AgriSight is one of my most ambitious projects, designed to bridge the gap between space technology and food security. It’s a comprehensive platform that monitors agricultural health in conflict-affected regions using Sentinel-2 satellite data and Machine Learning.

🏗️ Architecture: Microservices & Cloud-Native

The system is built on a robust microservices architecture to ensure scalability and reliability:

• Frontend: React 19 + Vite + Tailwind CSS for a high-performance, responsive UI.
• Backend API: Django REST Framework serving as the central orchestration layer.
• Geospatial Engine: PostGIS enabled PostgreSQL for complex spatial queries.
• Satellite Processing: A dedicated service integrating with Sentinel Hub API and Google Earth Engine to fetch and process multi-spectral imagery.
• Async Workers: Celery + Redis for handling heavy image processing tasks and vegetation index (NDVI, EVI) calculations.
• ML Layer: Custom models trained to detect stress patterns and correlate them with local conflict events.

🛰️ Core Features

• Real-time Monitoring: Automated ingestion of satellite tiles every time a new pass is available.
• Vegetation Indices: On-the-fly calculation of NDVI (Normalized Difference Vegetation Index) to assess plant health.
• Conflict Correlation: Overlaying ACLED data to visualize the impact of instability on agricultural productivity.
• Automated Alerts: Location-based notifications sent via WebSockets when significant stress is detected.

AgriSight represents the future of ICT4D, providing data-driven insights to humanitarian organizations and policy makers in regions where ground-level data is hard to come by.

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