About Workshop
The workshop is designed to provide participants with an in-depth understanding of the integration of Artificial Intelligence and Hyperspectral Imaging for crop disease detection and precision agriculture. With increasing demand for sustainable and technology-driven farming practices, the ability to identify plant diseases at an early stage has become critical for improving crop yield and reducing economic losses. This workshop will explore the scientific foundations of hyperspectral sensing, the role of spectral information in detecting subtle plant stress signals, and the use of AI-based models for accurate disease recognition and classification. It will further examine practical workflows, real-world applications, and current research trends in intelligent crop health monitoring. By combining conceptual learning with applied hands-on sessions, the workshop aims to equip researchers, academicians, and industry professionals with the knowledge and skills required to leverage advanced imaging and AI tools for smarter agricultural solutions.
Aim
To equip participants with knowledge of AI-driven hyperspectral imaging techniques for the timely identification and analysis of crop diseases, thereby enhancing decision-making, crop health monitoring, and agricultural productivity.
What Participants Will Learn
Structure
Day 1 â Foundations of Hyperspectral Imaging for Crop Health
- Importance of early crop disease detection
- Basics of hyperspectral imaging and spectral signatures
- Healthy vs diseased crop reflectance characteristics
- Data acquisition and preprocessing techniques
- Identification of disease-sensitive spectral bands
Hands-on
Hyperspectral Data Exploration in Google Colab / Jupyter Notebook Participants will visualize spectral signatures, compare healthy and diseased samples, and perform basic preprocessing and PCA-based analysis.Day 2 â AI Models for Crop Disease Detection
- AI workflow for hyperspectral disease analysis
- Feature extraction and band selection
- Machine learning models for classification
- Introduction to deep learning for hyperspectral data
- Model evaluation using key performance metrics
Hands-on
Crop Disease Classification using AI Models Participants will build and evaluate a basic machine learning and deep learning pipeline for crop disease detection using hyperspectral data.Day 3 â Advanced Applications and Deployment
- Explainable AI for hyperspectral disease detection
- Disease severity assessment and mapping
- UAV and field-scale monitoring applications
- Real-world deployment challenges in precision agriculture
- Emerging trends in intelligent crop health monitoring
Hands-on
Explainable AI and Disease Mapping Notebook Participants will interpret model predictions, identify important spectral features, and generate a simple disease probability or severity mapping workflow.Important Dates
Registration Ends
4:00 PM IST
Workshop Dates
2026-05-13
05:30PM IST
05:30PM IST
What You Will Gain
Outcomes
- Understand the fundamentals of AI and hyperspectral imaging in agriculture.
- Gain knowledge of early crop disease detection techniques.
- Learn how AI can improve the accuracy and efficiency of disease diagnosis.
- Develop awareness of data-driven crop health monitoring methods.
- Recognize the value of precision agriculture for sustainable farming.
Who Should Attend
- Researchers in agriculture, remote sensing, and AI
- Academicians, faculty members, and scholars
- Agritech and precision agriculture professionals
- Data scientists and ML practitioners in agriculture
- UAV and remote sensing practitioners
- R&D professionals and innovation teams
- Anyone interested in AI-based crop disease detection using hyperspectral imaging