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Development of AI-Driven Animal Health Monitoring System for Early Malnutrition Detection in Livestock

dac.digital

Agriculture

Food & Beverage

Information technology

Challenges in Timely Malnutrition Detection in Farm Animals

Traditional methods for detecting malnutrition in livestock are slow and reactive, leading to increased risks of ketosis and acidosis. Current systems lack real-time monitoring capabilities, resulting in delayed interventions and potential economic losses. Farmers require automated, accurate, and proactive health monitoring tools integrated with existing farm infrastructure.

About the Client

Agricultural technology company specializing in livestock health monitoring solutions

Goals for the AI-Powered Animal Health Monitoring System

Develop a neural network model for early malnutrition detection using milk protein-to-fat ratio analysis
Implement real-time sensor data integration for pH and temperature monitoring
Create a user-friendly interface for health insights visualization and alert management
Achieve 3.5x faster detection compared to conventional methods
Ensure zero missed cases of malnutrition through robust preprocessing

Core System Functionalities

Recurrent Neural Network (RNN) for health deterioration prediction
IoT sensor integration for pH and temperature data collection
Automated alert system for malnutrition detection (triggered at pH ≤5.8)
Interactive dashboards with time-series data visualization
Supply chain reporting tools for provenance tracking

Technology Stack Requirements

Deep Neural Network (DNN) architecture

TensorFlow/Keras framework

Python-based backend

IoT sensor protocols (MQTT/CoAP)

Time-series database (InfluxDB)

System Integration Needs

Farm management systems (e.g., FarmERP)
Ruminal probe sensors
Supply chain traceability platforms
Mobile notification services

Performance and Quality Standards

99.9% system uptime for continuous monitoring
Sub-second latency for alert notifications
Data encryption for farm health records
Scalable architecture for 10,000+ sensor nodes
User role-based access control

Expected Business and Animal Welfare Impact

Enables proactive livestock management through early malnutrition detection, reducing animal health risks by 70%. Improves farm productivity through timely interventions, with potential cost savings from prevented livestock losses. Enhances supply chain transparency with auditable health data reporting, meeting food safety compliance requirements.