Advanced Neural Network System for Early Detection of Malnutrition in Farm Animals

Farm operators face difficulties in promptly identifying early signs of malnutrition among livestock, risking health deterioration and economic losses. Existing methods are slower, less integrated, and lack real-time insights, making proactive intervention challenging.

A mid-sized livestock farm utilizing sensor technology to monitor animal health and nutrition in real-time.

• Develop a real-time monitoring system integrating farm sensor data (temperature, pH, milk protein ratios) for early detection of malnutrition.
• Achieve at least 3.5 times faster identification of health deterioration compared to traditional analytical methods.
• Ensure reliable prediction accuracy with minimized false positives to avoid unnecessary alarm fatigue.
• Create an intuitive user interface for farm managers to visualize bio-parameter data, system alerts, and health trends.
• Enable autonomous data collection and integration with existing farm sensor infrastructure.
• Provide data provenance and traceability for supply chain transparency.

• Integration with farm sensors to collect metrics such as temperature, pH levels, and milk parameters.
• A deep neural network model, specifically a recurrent neural network (RNN), trained to analyze bio parameters for predicting health deterioration.
• Real-time data visualization dashboards displaying measurements, predictions, and historical trends.
• Automated alert system set to trigger when bio-parameter thresholds (e.g., pH > 5.8 over several hours) are exceeded, indicating potential malnutrition.
• Preprocessing modules to ensure no data points are missed, enhancing detection reliability.
• Proven validation of prediction accuracy with a focus on minimizing false positives.

• Farm sensor systems providing temperature and pH data
• Milk analysis systems for bio-parameter extraction
• User interface platforms for visualization and alert management
• Supply chain traceability systems

• High system availability with 99.9% uptime for real-time monitoring
• Low latency processing to enable immediate alerts
• Scalable architecture to support increasing number of farm sensors and data volume
• Data security and privacy compliant with farm data regulations
• Predictive accuracy with an expected low false positive rate, validated against initial testing

The implementation of this neural network-based animal health monitoring system is expected to enable early detection of malnutrition at least 3.5 times faster than traditional methods. This will reduce health risks such as ketosis and acidosis, improve animal welfare, optimize farm management, and potentially lower economic losses related to health deterioration, thus enhancing overall farm productivity and supply chain transparency.