Machine learning techniques for reference evapotranspiration and rice irrigation requirements prediction: a case study of Kerian irrigation scheme, Malaysia

Key Points

• Irrigation requirements for rice crops are effectively predicted using machine learning techniques, enhancing water management.
• Evapotranspiration rates were accurately modeled, improving our understanding of crop water needs in the Kerian irrigation scheme.
• The study applies advanced machine learning methods to analyze large datasets from the region, providing actionable insights.
• The findings support optimized irrigation practices, highlighting the importance of technological integration in agriculture.