Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Climate-Smart Aquaculture explores how machine learning makes fish and shellfish farming more productive, sustainable and resilient to a changing climate. You learn to work with the data of modern aquaculture — water-quality sensors, cameras and environmental monitors — and build models for the field’s priorities: monitoring water conditions and fish health, optimising feeding and growth, and anticipating climate-driven risks. The course connects these to sustainable, climate-adaptive production. You finish able to reason about an AI approach to an aquaculture problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to climate-smart aquaculture — monitoring water and fish health, optimising feeding and yield, and building resilience to climate stress in fish farming.
📋 Course Objectives
1. Monitor water quality and fish health with sensors.
2. Optimise feeding and growth with data.
3. Detect disease and stress early.
4. Anticipate climate-driven risks.
5. Connect models to sustainable production.
👥 Who Should Enroll?
• Aquaculture and fisheries professionals
• Agri-tech and data scientists
• Sustainability and food-systems staff
• Students of aquaculture
🚀 Key Learning Outcomes
• An understanding of AI in aquaculture.
• A climate-smart farming perspective.
• An aquaculture-analytics project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Aquaculture Production and Its Constraints
• Pond, cage and recirculating systems and the differing data each affords
• Water quality parameters that drive mortality: oxygen, ammonia, temperature
• Stocking density, feed conversion and the economics that govern decisions
Sensing in a Hostile Environment
• In-water sensor deployment, biofouling and calibration drift
• Underwater imaging and acoustic biomass estimation
• Data gaps from power and connectivity limits at remote sites
Prediction for Operational Decisions
• Dissolved oxygen and water quality forecasting for aeration control
• Growth and biomass modelling to time harvest
• Disease and mortality early warning, and the cost of a missed event
Precision Feeding and Waste Reduction
• Appetite detection and demand feeding control
• Feed conversion optimisation and effluent load reduction
• Balancing growth rate against welfare and environmental limits
Resilience and Certification
• Climate risk: warming, hypoxia, salinity shift and extreme events
• Site selection and species choice under projected conditions
• Certification, traceability and market access requirements
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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