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Cow estrus detection with low-frequency accelerometer sensor by unsupervised learning

Published: 04 December 2019 Publication History

Abstract

In recent years, Internet of Things (IoT) and Machine Learning (ML) has been applied successfully in agriculture. These technologies increase productivity as well as reduce labor significantly. In this paper, we focus on improving the autonomous cow estrus detection system in terms of energy consumption and precision. In previous detection pipelines, an accelerometer is mounted to the neck of cows to capture motion data with high frequency, followed by the ML algorithm to check the data and determine whether it is in estrus or not. Instead, we configured the accelerometer to sample with low frequency for minimizing its energy consumption. However, low-sampling rate as input of ML pipeline leads to an undesirable higher false alarm rate. To solve this problems, we designed a pipeline of unsupervised learning with a new heuristic post-processing algorithm. The proposed post-processing algorithm is a backtracking algorithm that incorporates the timing constraint of the period obtained by agriculture knowledge. With the constraint, the post-processing algorithm facilitates a significantly higher precision than simple adaptive threshold techniques in previous studies on a simulated dataset. Finally, the overall result of the pipeline with the proposed algorithm is visualized on real-world data captured on the farm in our agriculture department.

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Cited By

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  • (2024)Development of a Novel Classification Approach for Cow Behavior Analysis Using Tracking Data and Unsupervised Machine Learning TechniquesSensors10.3390/s2413406724:13(4067)Online publication date: 22-Jun-2024
  • (2024)Scoping review of precision technologies for cattle monitoringSmart Agricultural Technology10.1016/j.atech.2024.1005969(100596)Online publication date: Dec-2024
  • (2024)Sensor-Type Agnostic Heat Detection in Dairy Cows using Multi-autoencoders with Shared Latent SpaceApplied Soft Computing10.1016/j.asoc.2024.112200(112200)Online publication date: Sep-2024
  • Show More Cited By

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    cover image ACM Other conferences
    SoICT '19: Proceedings of the 10th International Symposium on Information and Communication Technology
    December 2019
    551 pages
    ISBN:9781450372459
    DOI:10.1145/3368926
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • SOICT: School of Information and Communication Technology - HUST
    • NAFOSTED: The National Foundation for Science and Technology Development

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    Publication History

    Published: 04 December 2019

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    Author Tags

    1. estrus detection
    2. internet of things
    3. unsupervised learning

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    Overall Acceptance Rate 147 of 318 submissions, 46%

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    Cited By

    View all
    • (2024)Development of a Novel Classification Approach for Cow Behavior Analysis Using Tracking Data and Unsupervised Machine Learning TechniquesSensors10.3390/s2413406724:13(4067)Online publication date: 22-Jun-2024
    • (2024)Scoping review of precision technologies for cattle monitoringSmart Agricultural Technology10.1016/j.atech.2024.1005969(100596)Online publication date: Dec-2024
    • (2024)Sensor-Type Agnostic Heat Detection in Dairy Cows using Multi-autoencoders with Shared Latent SpaceApplied Soft Computing10.1016/j.asoc.2024.112200(112200)Online publication date: Sep-2024
    • (2023)An Investigation of Surface Temperature Effect on Estrus Detection of Dairy Cows using Supervised Learning2023 Third International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP)10.1109/ICA-SYMP56348.2023.10044943(49-52)Online publication date: 18-Jan-2023

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