Farm Sensor and Satellite Data Analytics for Precision Agriculture

Added Jun 2025 3 design docs

Precision agriculture promises higher yields with fewer inputs, but it depends on infrastructure most farm operations lack: thousands of field sensors and regular satellite passes generate data that must be collected, processed, and made legible fast enough to influence this week's decisions rather than next season's. The intern builds that infrastructure as a cloud-based analytics platform. Distributed farm devices stream soil moisture, weather, and equipment readings into an AWS-hosted ingestion layer, with MongoDB storing device data and farm configurations. Spark processes the combined sensor and satellite datasets at scale, computing field-level indices, detecting crop stress patterns, and powering automated alerts when conditions cross grower-defined thresholds. A Python service layer exposes the analytics, and a React dashboard lets growers monitor real-time conditions, review trends field by field, and configure the alert rules that matter to their operation. The intern gains end-to-end big data experience anchored in a tangible domain: IoT ingestion design, distributed processing with Spark, cloud deployment on AWS, and the craft of translating raw telemetry into the daily decisions of precision farming.

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