Skills you will gain:
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.
Program Objectives:
Introduce AI and hyperspectral imaging in agriculture.
Explain hyperspectral data for early crop disease detection.
Demonstrate AI-based disease detection methods.
Build understanding of data-driven crop health monitoring.
Highlight the role of advanced imaging in precision agriculture.
Promote innovative technologies for sustainable productivity.
What you will learn?
Participants will learn:
- Single-cell RNA-seq and spatial transcriptomics fundamentals
- Omics use cases in drug discovery and precision medicine
- Data ingestion, QC, filtering, and normalization
- PCA, UMAP, t-SNE, clustering, and annotation
- Differential expression and pathway interpretation
- Cell-cell communication and tumor microenvironment analysis
- Spatial transcriptomics integration with single-cell datasets
- AI-assisted omics workflow support
- Capstone project development and presentation
Intended For :
- 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
Career Supporting Skills