Smart Livestock: A Federated Learning Based System for Real-Time Cattle Health, Stress and Water Resource Monitoring.

Area of Science:

• Smart Farming
• Artificial Intelligence
• Animal Health Monitoring

Background:

• Centralized machine learning (ML) models face challenges with data privacy, latency, and rural internet access.
• Federated learning (FL) offers a decentralized approach for real-time health and stress detection in cattle.
• Edge devices like smart collars and sensors can collaboratively train models without sharing raw data.

Purpose of the Study:

• To develop a federated learning (FL)-enabled architecture for real-time cattle health and stress detection.
• To address data privacy, latency, and poor internet connectivity in rural smart farming.
• To create a distributed system using smart farming devices for enhanced livestock monitoring.

Main Methods:

• Utilized federated learning (FL) for collaborative model training across edge devices (smart collars, sensors, cameras).
• Employed multimodal time-series data (temperature, heart rate, motion, environment) for anomaly and behavior pattern detection.
• Combined Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) for advanced data analysis.

Main Results:

• The FL-based model achieved 93.1% accuracy, outperforming centralized ML models.
• Demonstrated superior convergence properties and significant savings in raw data transmission.
• Successfully applied to early stress detection, illness identification, and abnormal behavior analysis in cattle.

Conclusions:

• Federated AI systems provide a secure, privacy-preserving, and efficient solution for livestock monitoring.
• The proposed system enhances animal welfare and economic performance in smart agriculture.
• Potential for future integration with other smart farming systems, such as irrigation management.