Weather-Aware Smart Irrigation Scheduling: From Rule Engines to Explainable ML

About This Course

This three-week, hands-on course guides participants through building a practical irrigation decision-support tool—from scratch and with real-world constraints in mind. You’ll start by creating a clean farm dataset, design a simple rule-based irrigation scheduler, integrate weather forecast inputs, and optionally add a basic machine learning model to refine water-need estimates.

The focus is on farmer-ready outputs, not complex theory. By the end of the course, you’ll be able to generate clear, actionable irrigation recommendations (e.g., how many millimeters of water to apply and what that means in pump hours), while understanding when rules or ML can fail in real field conditions.

Aim

To help participants build a practical, farmer-ready irrigation decision system—starting with rule-based scheduling using field and weather data, then integrating forecasts, and finally adding a basic ML model to improve irrigation recommendations and water-use efficiency.

Course Objectives

By the end of this course, participants will be able to:

• Understand how over- and under-irrigation affect yield, water use, and cost

• Build a clean irrigation dataset using rainfall, ET₀, soil moisture, and crop stage

• Design and implement a simple rule-based irrigation engine (Excel or Python)

• Integrate weather forecast data to adjust irrigation decisions

• Train a basic ML regression model to estimate crop water requirements

• Compare rule-based vs ML-based recommendations and identify failure cases

• Generate clear, farmer-friendly irrigation outputs (mm of water and pump hours)

Course Structure

Module 1: Data and Rule-Based Irrigation Scheduling

• Irrigation context: yield loss, water waste, and cost from poor scheduling

• Dataset creation in Excel/Colab

  • Columns: date, rainfall, ET₀, soil moisture, crop stage

• Rule design for irrigation decisions

  • Example: If soil moisture < X and no rainfall expected → irrigate Y mm

  • Crop-stage-based adjustments (e.g., flowering, fruiting)

• Implementation using Excel formulas or Python if–else logic

• Discussion: where simple rules fail (sensor errors, soil variability, heat spikes, surprise rainfall)

Module 2: Integrating Weather Forecast Data

• Weather forecast basics (high-level): what data is available and why it matters

• Hands-on:

  • Option A: Load a provided forecast CSV

  • Option B: Make a simple API call in Colab (JSON → DataFrame)

• Integrating forecast data into irrigation rules

  • Using predicted rainfall for the next 2–3 days

  • Delaying or reducing irrigation when rain is likely

Module 3: Basic ML Add-On and Farmer-Facing Outputs

• ML concept: estimating water requirement as a regression problem

• Hands-on: Train a simple regression model using historical data

  • Features: ET₀, temperature, crop stage, soil moisture (optional rainfall)

• Compare ML-based recommendations with rule-based outputs

• Designing farmer-friendly outputs

  • Translating numbers into plain language

  • Example: “Irrigate today: 30 mm (≈ X hours with your pump)”

Who Should Enrol?

This course is ideal for:

• Students, researchers, and professionals in Agriculture, Environmental Science, Agronomy, or Water Management

• Data science and AI learners interested in agritech and decision-support tools

• Extension professionals and sustainability practitioners working with farmers

• Anyone interested in practical irrigation scheduling using data and simple AI

No advanced coding is required—Excel-first workflows and guided Colab notebooks are provided.