New Year Offer End Date: 30th April 2024
Program

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

Skills you will gain:

About Program:

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.

Program 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.

What you will learn?

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

Mentor Profile

Fee Plan

INR 1999 /- OR USD 50

Get an e-Certificate of Participation!

Intended For :

  • 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.

Career Supporting Skills

Program 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.

FREEDOM TO LEARN • 10% OFF All Courses & Workshops • Use Code: NANOINDIA10 • ⏳ Offer Ends In: Loading... • Learn Today. Lead Tomorrow. • Explore Programs →
FREEDOM TO LEARN • 10% OFF All Courses & Workshops • Use Code: NANOINDIA10 • ⏳ Offer Ends In: Loading... • Learn Today. Lead Tomorrow. • Explore Programs →
Support