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, IoT and Remote Sensing for Precision Agriculture is a 3‑day hands‑on course designed to introduce participants to the practical use of smart technologies in modern farming and agricultural monitoring. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
AI, IoT and Remote Sensing for Precision Agriculture is a 3‑day hands‑on course designed to introduce participants to the practical use of smart technologies in modern farming and agricultural monitoring.
📋 Course Objectives
1. Apply AI in Industry & Manufacturing methods to authentic research and industry problems.
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 Industry & Manufacturing
• R&D engineers and working professionals applying AI in Industry & Manufacturing in industry
• Academics and educators building research or teaching capacity in AI in Industry & Manufacturing
🚀 Key Learning Outcomes
• A demonstrable AI in Industry & Manufacturing 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
Day 1 – Foundations of AI, IoT & Remote Sensing
Explore precision agriculture concepts and smart‑farm ecosystems • Identify field‑level data types: soil moisture, temperature, humidity, crop health, vegetation status • Set up virtual IoT sensors and map smart‑farm data flows • Analyse satellite and drone imagery basics
Day 2 – IoT‑Based Farm Monitoring & Irrigation Intelligence
Build IoT architecture for smart agriculture applications • Create virtual sensors for soil moisture, temperature, humidity and crop environment • Implement MQTT communication for real‑time data transfer • Publish/subscribe sensor streams using HiveMQ
Day 3 – Remote Sensing, Yield Estimation & Decision Dashboards
Explore satellite remote sensing for agricultural monitoring • Use Sentinel Hub for crop health and vegetation analysis • Interpret vegetation indices (NDVI, etc.) for precision farming • Visualise spatial farm data with QGIS
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Wokwi |
| Covered Tool / Platform | MQTT |
| Covered Tool / Platform | HiveMQ |
| Covered Tool / Platform | Google Sheets |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Sentinel Hub |
| Covered Tool / Platform | QGIS |
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