AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry

被引:7
|
作者
Cho, Youngjoon [1 ]
Kim, Jongwon [2 ]
机构
[1] Korea Univ Technol & Educ, Dept Elect Engn, Cheonan 31253, South Korea
[2] Korea Univ Technol & Educ, Dept Electromech Convergence Engn, Cheonan 31253, South Korea
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 04期
关键词
intelligent monitoring system; augmented recognition model; activity data acquisition; estrus prediction; YOLOv5;
D O I
10.3390/app13042442
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In order to improve a livestock breeding environment that considers securing safe cattle resources and improving productivity for the intelligent farm, we propose an animal-friendly and worker-friendly intellectual monitoring system with Artificial Intelligent (AI) technology. In order to secure safe cattle resources and increase productivity for the livestock industry, it is necessary to secure the self-activities of the cattle and predict the estrous state of target cattle as quickly as possible. For the prediction of the estrous state, it is necessary to continuously observe the cattle behavior by workers and quantify the behavior of the target cattle, but that is not easy for workers and needs a long period of continuous observation. We developed the intelligent monitoring system (IMS) with the ARM (Augmented Recognition Model) for the intelligent farm that can predict the estrus of target cattle and get activity data for individual cattle, and then the system was applied to a typical cattle farm for activity monitoring of the Korean cattle (Hanwoo). Therefore, we confirmed the target Hanwoo group with more than 400 activities among the Hanwoo groups using the ARM threshold. Thus, we verified the potential of the proposed system for tracking multiple similar objects.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] Intelligent Monitoring System with Privacy Preservation Based on Edge AI
    Kim, Soohee
    Park, Joungmin
    Jeong, Youngwoo
    Lee, Seung Eun
    MICROMACHINES, 2023, 14 (09)
  • [32] AI-Based Assistance System for Manufacturing
    Deppe, Sahar
    Brandt, Lukas
    Bruenninghaus, Marc
    Papenkordt, Jorg
    Heindorf, Stefan
    Tschirner-Vinke, Gudrun
    2022 IEEE 27TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2022,
  • [33] AI-Based Health Management System
    Nagulpelli, Swadhin
    Chavan, Akash
    Kandalkar, Aniket
    Kulkarni, Smita
    INTELLIGENT SYSTEMS AND APPLICATIONS, ICISA 2022, 2023, 959 : 379 - 389
  • [34] AI-Based Health Management System
    Nagulpelli, Swadhin
    Chavan, Akash
    Kandalkar, Aniket
    Kulkarni, Smita
    Lecture Notes in Electrical Engineering, 2023, 959 : 379 - 389
  • [35] Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System
    He, Yuning
    Schumann, Johann
    INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT, 2024, 15 (03)
  • [36] A Novel AI-Based System for Detection and Severity Prediction of Dementia Using MRI
    Jain, Varun
    Nankar, Om
    Jerrish, Daryl Jacob
    Gite, Shilpa
    Patil, Shruti
    Kotecha, Ketan
    IEEE ACCESS, 2021, 9 : 154324 - 154346
  • [37] Yield prediction in semiconductor manufacturing using an AI-based cascading classification system
    Stich, Peter
    Wahl, Michael
    Czerner, Peter
    Weber, Christian
    Fathi, Madjid
    2020 IEEE INTERNATIONAL CONFERENCE ON ELECTRO INFORMATION TECHNOLOGY (EIT), 2020, : 609 - 614
  • [38] A study on the RFID based livestock estrus detection system
    Jeong, H. (hsjeong@sunchon.ac.kr), 1600, Science and Engineering Research Support Society, 20 Virginia Court, Sandy Bay, Tasmania, Australia (07):
  • [39] Advancing Bridge Construction Monitoring: AI-Based Building Information Modeling for Intelligent Structural Damage Recognition
    Yang, Honglei
    Xia, Mim
    APPLIED ARTIFICIAL INTELLIGENCE, 2023, 37 (01)
  • [40] Prediction of Breast Cancer Using AI-Based Methods
    Aamir, Sanam
    Rahim, Aqsa
    Bashir, Sajid
    Naeem, Muddasar
    INTELLIGENT ENVIRONMENTS 2021, 2021, 29 : 213 - 220