A 3-day hands-on workshop to build an irrigation decision tool: create a farm dataset, apply simple rule-based scheduling, add weather-forecast inputs, and optionally train a basic ML model—ending with clear, farmer-friendly irrigation recommendations.
Aim
To help participants build a practical, farmer-ready irrigation decision system—starting with simple rule-based scheduling using field and weather data, then integrating forecast data, and finally adding a basic ML model to improve water-need estimates and generate clear irrigation recommendations.
What Participants Will Learn
Understand the impact of over- and under-irrigation on yield, water use, and cost.
Build a clean irrigation dataset (rainfall, ET₀, soil moisture, crop stage) in Excel/Colab.
Design and implement a simple rule-based irrigation engine (Excel formulas / Python if-else).
Integrate weather forecast data (CSV/API) to adjust irrigation based on expected rainfall.
Develop a basic ML regression model to estimate water requirements from historical data.
Compare rule-based vs ML-based recommendations and identify when each can fail.
Generate clear, farmer-friendly outputs (e.g., mm of water and equivalent pump hours).
Structure
📅 Day 1 — Data + Simple Rules (Rule Engine First)
Context: over-irrigation vs under-irrigation (yield, water, cost)
