IWQP4Net: An Efficient Convolution Neural Network for Irrigation Water Quality Prediction

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