Predicting Irrigation Requirements

Agriculture consumes over 70% of global freshwater, requiring precise irrigation based on complex crop evapotranspiration (ETo) and soil moisture forecasts. Classical models are often computationally inefficient.

Hybrid quantum-classical deep learning models (such as Quantum Long Short-Term Memory) could enhance feature learning and predictive accuracy for key water variables.

This supports advanced, efficient, and data-driven irrigation scheduling, critical for optimising freshwater resources in agriculture and ensuring food security