About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Artificial Intelligence for Communications and Networks. 4th EAI International Conference, AICON 2022, Hiroshima, Japan, November 30 - December 1, 2022, Proceedings

Research Article

Efficient Estimation of Cow’s Location Using Machine Learning Based on Sensor Data

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-29126-5_7,
        author={Tomohide Sawada and Tom Uchino and Niken P. Martono and Hayato Ohwada},
        title={Efficient Estimation of Cow’s Location Using Machine Learning Based on Sensor Data},
        proceedings={Artificial Intelligence for Communications and Networks. 4th EAI International Conference, AICON 2022, Hiroshima, Japan, November 30 - December 1, 2022, Proceedings},
        proceedings_a={AICON},
        year={2023},
        month={3},
        keywords={Indoor localization Machine learning Sensor data Farm management Cow location},
        doi={10.1007/978-3-031-29126-5_7}
    }
    
  • Tomohide Sawada
    Tom Uchino
    Niken P. Martono
    Hayato Ohwada
    Year: 2023
    Efficient Estimation of Cow’s Location Using Machine Learning Based on Sensor Data
    AICON
    Springer
    DOI: 10.1007/978-3-031-29126-5_7
Tomohide Sawada1,*, Tom Uchino1, Niken P. Martono1, Hayato Ohwada1
  • 1: Department of Industrial Administration, Faculty of Science and Technology
*Contact email: 7422519@ed.tus.ac.jp

Abstract

Indoor localization of dairy cows is important for determining cow behavior and enabling an effective farm management. In this study, a low-cost localization system was constructed by attaching accelerometers to dairy cows kept indoors in a barn in order to obtain radio wave strength. Using link quality indicator (LQI) data, we employed four machine learning models to predict the position of the cow: LightGBM, logistic regression, support vector machine (SVM), and neural network. The prediction performance and computational cost of the models were compared and evaluated. In the monitoring and building of the prediction models for cow’s location, we considered various sizes of location (barn) compartments and evaluated the performance of each prediction model using with different compartments. The experimental results showed that LightGBM and neural networks have an accuracy of 46.6% at 9 m horizontal and 12 m vertical and an accuracy of 90% at 45 m horizontal and 15 m vertical. In terms of the computational score, we may consider whether to use neural network or LightGBM depending on the amount of data to be predicted at a time in the location estimation system.

Keywords
Indoor localization Machine learning Sensor data Farm management Cow location
Published
2023-03-26
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-29126-5_7
Copyright © 2022–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL