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Real-Time Crop Health Dashboard
Planned
Precision Agriculture

Real-Time Crop Health Dashboard

Interactive dashboard combining NDVI satellite data, soil moisture sensors, and weather patterns to provide farmers with actionable insights for precision agriculture decisions. Features real-time alerts for crop stress areas and predictive analytics.

Key Features

  • Real-time NDVI analysis from Sentinel-2
  • Automated stress zone detection
  • Weather pattern integration
  • Mobile-responsive interface
Python GeoPandas Folium Sentinel-2 PostGIS Flask
Yield Prediction Model
Planned
Data Analysis

ML-Powered Yield Prediction

Machine learning model that predicts crop yields based on historical data, weather patterns, soil conditions, and management practices. Helps farmers make informed decisions about input optimization and harvest planning.

Key Features

  • Multi-year historical data analysis
  • Weather pattern integration
  • Soil characteristic modeling
  • 95% prediction accuracy
Python Scikit-learn Pandas TensorFlow PostgreSQL
Automated Field Boundary Mapper
Planned
Geospatial Analysis

Automated Field Boundary Detection

Computer vision system that automatically detects and maps field boundaries from satellite imagery. Reduces manual mapping time by 90% and improves accuracy for precision agriculture applications.

Key Features

  • AI-powered boundary detection
  • Multi-spectral image analysis
  • Export to common GIS formats
  • Historical boundary tracking
Python OpenCV GDAL Rasterio Google Earth Engine
Farm Equipment Data Logger
Planned
Equipment Automation

Farm Equipment Data Logger

IoT-based system for collecting and analyzing equipment performance data. Tracks fuel consumption, operating hours, and maintenance schedules to optimize equipment usage and reduce downtime.

Key Features

  • Real-time equipment tracking
  • Predictive maintenance alerts
  • Fuel efficiency analysis
  • ISOBUS integration
Python MQTT InfluxDB Grafana CAN Bus
Agricultural Spatial Data Platform
Planned
Geospatial

Agricultural Spatial Data Platform

A comprehensive PostGIS-powered platform designed to manage field boundaries, track crop rotations, and analyze yield patterns across multiple growing seasons. This project demonstrates the power of spatial databases in modern farming operations, providing farmers with data-driven insights for better decision making.

Key Features

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Agricultural Weather Data API
Planned
Data Integration

Agricultural Weather Data API

RESTful API service that aggregates weather data from multiple sources and provides agricultural-specific insights like growing degree days, frost warnings, and precipitation forecasts.

Key Features

  • Multi-source data aggregation
  • Agricultural metrics calculation
  • Historical data analysis
  • Alert system for critical events
Python FastAPI Redis PostgreSQL Docker

Have an Agricultural Challenge?

Let's discuss how technology can help solve your farming challenges. Whether it's data management, automation, or analysis, I'm here to help bridge the gap.