Water Amount Prediction for Smart Irrigation Based on Machine Learning Techniques

Water is a critical resource that needs to be perfectly managed in the agriculture field to achieve high crop production with minimum water usage and without wastage. In this paper, we proposed a smart irrigation solution using different Machine Learning (ML) models to predict the daily irrigation water amount for the cucumber crop. The various used ML takes the plant’s environmental conditions parameters as input to predict the suitable amount of water as output. The results showed that the Support Vector Regression was the best model that gave the highest coefficient of determination ( \(R^2\) score \(\,\approx \,60\) %) with the smallest Mean Squared Error value (0.28).