Name: AI for Crop Disease Detection using Hyperspectral and Drone Imaging
Start: 2026-06-11
End: 2026-06-13

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About Workshop

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.

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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.

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What Participants Will Learn

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.

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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

Important Dates

Registration Ends

4:30 PM IST

Workshop Dates

2026-06-11

5:30 IST

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What You Will Gain

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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.

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Who Should Attend

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.