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
Explore the integration of satellite imagery, deep learning, and advanced crop intelligence techniques to enable data‑driven precision farming, enhancing yield prediction, resource optimization, and sustainable agricultural practices. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Explore the integration of satellite imagery, deep learning, and advanced crop intelligence techniques to enable data‑driven precision farming, enhancing yield prediction, resource optimization, and sustainable agricultural practices.
📋 Course Objectives
1. Translate AI in Energy & Utilities theory into practical, reproducible analysis.
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 in Energy & Utilities
• R&D engineers and working professionals applying AI in Energy & Utilities in industry
• Academics and educators building research or teaching capacity in AI in Energy & Utilities
🚀 Key Learning Outcomes
• Tangible, reproducible AI in Energy & Utilities work to show supervisors or employers.
• 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
Day 1 – Introduction to AI in Precision Agriculture & Satellite Imagery
Understand precision agriculture concepts and global significance • Explore AI & deep learning roles in modern farming • Calculate NDVI and visualize crop health maps in Google Colab
Day 2 – Deep Learning for Crop Classification & Disease Detection
Design CNN models for satellite‑based crop type classification • Detect early disease symptoms and stress patterns • Train a simple CNN on sample imagery in Google Colab
Day 3 – Crop Intelligence, Yield Prediction & Future Trends
Integrate satellite data and AI for yield prediction models • Build regression models to forecast crop yield using historical indices • Explore emerging tools such as drones, IoT, and transformer models
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Rasterio |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
