Classification of the cattle's behaviors by using accelerometer data with simple behavioral technique | IEEE Conference Publication | IEEE Xplore

Classification of the cattle's behaviors by using accelerometer data with simple behavioral technique


Abstract:

To prognosticate the cattle's health, the farmer can observe the cattle activities such as the time period of walking-grazing, standing and sleeping. However, to monitor ...Show More

Abstract:

To prognosticate the cattle's health, the farmer can observe the cattle activities such as the time period of walking-grazing, standing and sleeping. However, to monitor the cattle's behaviors, it is unable to monitor such behavior all the time and thorough, especially raise many cattle. Therefore, this paper proposes to classify the cattle's behaviors by using the magnitude and standard deviation of accelerometer output signal. The magnitude of each axis is used to classify the behaviors into two groups: 1) walking-grazing and standing and 2) lying down. While the standard deviation of Y-axis is used to notify the behaviors of walking-grazing and standing. The classification results were tested with two cattle and measured precise time of each behavior comparing with human observers. As a result, duration of each behavior is nearby, it has the errors as follows walking-grazing maximum errors 2% standing maximum errors 13% and lying maximum errors 7%.
Date of Conference: 09-12 December 2014
Date Added to IEEE Xplore: 16 February 2015
Electronic ISBN:978-6-1636-1823-8
Conference Location: Siem Reap, Cambodia
Citations are not available for this document.

I. Introduction

An automated system to classify of animal behavior could provide useful information to identify health problems or a risk of animals for disease that would have a significant impact on practical farming and also be useful in alleviating health and economic costs associated with illness [1], [2]

Cites in Papers - |

Cites in Papers - IEEE (3)

Select All
1.
Mohammad Sakib, Sk. Shah Alam, M. Mofazzal Hossain, "Detection of Broiler Behaviors Through a Wearable Sensor System and Machine Learning Methods", 2023 IEEE Engineering Informatics, pp.1-6, 2023.
2.
Kadali Umesh Chandra, Rapolu Shiva Teja, Siri Arelli, Debanjan Das, "CattleCare: IoT-Based Smart Collar for Automatic Continuous Vital and Activity Monitoring of Cattle", 2022 International Conference on Futuristic Technologies (INCOFT), pp.1-7, 2022.
3.
Sukumar Katamreddy, Daniel Riordan, Pat Doody, "Artificial calf weaning strategies and the role of machine learning: A review", 2017 28th Irish Signals and Systems Conference (ISSC), pp.1-6, 2017.

Cites in Papers - Other Publishers (5)

1.
X. Yang, Q. Hu, L. Nie, C. Wang, "Energy-Aware Feature and Classifier for Behaviour Recognition of Laying Hens in an Aviary System", animal, pp.101377, 2024.
2.
G. S. Karthick, M. Sridhar, P. B. Pankajavalli, "Internet of Things in Animal Healthcare (IoTAH): Review of Recent Advancements in Architecture, Sensing Technologies and Real-Time Monitoring", SN Computer Science, vol.1, no.5, 2020.
3.
Quang-Trung Hoang, Phung Cong Phi Khanh, Bui Trung Ninh, Chu Thi Phuong Dung, Tan Duc Tran, "Cow Behavior Monitoring Using a Multidimensional Acceleration Sensor and Multiclass SVM", International Journal of Machine Learning and Networked Collaborative Engineering, vol.2, no.3, pp.110, 2018.
4.
Admela Jukan, Xavi Masip-Bruin, Nina Amla, "Smart Computing and Sensing Technologies for Animal Welfare", ACM Computing Surveys, vol.50, no.1, pp.1, 2018.
5.
Valentin Sturm, Dmitry Efrosinin, Natalia Efrosinina, Leonie Roland, Michael Iwersen, Marc Drillich, Wolfgang Auer, Distributed Computer and Communication Networks, vol.700, pp.120, 2017.

References

References is not available for this document.