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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

★ 4.1 (32 ratings)25–27 Sep 20265:30 PM IST3 Days (60-90 Minutes)Certificate
The live sessions are over · get the full recording

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.
Next: Day-by-day →

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.

Next: Dates →

Important Dates

Registration Ends

September 25, 2026
IST 5:00 PM

Workshop Dates

September 25, 2026 – September 27, 2026
IST 5:30 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.
Next: Mentor →

Meet Your Mentor(s)

Mentor Photo

Ms Jaspreet Kaur

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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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 WhatsApp +91 99581 61117

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