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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM46936

A Smart IOT-Based Cattle Health Monitoring System with Machine Learning Integration

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM46936

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem46936

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MGT

Mr. GOWTHAM TJ

MGT

Mr. GOUTHAM T

MKKK

Mr. KISHORE KUMAR K

MKTK

Mr. KAAVIYA TAMZHAN K

MRC

Mr. RAJU C

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Abstract

Efficient livestock health monitoring is essential for improving productivity and reducing losses caused by diseases. This paper presents an IoT-based cattle health monitoring system integrated with machine learning to predict cattle health status and identify potential diseases. By leveraging real-time sensor data, Firebase cloud storage, predictive models, and a web-based interface for health visualization, the system enables precise, data-driven interventions. Key diseases addressed include Bovine Respiratory Disease (BRD), Mastitis, Heat Stress, Foot and Mouth Disease (FMD), Ketosis and Lameness. The system’s user-friendly design provides medical insights, making it a valuable tool for farmers and veterinarians.

KEYWORDS

IoT cattle health monitoring machine learning in livestock disease prediction Bovine Respiratory Disease (BRD) Mastitis Heat Stress Foot and Mouth Disease (FMD) Ketosis Lameness real-time monitoring Firebase livestock management cattle health visualization veterinary decision support smart farming solutions

Cite This Article

Mr. GOWTHAM TJ, Mr. GOUTHAM T, Mr. KISHORE KUMAR K, Mr. KAAVIYA TAMZHAN K, Mr. RAJU C, "A Smart IOT-Based Cattle Health Monitoring System with Machine Learning Integration," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025. https://doi.org/10.55041/ijsrem46936

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Publication Date

5/6/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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