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

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.

• 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).

📅 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)”

• 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.