Training Course on GIS for Smart Agriculture - GIS Software

Module 1: Introduction to GIS in Agriculture

Theory:

Role of GIS in smart and precision agriculture.

Overview of agricultural datasets and spatial analysis applications.

GIS tools for crop monitoring, irrigation planning, and yield analysis.

Applications: Farm management, crop monitoring, resource optimization.

Hands-on: Explore sample agricultural datasets and visualize crop distribution.

Case Study: Mapping agricultural land use for precision farming.

Module 2: Geospatial Data Acquisition and Management

Theory:

Sources of agricultural data: satellite imagery, drones, GPS, IoT sensors, field surveys.

Data formats, projections, and GIS database management.

Ensuring data quality, consistency, and accuracy.

Hands-on: Import, clean, and organize soil, crop, and irrigation datasets.

Case Study: Integrating multi-source data for farm-level GIS analysis.

Module 3: Remote Sensing for Crop Monitoring

Theory:

Remote sensing basics for agriculture.

Vegetation indices (NDVI, EVI, SAVI) and their applications.

Temporal analysis for crop growth and health assessment.

Hands-on: Compute vegetation indices and generate crop health maps.

Case Study: Monitoring seasonal crop growth using Sentinel-2 imagery.

Module 4: Soil and Water Resources Analysis

Theory:

Mapping soil types, fertility, and moisture levels.

Terrain analysis for irrigation planning and erosion prevention.

GIS modeling for optimal water allocation.

Hands-on: Create soil and water suitability maps for specific crops.

Case Study: Designing an irrigation plan based on spatial analysis of soil and terrain.

Module 5: Spatial Modeling for Crop Management

Theory:

Crop suitability analysis and land evaluation using GIS.

Pest and disease risk modeling.

Fertilizer and input application optimization.

Hands-on: Generate crop suitability maps and risk zones for pest outbreaks.

Case Study: Identifying high-yield zones and risk-prone areas in farmland.

Module 6: IoT and Real-Time Data Integration

Theory:

Use of IoT sensors in agriculture: soil moisture, temperature, weather stations.

Integrating real-time sensor data with GIS platforms.

Automated alerts and decision-making using IoT-GIS systems.

Hands-on: Connect simulated IoT sensor data to GIS for farm monitoring.

Case Study: Smart irrigation system based on soil moisture sensor data.

Module 7: Crop Yield Prediction and Forecasting

Theory:

GIS and machine learning for crop yield modeling.

Integration of remote sensing, climate, and soil data for prediction.

Temporal analysis for seasonal forecasting.

Hands-on: Build a crop yield prediction model using historical data.

Case Study: Predicting maize yield using NDVI and soil data.

Module 8: Precision Agriculture Planning

Theory:

GIS-based farm management and resource allocation.

Variable rate technology for inputs (fertilizers, water).

Decision support systems for precision farming.

Hands-on: Generate input application maps based on spatial analysis.

Case Study: Optimizing fertilizer application using GIS and soil data.

Module 9: Visualization, Mapping, and Reporting

Theory:

Creating thematic maps, dashboards, and interactive visualizations.

Reporting tools for farm managers and stakeholders.

Integrating GIS outputs into mobile or web-based platforms.

Hands-on: Develop an interactive dashboard for monitoring farm performance.

Case Study: Visualizing crop health, irrigation, and yield forecasts in a unified platform.

Module 10: Capstone Project

Project Examples:

GIS-based crop monitoring and yield prediction for a farm or region.

Smart irrigation planning using soil, terrain, and IoT sensor data.

Integrated farm management system combining crop health, input optimization, and monitoring dashboards.

Hands-on: Complete end-to-end GIS workflow: data collection → analysis → modeling → visualization → reporting.

Presentation: Showcase project outcomes, insights, and recommendations for smart agriculture applications.