Sri Grow

A full-stack agricultural advisory web app targeting rural agricultural officers in Sri Lanka, supporting crop planning, pest alerts, and market price tracking. I trained a Random Forest Regressor on a custom Sri Lanka weather dataset to predict maximum precipitation by city and date range, enabling data-driven crop scheduling. Served the ML model as a Python Flask microservice alongside a Node.js/Express backend and React frontend — a 3-tier architecture with a separate ML inference layer. Features include live weather forecasting, crop-specific soil and weather guidance, real-time pest alerts, and current market prices sourced from structured datasets.