Weather-Aware Smart Irrigation Scheduling: From Rule Engines to Explainable ML
Smart Water, Simple Decisions: Your First AI Irrigation Tool
Skills you will gain:
About Program:
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.
Program Objectives:
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Understand the impact of over- and under-irrigation on yield, water use, and cost.
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Build a clean irrigation dataset (rainfall, ET₀, soil moisture, crop stage) in Excel/Colab.
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Design and implement a simple rule-based irrigation engine (Excel formulas / Python if-else).
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Integrate weather forecast data (CSV/API) to adjust irrigation based on expected rainfall.
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Develop a basic ML regression model to estimate water requirements from historical data.
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Compare rule-based vs ML-based recommendations and identify when each can fail.
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Generate clear, farmer-friendly outputs (e.g., mm of water and equivalent pump hours).
What you will learn?
📅 Day 1 — Data + Simple Rules (Rule Engine First)
- Context: over-irrigation vs under-irrigation (yield, water, cost)
- Hands-on: data template in Excel/Colab
- Columns: date, rainfall, ET₀ (given), soil moisture (given), crop stage
- Rule design: simple irrigation rules
- Example: If soil moisture < X and no rainfall forecast → irrigate Y mm
- Stage-based adjustments (flowering/fruiting, etc.)
- Implementation: Excel formulas or Python if/else
- Discussion: when rules fail (sensor errors, soil variability, heat spikes, unexpected rain)
📅 Day 2 — Weather Forecast Data (API or CSV)
- Weather forecast basics (high-level): what forecast data contains and why it matters
- Hands-on: get forecast data
- Option A: load provided forecast CSV
- Option B: simple API call in Colab (JSON → DataFrame)
- Integrate forecast into rules
- Include rainfall for next 3 days
- Delay/reduce irrigation if rain is predicted
📅 Day 3 — Basic ML Add-on + Farmer-Facing Output
- ML idea: water requirement estimation as a regression problem (using historical data)
- Hands-on: train a quick regression model
- Features: ET₀, temperature, crop stage, soil moisture (optional: rainfall)
- Compare ML vs rule-based recommendations
- Farmer output design
- Convert into plain language: “Irrigate today: 30 mm (~X hours with your pump)”
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Career Supporting Skills
Program Outcomes