AI-Powered Precision Agriculture: Remote Sensing, Satellite Data & Machine Learning for Crop Prediction
Hands-on Analysis Using Sentinel-2, Landsat, ERA5 Climate Data, NDVI & AI-Based Predictive Models
About This Course
Participants will learn how to work with Sentinel-2, Landsat, NDVI, and ERA5 climate datasets, process agricultural data, extract meaningful environmental features, and develop AI/ML workflows for crop analysis and prediction.
Through practical hands-on sessions, participants will gain experience in remote sensing analysis, climate data integration, and predictive modelling, enabling them to understand how AI can support sustainable farming, climate-resilient agriculture, and data-driven agricultural decision-making.
Aim
This workshop aims to provide participants with practical knowledge of AI-powered precision agriculture by integrating satellite remote sensing, climate data analytics, and machine learning techniques for crop monitoring, environmental assessment, and predictive agricultural modelling.
Workshop Objectives
- Understand the role of remote sensing and AI in climate-smart precision agriculture.
- Explore Sentinel-2, Landsat, MODIS, and ERA5/ERA5-Land datasets for agricultural applications.
- Generate and interpret NDVI-based crop-health and vegetation-stress maps.
- Extract and analyse temperature, rainfall, soil-moisture, and related climate variables.
- Integrate NDVI and ERA5 data for crop-stress and climate-response analysis.
- Prepare satellite and climate datasets for machine-learning applications.
- Develop crop-condition and yield-prediction models using Random Forest and XGBoost.
- Evaluate and interpret model performance using MAE, RMSE, R², and feature-importance techniques.
- Apply AI-generated insights to climate-smart agricultural decision support.
Workshop Structure
Day 1: Remote Sensing & Crop Monitoring
- Introduction to AI-Powered Precision Agriculture
- AI applications in smart farming and crop management
- Fundamentals of agricultural remote sensing
- Satellite datasets: Sentinel-2, Landsat & MODIS
- Spatial, temporal & spectral resolution concepts
- NDVI and vegetation indices for crop health analysis
Hands-on Session 1: Satellite-Based Crop Health Mapping
- Satellite image processing workflow
- NDVI generation and crop condition analysis
- Interpretation of vegetation health patterns
Tools Covered: Google Earth Engine | QGIS | Sentinel-2 | Landsat
Day 2: Climate Intelligence & Data Integration
- Role of climate analytics in agriculture
- ERA5 climate data and agricultural variables
- Temperature, rainfall, soil moisture & solar radiation analysis
- Integration of satellite and climate datasets
- Agricultural data preprocessing and feature extraction
Hands-on Session 2: Climate-Smart Agriculture Workflow
- Accessing and analyzing ERA5 datasets
- Combining NDVI with climate variables
- Preparing datasets for AI modelling
Tools Covered: ERA5 | Python | Google Colab
Day 3: Machine Learning for Crop Prediction
- AI/ML workflow for agricultural prediction
- Agricultural dataset preparation
- Feature engineering and selection
- Machine learning models:
- Random Forest
- XGBoost
- Regression Models
- Model evaluation and explainable AI for agriculture
Hands-on Session 3: Crop Prediction Model Development
- Building ML-based crop prediction workflows
- Model performance evaluation
- Generating AI-driven agricultural insights
Tools Covered: Python | Scikit-learn | XGBoost | Google Colab
Who Should Enrol?
- Undergraduate and postgraduate students
- PhD scholars and research scholars
- Faculty members and academicians
- Agricultural and environmental researchers
- Agronomy and crop-science professionals
- Remote-sensing and GIS professionals
- Data-science and AI enthusiasts
- Agri-tech and precision-agriculture professionals
- Climate and sustainability researchers
- Industry professionals working in agriculture, geospatial analytics or environmental monitoring
Prerequisite: Basic understanding of agriculture, environmental science, remote sensing, data analysis or Python is helpful but not mandatory.
Important Dates
Registration Ends
September 21, 2026
IST 4:30 PM
Workshop Dates
September 21, 2026 – September 23, 2026
IST 5:00 PM
Workshop Outcomes
- Access and process satellite and climate datasets for agricultural analysis.
- Create NDVI-based crop-health and vegetation-stress maps.
- Analyse ERA5 climate variables and identify climate anomalies affecting crops.
- Integrate remote-sensing and climate data into a unified analytical dataset.
- Identify relationships between vegetation response and climatic conditions.
- Build and evaluate AI models for crop-condition or yield forecasting.
- Interpret important climatic and vegetation predictors using explainable AI.
- Develop an end-to-end workflow from satellite observation to climate-smart agricultural decision support.
Fee Structure
Student
₹2499 | $65
Ph.D. Scholar / Researcher
₹3499 | $75
Academician / Faculty
₹4499 | $85
Industry Professional
₹5499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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