Smart Livestock Monitoring and Disease Prediction System
Smart Livestock Monitoring and Disease Prediction System
Agesta Jenifer A1, Muralidharan V 2, Adhithyan K 3 , Prakash M 4 , Naveen M 5 , Yuva Prakash M 6
1 UG-CSE & Holy Cross Engineering College, Thoothukudi , India
2 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India
3 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India
4 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India
5 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India
6 PG-MBA & Karpagam College of Engineering – SoMS, Coimbatore, India
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ABSTRACT - Livestock farming is a cornerstone of global food security and rural economy, yet it remains highly vulnerable to infectious diseases that cause devastating economic losses and threaten food supply chains. Early detection and continuous health monitoring of cattle, poultry, swine, and sheep are critical for timely intervention and disease containment. This paper presents a Smart Livestock Monitoring and Disease Prediction System (SLMDPS) that integrates IoT-based wearable biosensors, edge computing, and machine learning to enable real-time, non-invasive health monitoring of individual animals. The system continuously tracks vital parameters including body temperature, heart rate, respiratory rate, activity level, and rumination behaviour through lightweight sensor collars and ear tags. Data is transmitted via LoRaWAN to an edge gateway and then to a cloud platform. A hybrid Random Forest–Recurrent Neural Network (RF-RNN) model is trained to detect early signs of diseases such as Foot-and-Mouth Disease (FMD), Bovine Respiratory Disease (BRD), Mastitis, and Lumpy Skin Disease (LSD). Experiments conducted on a farm dataset of 300 cattle over six months demonstrate a disease prediction accuracy of 94.7%, with a recall of 96.2% for high-severity conditions. The system also integrates a geofencing module for location-based anomaly detection and a veterinarian-facing mobile dashboard for remote diagnostics and alert management.
Keywords — IoT, Livestock Monitoring, Disease Prediction, Wearable Biosensors, Random Forest, Recurrent Neural Network, LoRaWAN, Precision Livestock Farming, Animal Health, Smart Agriculture.