Precision Livestock Farming: AI for Animal Health and Sustainable Production
Smarter Farms, Healthier Animals, Sustainable Production with AI
About This Course
This 3-day hands-on workshop introduces AI-driven approaches for precision livestock farming. Participants will learn how data from wearables, IoT sensors, and farm records can be transformed into actionable insights for early disease detection, efficient feeding, and informed breeding decisions. Through guided Google Colab notebooks, learners will build practical machine learning models aligned with animal welfare, productivity, and sustainability goals.
Aim
To train participants to apply AI and machine learning for real-time animal health monitoring, feed optimization, and genetic improvement to support sustainable and welfare-driven livestock production.
Workshop Objectives
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Understand key animal health signals and data sources in precision livestock systems.
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Apply AI methods for early disease detection, stress monitoring, and welfare assessment.
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Build predictive models to optimize feed efficiency and reduce resource waste.
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Use AI for breeding and genetic selection to improve productivity and resilience.
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Integrate health, nutrition, and genetics insights for sustainable livestock management.
Workshop Structure
📅 Day 1 — AI for Real-Time Animal Health Monitoring
- Animal health signals: behavioral, physiological, and environmental indicators (activity, temperature, heart rate, rumination)
- Data sources: wearables, IoT sensors, imaging systems, and farm management records
- Health analytics: AI methods for early disease detection, stress identification, and welfare monitoring
- Hands-on: Build a basic machine learning model to detect early health anomalies from livestock sensor data
📅 Day 2 — Predictive Models for Feed Optimization and Resource Efficiency
- Feed efficiency metrics: feed conversion ratio, intake patterns, and growth performance
- Predictive modeling: using AI to forecast feed requirements and optimize feeding schedules
- Sustainability outcomes: reducing feed waste, methane emissions, and resource consumption
- Hands-on: Train a predictive model to optimize feed consumption and identify waste-reduction opportunities
📅 Day 3 — Machine Learning for Breeding and Genetic Optimization
- Breeding data: genomic markers, pedigree records, and performance traits
- AI in selection: predicting breeding values, health resilience, and productivity traits
- Decision support: integrating genetic insights with sustainability and animal welfare goals
- Hands-on: Develop a machine learning model to rank animals for breeding based on genetic and performance indicators
Who Should Enrol?
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Animal scientists, veterinarians, agricultural researchers, data scientists, and livestock industry professionals.
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Basic understanding of animal science or agriculture is helpful; Python/ML basics are a plus but not required.
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Interest in applying AI for animal health, productivity, and sustainability.
Important Dates
Registration Ends
02/13/2026
IST 4 : 30 PM
Workshop Dates
02/13/2026 – 02/15/2026
IST 5 : 30 PM
Workshop Outcomes
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Analyze livestock sensor data to detect early health anomalies using AI models.
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Develop predictive models to optimize feed consumption and improve feed efficiency.
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Identify opportunities to reduce feed waste and environmental impact (e.g., methane emissions).
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Build machine learning models to rank animals for breeding based on genetic and performance traits.
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Apply AI-driven decision support for sustainable and welfare-focused livestock production.
Fee Structure
Student
₹2499 | $65
Ph.D. Scholar / Researcher
₹3499 | $75
Academician / Faculty
₹4499 | $85
Industry Professional
₹6499 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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