource-constrained biomedical applications. IEEE J Emerg Sel Topics Circuits Syst 8(4):693–707

    Article  Google Scholar 

  76. Tang Y, Verma N (2018) Energy-efficient pedestrian detection system: exploiting statistical error compensation for lossy memory data compression. In: IEEE transactions on very large scale integration (VLSI) systems, vol 26, no 7

  77. Kok A, van Knegsel ATM, van Middelaar CE, Hogeveen H, Kemp B, de Boer IJM (2015) Technical note: Validation of sensor-recorded lying bouts in lactating dairy cows using a 2-sensor approach. J Dairy Sci 98:7911–7916

  78. Henriksen JC, Munksgaard L (2019) Validation of AfiTagII, a device for automatic measuring of lying behaviour in Holstein and Jersey cows on two different bedding materials. Animal 13:617–621

  79. Borchers MR, Chang YM, Tsai IC, Wadsworth BA, Bewley JM (2016) A validation of technologies monitoring dairy cow feeding, ruminating, and lying behaviors. J Dairy Sci 99(9):7458–7466

  80. Gómez-Cárdenas A, Masip-Bruin X, Marín-Tordera E et al (2019) Resource identification in fog-to-cloud systems: toward an identity management strategy. J Reliab Intell Environ 5:29–40

    Article  Google Scholar 

  81. Andò B, Baglio S, Lombardo CO, Marletta V (2016) A multisensor data-fusion approach for ADL and fall classification. IEEE Trans Instrum Meas 65(9):1960–1967

    Article  Google Scholar 

  82. Huynh Q, Nguyen U, Irazabal L, Ghassemian N, Binh T (2015) Optimization of an accelerometer and gyroscope-based fall detection algorithm. J Sensors 2015:1–8

  83. Benaglia T, Chauveau D, Hunter D, Young D (2009) Mixtools: an R package for analyzing finite mixture models. J Stat Softw 32(6). https://doi.org/10.18637/jss.v032.i061

  84. Borges G, Brusamarello V (2015) Sensor fusion methods for reducing false alarms in heart rate monitoring. J Clin Monit Comput 30(6):859–867

    Article  Google Scholar 

  85. Tsinganos P, Skodras A (2018) On the comparison of wearable sensor data fusion to a single sensor machine learning technique in fall detection. Sensors 18(2):592

    Article  Google Scholar 

  86. Scrucca L, Fop M, Murphy T, Raftery A (2016) mclust 5: Clustering. Classification and density estimation using Gaussian finite mixture models. R J 8(1):289

  87. Horvath Z, Jenak I, Brachmann F (2017) Battery consumption of smartphone sensors. J Reliab Intell Environ 3:131–136

    Article  Google Scholar 

  88. Nielsen PP, Fontana I, Sloth KH, Guarino M, Blokhuis H (2018) Technical note: validation and comparison of 2 commercially available activity loggers. J Dairy Sci 101(6):5449–5453

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Acknowledgements

The authors are grateful to the dairy cows farm SOFLAIT, located in the region of Draa Ben Khedda, Tizi Ouzou, Algeria, for providing us with the opportunity of carrying out the trials and allowing the data collection.

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Authors and Affiliations

  1. LARI Laboratory, Mouloud Mammeri University of Tizi-Ouzou, Tizi Ouzou, Algeria

    Brahim Achour, Malika Belkadi, Mourad Laghrouche, Mustapha Lalam & Mehammed Daoui

  2. LAMPA Laboratory, Mouloud Mammeri University of Tizi-Ouzou, Tizi Ouzou, Algeria

    Rachida Aoudjit

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  1. Brahim Achour
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  2. Malika Belkadi
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  3. Rachida Aoudjit
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Correspondence to Brahim Achour.

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Achour, B., Belkadi, M., Aoudjit, R. et al. Classification of dairy cows’ behavior by energy-efficient sensor. J Reliable Intell Environ 8, 165–182 (2022). https://doi.org/10.1007/s40860-021-00144-3

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