Description
The Problem
When extreme weather events like late frosts hit Mediterranean olive or almond harvests, agricultural commodity markets react with severe price volatility and supply chain disruption. Official government damage assessments typically take several weeks. During this critical window, procurement teams, commodity traders, and food processors lack objective, reliable data to reroute sourcing and mitigate financial losses.
The Solution
OrchardCast is a high-resolution yield-forecasting system. By combining open Copernicus satellite data (Sentinel) with meteorological telemetry, it quantifies regional crop losses at the comarca (district) level within 72 hours of an event. The underlying system architecture and data backend—integrating process-based crop growth modeling, particle filters, and data APIs—are designed and functionally deployed.
Current Project Status
Beyond the core system architecture and technical roadmap, an initial business plan—covering pricing tiers, target personas, and a risk matrix—is already prepared. With this groundwork and well-defined work packages in place, we can quickly align on Friday evening and hit the ground running immediately.
Hackathon Goal
The objective for the weekend is to turn backend model outputs into a concrete, market-ready deliverable:
- Data Visualization & Reporting: Build an automated pipeline that ingests raw model CSV outputs and generates polished, executive-ready PDF damage reports (featuring GIS map plots, quantile distribution curves, and regional data tables).
- Business Case & Commercial Strategy: Translate the technical solution into a sharp B2B pitch demonstrating tangible ROI for supply chain risk management and food system resilience.