Smart Irrigation Cuts Costs and Water Use in Arid Regions

Researchers developed a cloud-based smart irrigation system integrating IoT sensors, big data analytics, and renewable energy to improve sustainable agriculture in arid regions like Rajasthan, India, the MENA region, and the Sahel. The system, SPINN.

WBO, forecasts water demand using a Statistical-Physics Informed Neural Network and optimizes distribution with a Wolf-Bird Optimizer. The team tested the model in MATLAB, utilizing the Pump Sensor Data Dataset to demonstrate its performance against conventional methods like artificial neural networks and support vector machines.

Results indicate SPINN. WBO reduces water consumption by 20 to 100 kiloliters over 30 days and lowers operational costs to $7,080. This technology supports UN Sustainable Development Goals related to zero hunger, clean water, affordable energy, and climate action, aiming to make irrigation more efficient and accessible.