ALGX
Precision agriculture data platform connecting satellite imagery, IoT sensor networks, and ML-driven crop analytics.
The Challenge
Data Scale
Processing 50TB+ of satellite and sensor data daily with sub-second query response times across global farm operations.
User Diversity
Designing for agronomists, farm managers, and data scientists with fundamentally different technical backgrounds and workflow needs.
Real-Time Requirements
Delivering live IoT sensor feeds, weather alerts, and anomaly detection with less than 200ms latency to field devices.
The Approach
Data Architecture
Designed a multi-tier data pipeline using Apache Kafka for stream processing, TimescaleDB for time-series sensor data, and PostGIS for geospatial queries. The architecture handles burst ingestion of 100K+ events per second.
Interface Design
Created a map-centric interface with progressive disclosure. Simple overview dashboards that drill down into granular field-level analytics. Every visualization was tested with real agronomists.
ML Integration
Built custom crop health prediction models using TensorFlow, trained on 5 years of historical NDVI data. The system now predicts yield anomalies 3 weeks before visible symptoms.
Performance
Implemented edge computing on farm gateways for local processing, reducing cloud dependency by 60% and enabling offline-first capability for remote operations.
The Impact
Technology Stack
JU. transformed our fragmented data systems into a unified platform that our entire team actually wants to use. The precision of the engineering and the elegance of the interface exceeded every expectation.