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Vol.13, No.2, May 2024. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning
Aekarat Saeliw, Watcharasuda Hualkasin, Supattra Puttinaovarat
© 2024 Supattra Puttinaovarat, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 13, Issue 2, Pages 966-975, ISSN 2217-8309, DOI: 10.18421/TEM132-12, May 2024.
Received: 08 January 2023. Revised: 25 March 2024.
Abstract:
One of the challenges encountered by farmers engaged in livestock farming is the menace posed by stray or ownerless dogs, causing harm to the animals being raised on the farm. This not only adversely affects the health of the animals but also impacts the overall cost associated with their upbringing. Consequently, this research introduces the development of a sophisticated system aimed at preventing threats and intrusions by dogs that pose harm to farm animals. The system leverages Internet of Things (IoT) technology and employs Machine Learning algorithms, specifically Convolutional Neural Network, for real-time tracking and monitoring. The research findings reveal that the developed system demonstrates a high level of efficiency, swiftly and accurately classifying animals entering areas equipped with cameras, achieving an impressive accuracy rate of 92.54%. Furthermore, the system is equipped to promptly notify users and emit deterrent sounds to repel dogs entering the monitored area, enhancing its effectiveness in safeguarding livestock and optimizing farm management practices.
Keywords –Dog classification, deep learning, IoT, mobile application. |
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