AI-Driven Livestock Lameness Detection System for Enhanced Animal Welfare and Productivity

The client faces difficulties in consistently detecting early signs of Lameness in cattle, which often require expert observation of behavioral deviations such as back posture and gait anomalies. This leads to delayed diagnosis, compromised animal welfare, and significant productivity losses.

A mid-sized cattle breeding enterprise seeking innovative solutions to improve animal health monitoring and reduce economic losses due to undetected Lameness.

• Develop an AI-powered system capable of automatically identifying and classifying Lameness in cattle through image analysis.
• Create a scalable, fault-tolerant architecture that ensures reliable operation and facilitates retraining and improvements over time.
• Achieve reliable detection accuracy comparable to expert manual observation, enabling early intervention.
• Implement traceability and transparency features for data monitoring and system auditability.
• Reduce false negatives and positives to ensure consistent performance in diverse herd environments.

• Image segmentation module utilizing machine learning algorithms such as Mask R-CNN for object detection and outlining high-risk regions on animal bodies.
• Supervised and unsupervised training data handling for continuous model improvement, including labeled datasets to distinguish healthy versus unhealthy cattle.
• Automated classification system for varying degrees of Lameness severity, from subtle deviations to obvious cases.
• Fault-tolerant, decoupled system architecture supporting scalability and traceability.
• User interface/dashboard for monitoring herd health status, viewing detection results, and system performance metrics.
• Data logging and audit trail features for transparency and compliance.

• Cattle image and video capture systems or sensors.
• Data storage and processing platforms for model training and inference.
• Monitoring dashboards and alert systems.

• System scalability to handle increasing herd sizes and data volume.
• Real-time or near-real-time processing capabilities for timely alerts.
• Robust security measures for data integrity and privacy.
• High accuracy in detection with minimal false positives/negatives.
• Fault tolerance and system reliability under operational conditions.

Implementation of this AI-driven system is expected to significantly improve early detection of Lameness, reducing economic losses by enabling timely treatment, enhancing animal welfare, and increasing productivity. The system aims to achieve detection accuracy comparable to expert observation, with scalable architecture supporting long-term operational efficiency.