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AI-Driven Livestock Health Monitoring System for Lameness Detection

rootstrap.com

Agriculture

Health & Fitness

Information technology

Challenges in Early Lameness Detection in Cattle

Lameness in cattle significantly impacts animal welfare, productivity, and economic outcomes. Traditional detection methods rely on manual observation of movement patterns and physical deviations, which are labor-intensive, inconsistent, and prone to human error. Existing solutions lack automation and scalability for herd-level monitoring.

About the Client

A cattle breeding enterprise focused on optimizing animal welfare and productivity through advanced health monitoring technologies.

Project Objectives for AI-Enhanced Lameness Detection

Develop an AI-powered system for automated lameness detection using image analysis
Create a scalable, fault-tolerant architecture for real-time health monitoring
Implement machine learning algorithms for accurate image segmentation and classification
Establish a retrainable system with data transparency and traceability

Core Functional Requirements

Real-time video/image analysis for movement pattern recognition
AI-driven image segmentation for back curvature and gait analysis
Binary classification of healthy/unhealthy cattle
Scalable data storage with audit trails
Retraining pipeline for model improvement

Preferred Technologies

Mask R-CNN for image segmentation

TensorFlow/PyTorch for ML model training

COCO dataset for transfer learning

Decoupled microservices architecture

Required System Integrations

Farm management software integration
Camera/sensor hardware integration
Cloud storage for data traceability

Non-Functional Requirements

High scalability for herd size variations
Fault tolerance for continuous operation
Low-latency processing for real-time analysis
Secure data handling with audit capabilities

Expected Business Impact of AI Livestock Health System

The AI solution will enable early lameness detection, improving animal welfare and reducing economic losses from productivity decline. The system will provide consistent monitoring across herds, reduce manual labor costs, and create actionable health insights for proactive veterinary intervention.