Year: 2025
Type: article
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.
DOI: 10.55041/ijsrem46936
Author: Mr. GOWTHAM TJ
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4410125884