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
In this intensive 3‑day program you will discover how AI powers crop health monitoring, yield forecasting, and smart farming decisions using real‑world, multi‑source agricultural data. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
In this intensive 3‑day program you will discover how AI powers crop health monitoring, yield forecasting, and smart farming decisions using real‑world, multi‑source agricultural data.
📋 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 – Monitoring Crop Health with AI & Remote Sensing
Explore crop intelligence and precision agriculture concepts • Process satellite imagery and NDVI indices using Python (NumPy, Pandas, Rasterio) • Detect stress signals and visualize health metrics
Module 2 – Predicting Crop Yields Using Machine Learning
Clean and engineer features from historic MDPI crop datasets • Build supervised models (Random Forest, XGBoost, LSTM) for yield prediction • Integrate climate and environmental variables and evaluate model performance
Module 3 – Data‑Driven Decision Support for Crop Productivity
Design AI‑driven irrigation, fertilization, and pest‑management strategies • Create visual dashboards with Matplotlib, Seaborn, and Plotly • Analyze trends and generate actionable agronomic recommendations
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | Rasterio |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | XGBoost |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | LSTM |
| Covered Tool / Platform | Matplotlib |
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