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).

• 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.