#Accurate deep learning model forecasts water and energy demands.

New deep learning model predicts water and energy demands in agriculture with great accuracy

Researchers from the University of Córdoba are utilizing data science and Artificial Intelligence to develop a precision irrigation model to address water scarcity and high energy costs in irrigation communities. The HOPE project focuses on using AI to guide decision-making for efficient water management in agriculture. The latest model can predict irrigation water demand with less than 2% error, enabling effective resource management without compromising user autonomy.

The researchers have applied the Transformer Deep Learning architecture to precision irrigation, allowing for the processing of large amounts of data simultaneously. By validating the model with data from the Zujar Canal’s Community of Irrigators, they have reduced the margin of error from 20% to 2%. This accurate forecasting of irrigation water demand can support decision-making in contexts of water scarcity and high energy prices, promoting sustainable resource management.

The study is published in the journal Computers and Electronics in Agriculture, showcasing the potential of AI and data science in revolutionizing precision agriculture and water management. The research team’s innovative approach represents a significant advancement in digitization applied to irrigation, offering a practical solution to the challenges faced by irrigation communities.

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