Due to the environmental changes and increasing global temperature and drought conditions, irrigation of plants along with theirs protection of growth as well as high yield is very important. Therefore, water consumption can be reduced significantly in agriculture by monitoring and control of plant growth conditions and increase the irrigation efficiency through the conversion of surface irrigation methods to smart irrigation systems in the situation of water crisis. In this study, to detect plant water requirement, a set of turf grass plants images were taken and studied under drought stress conditions to extract the color, texture and number of features in the frequency domain. Thereafter, all the extracted parameters of images were investigated, then, according to the results of statistical analysis (p<0.05), most appropriate features were selected to predict water content of plant by support vector regression algorithm (SVR). Finally, it was shown that the linear kernel function of SVR algorithm has the highest correlation coefficient (0.95) and the lowest values of MAPE (14.08), RMSE (0.10), SRE (0.063) and RAV (0.14) compared to other kernels. Thus, this indicated that the ability of suggested system to measure and evaluate wilting plant conditions and control of required water for plant.