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AI for Crop Disease Detection using Hyperspectral and Drone Imaging

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Recordings + study material, certificate after completion, 1-year access. The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.

★ 4.1 (31 ratings)11–13 Jun 20265:30 IST3 Days (60-90 Minutes each day)Certificate
The live sessions are over · get the full recording

About This Course

AI for Crop Disease Detection using Hyperspectral and Drone Imaging is a 3-day hands-on workshop designed to introduce participants to the role of artificial intelligence in precision agriculture and crop health monitoring.

Participants will learn how crop disease symptoms, plant stress indicators, spectral signatures, vegetation indices, and drone-based imagery can be analyzed using AI and image processing techniques. The workshop covers the difference between RGB, multispectral, and hyperspectral imaging, along with practical workflows for preprocessing crop image datasets, extracting features, building basic deep learning models, and visualizing disease-prone or stress-affected crop zones.

Aim

The aim of this workshop is to help participants understand how AI, image processing, hyperspectral imaging, and drone imagery can be used for early crop disease detection, plant stress analysis, and precision agriculture decision-making.

Workshop Objectives

  • Introduce the role of AI in precision agriculture, crop health monitoring, and smart farming.
  • Explain common crop disease symptoms and plant stress indicators.
  • Build foundational understanding of RGB, multispectral, hyperspectral, and drone-based imaging.
  • Help participants understand spectral signatures of healthy and diseased crops.
  • Introduce vegetation indices for crop stress and disease pattern analysis.
  • Demonstrate crop disease image dataset exploration and preprocessing techniques.
  • Teach image enhancement, resizing, segmentation, and feature extraction using OpenCV.
  • Explain the use of machine learning and deep learning for crop disease classification.
Next: Day-by-day →

Workshop Structure

📅 Day 1: Foundations of Crop Disease Detection and Spectral Imaging

  • Introduction to AI in precision agriculture and crop health monitoring
  • Understanding crop disease symptoms and plant stress indicators
  • Basics of hyperspectral imaging and drone-based crop monitoring
  • Difference between RGB, multispectral, and hyperspectral imaging
  • Understanding spectral signatures of healthy and diseased crops
  • Introduction to vegetation indices for crop stress analysis
  • Role of AI in early disease detection, field monitoring, and smart farming decisions

🛠️ Hands-on:

  • Hands-on 1: Crop Disease Image Dataset Exploration and Preprocessing
  • Hands-on 2: Spectral Signature and Vegetation Index Calculation

📅 Day 2: Image Processing and AI-Based Disease Classification

  • Understanding image preprocessing for crop disease detection
  • Working with drone images and hyperspectral image data
  • Image enhancement, resizing, segmentation, and feature extraction
  • Using vegetation indices for disease and stress pattern identification
  • Introduction to machine learning and deep learning for crop image classification
  • Building a basic CNN model for plant disease detection
  • Evaluating model performance using accuracy, confusion matrix, and classification reports

🛠️ Hands-on:

  • Hands-on 1: Crop Image Preprocessing and Feature Extraction using OpenCV
  • Hands-on 2: Disease Classification Model using TensorFlow / Keras

📅 Day 3: Hyperspectral and Drone Image Analytics for Smart Agriculture

  • Understanding geospatial crop monitoring using drone imagery
  • Working with raster data for agricultural field analysis
  • Using Rasterio for reading and processing crop imagery
  • Mapping crop stress zones and disease-prone areas
  • Combining spectral features, vegetation indices, and AI classification results
  • Visualizing disease detection outputs for field-level decision-making
  • Preparing a final AI-based crop disease detection workflow for research and practical use

🛠️ Hands-on:

  • Hands-on 1: Drone / Raster Image Processing using Rasterio
  • Hands-on 2: Mini Project: AI-Based Crop Disease Detection and Stress Mapping Workflow

🧰 Tools Covered: Python, Rasterio, OpenCV, TensorFlow / Keras, Google Colab

Who Should Enrol?

  • Students and postgraduate learners in agriculture, biotechnology, environmental science, data science, computer science, and engineering.
  • PhD scholars and researchers working in precision agriculture, plant sciences, remote sensing, AI, and crop monitoring.
  • Academicians and faculty members interested in AI applications in agriculture and smart farming.
  • Agriculture professionals, agritech enthusiasts, and crop monitoring specialists.
  • Data science and AI learners interested in real-world image analytics applications.
  • Remote sensing, GIS, and drone imaging learners interested in agricultural use cases.
  • Industry professionals working in agritech, digital agriculture, crop analytics, and sustainability.
Next: Dates →

Important Dates

Registration Ends

June 11, 2026
IST 4:30 PM IST

Workshop Dates

June 11, 2026 – June 13, 2026
IST 5:30 IST

Workshop Outcomes

  • Explain how AI supports crop disease detection and precision agriculture.
  • Differentiate between RGB, multispectral, and hyperspectral imaging.
  • Identify the importance of spectral signatures in detecting healthy and diseased crops.
  • Calculate and interpret basic vegetation indices for crop stress analysis.
  • Explore and preprocess crop disease image datasets using Python and Google Colab.
  • Apply basic image processing techniques such as enhancement, resizing, segmentation, and feature extraction.
  • Build a basic AI/deep learning model for plant disease classification.
  • Evaluate disease classification model performance using accuracy, confusion matrix, and classification reports.
Next: Fees →

Recording fee

Participants in India

₹2,499

Participants outside India

$50

  • All session recordings
  • Study material
  • Certificate after completion
  • 1-year access

Get the recording

The live sessions of this workshop are over, so the live fees no longer apply. The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.

Next: FAQ →

Frequently asked questions

Will I get the recording?

Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).

What if I miss a live session?

Watch the recording in your dashboard and send your questions to the mentor through the support desk.

Do I get a certificate?

Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.

Which fee category applies to me?

Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.

How can I pay?

By card, UPI or PayPal on the payment page; international bank transfer is also available there.

What is the refund policy?

See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy

Need Help?

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