Salahaldeen Duraibi

With the increasing worldwide population and the requirement for efficient approaches to farm care and irrigation, the demand for water is constantly rising, and water resources are becoming scarce. This has led to the development of smart water management systems that aim to improve the efficiency of water management. This paper pioneers an effective Irrigation Water Quality Prediction (IWQP) model using a convolution neural architecture that can be trained on any general computing device. The developed IWQP4Net is assessed using several evaluation measurements and compared to the Logistic Regression (LR), Support Vector regression (SVR), and k-Nearest Neighbor (kNN) models. The results show that the developed IWQP4Net achieved a promising outcome and better performance than the other comparative models.