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 Crop Disease Detection using Hyperspectral and Drone Imaging shows how machine learning turns advanced imaging into early warning for plant health. You learn how hyperspectral sensors reveal disease signatures invisible to the eye, how drones capture field-scale imagery, and how to build models that detect, classify and map crop disease from that data. The course covers the imaging and data-processing pipeline and connects detection to real decisions in precision agriculture. You finish able to reason about an AI crop-disease-detection system. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to crop disease detection — using hyperspectral and drone imagery with machine learning to spot plant disease early, before it spreads.
📋 Course Objectives
1. Explain hyperspectral disease signatures.
2. Capture and process drone field imagery.
3. Build disease-detection and classification models.
4. Map disease spread across fields.
5. Connect detection to precision-farming action.
👥 Who Should Enroll?
• Agri-tech and remote-sensing professionals
• Plant-pathology and crop scientists
• Data scientists in agriculture
• Students of agricultural technology
🚀 Key Learning Outcomes
• The ability to build crop-disease detection.
• A hyperspectral/drone imaging project.
• A precision-agriculture perspective.
• 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 Crop Disease Detection & Spectral Imaging
Introduce AI concepts for precision agriculture • Explore crop disease symptoms and stress indicators • Compare RGB, multispectral, and hyperspectral imaging • Calculate spectral signatures and vegetation indices
Day 2 – Image Processing & AI‑Based Disease Classification
Preprocess drone and hyperspectral images with OpenCV • Enhance, resize, segment, and extract features • Apply vegetation indices for pattern identification • Build a basic CNN model using TensorFlow/Keras
Day 3 – Hyperspectral & Drone Analytics for Smart Farming
Process raster data with Rasterio for field‑scale analysis • Map stress zones and disease‑prone areas • Integrate spectral features, indices, and AI predictions • Visualize outputs for decision‑making
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | OpenCV |
| Covered Tool / Platform | Rasterio |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Google Colab |
Frequently Asked Questions
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