Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The Conservation Edge AI Lab focuses on building real‑time wildlife and threat alert systems using edge AI, camera traps, acoustic sensors, and IoT networks in remote field conditions. Participants learn to detect poaching, habitat intrusion, and animal distress events as they occur, turning raw sensor data into actionable alerts for rangers and conservation teams. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Conservation Edge AI Lab focuses on building real‑time wildlife and threat alert systems using edge AI, camera traps, acoustic sensors, and IoT networks in remote field conditions. Participants learn to detect poaching, habitat intrusion, and animal distress events as they occur, turning raw sensor data into actionable alerts for rangers and conservation teams.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A demonstrable AI Enablement project for your research or industry portfolio.
• 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
Module 1 – Camera Trap Detection Basics (Day 1)
Load and explore a small camera‑trap image set (human, animal, empty). • Apply a pre‑trained object detection model to tag images. • Analyse detection outputs and count key events.
Module 2 – Simple Alert Rules & Edge Constraints (Day 2)
Contrast edge versus cloud processing limits. • Build a rule‑based alert engine over detection results. • Log alerts with timestamp, location, type, and confidence.
Module 3 – System Design Canvas & Governance (Day 3)
Map actors, data flows, devices, and policies for a reserve. • Define alert routing channels (SMS, app, radio). • Incorporate ethics, privacy, and fatigue mitigation into the design.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | OpenCV |
| Covered Tool / Platform | Raspberry Pi |
| Covered Tool / Platform | LoRaWAN |
| Covered Tool / Platform | Jupyter Notebook |
Frequently Asked Questions
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