QGIS-MCP: AI-Enhanced Agricultural GIS Platform: Professional agricultural monitoring and smart farming through natural language - optimized workflow-focused interface.
QGIS-MCP provides farmers, agricultural consultants, and GIS professionals with an intelligent interface to QGIS through conversational AI. Our consolidated workflow approach reduces complexity while delivering enterprise-grade crop monitoring, yield prediction, irrigation planning, and agricultural analysis capabilities. It provides a revolutionary bridge between conversational AI and professional GIS capabilities, enabling natural language control of sophisticated spatial analysis workflows enhanced with machine learning, computer vision, and intelligent decision support.
🌟 Key Features
🌱 Agricultural Workflow Integration (DEFAULT INTERFACE)
🌾 Crop Health Assessment**: Complete crop monitoring pipeline with stress detection
📈 Yield Prediction**: AI-powered yield forecasting using satellite data
💧 Irrigation Planning**: Smart water management based on vegetation stress
🐛 Pest Detection**: Early pest identification through spectral analysis
🌾 Harvest Readiness**: Optimal harvest timing determination
📊 Field Monitoring**: Continuous field monitoring and alerts
🔧 Core GIS Integration:
🔌 Direct QGIS Integration**: Control QGIS through MCP protocol
🛰️ Advanced Remote Sensing**: Calculate vegetation indices (NDVI, EVI, SAVI, TGI, VARI, ExG)
📊 Spatial Analysis**: Execute QGIS processing algorithms
🗂️ Project Management**: Load, save, and manage QGIS projects
📈 Data Visualization**: Render maps and create visualizations
🔧 Custom PyQGIS**: Execute arbitrary PyQGIS code for advanced operations
🔄 Workflow Templates**: Pre-built analysis pipelines (Agricultural Assessment, Forest Monitoring, Urban Growth)
☁️ Cloud Data Integration**: Automated satellite data access (Sentinel Hub, USGS Earth Explorer)
⚡ Batch Processing**: Parallel processing engine with intelligent task scheduling
📊 Performance Monitoring**: Comprehensive system health and operation tracking
⚙️ Advanced Configuration**: YAML-based config with environment variable overrides
🛡️ Enhanced Resilience**: Circuit breakers, automatic retry, and error recovery
🧠 Machine Learning Workflows**: Automated land cover classification, vegetation health prediction, spatial clustering
👁️ Computer Vision Analysis**: Vegetation health assessment, water body detection, urban feature analysis
🎯 Object Detection**: Building detection, vehicle detection, infrastructure analysis with confidence scoring
💡 Smart Recommendations**: AI-powered analysis suggestions, workflow optimization, next-step guidance
📈 Predictive Analytics**: Vegetation trend forecasting, crop yield prediction, climate analysis, anomaly detection
🔮 Intelligent Decision Support**: Context-aware recommendations based on data characteristics and analysis objectives
🎯 Use Cases
🌱 Complete Crop Health Assessment (Agricultural Workflows)
```
"Run crop health assessment workflow using satellite image data/sentinel2.tif and field boundaries data/fields.shp"
```
Uses the consolidated agricultural_workflows tool for complete pipeline analysis
📈 Smart Yield Prediction (Agricultural Workflows)
```
"Execute yield prediction workflow with satellite_image='landsat_june.tif' and historical_yields='harvest_data.csv'"
```
AI-powered yield forecasting with confidence intervals and trend analysis
💧 Irrigation Planning Workflow
```
"Run irrigation planning workflow using satellite_image='ndvi_current.tif' and soil_data='soil_moisture.shp'"
```
Smart water management based on vegetation stress mapping
📊 Quick NDVI Analysis (Remote Sensing)
```
"Calculate NDVI using nir_band_path='satellite.tif@8' and red_band_path='satellite.tif@4' and output_path='ndvi_result.tif'"
```
Direct access to common remote sensing operations
🌾 Field Monitoring Setup
```
"Set up field monitoring workflow with field_boundaries='farm_plots.shp' and monitoring_schedule='weekly'"
```
Continuous monitoring with automated alerts and reporting
👁️ Computer Vision Analysis (AI Integration)
```
"Analyze vegetation health in satellite image data/farm.tif using computer vision, detect stress patterns"
```
Advanced AI-powered image analysis with stress detection
🤖 Smart Agricultural Recommendations
```
"Get smart recommendations for my corn crop analysis using current satellite data and weather conditions"
```
Context-aware AI guidance for agricultural decision making
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