## 1 Introduction  
The world's population living in urban areas will increase by 13% by 2050 [1]. On the other hand, with the current agricultural production method, the capacity of the Earth is already exceeded [2]. Therefore, improving agricultural production and feeding the world without exceeding natural sources such as water remains the main problem in agriculture. Agricultural production depends on water. It is also the largest consumer of water as it consumes on average 70% of all freshwater [3]. Therefore, it is essential to optimize irrigation and make irrigation systems more efficient, especially in arid regions. Many factors need to be considered when scheduling irrigation. The amount of water available in the soil and reference evapotranspiration (ETo) are two critical factors [4, 5]. Evapotranspiration is the amount of water that evaporates from the Earth and plant surface. Solar radiation, wind speed, temperature, and relative humidity all influence daily ET, and this effect is highly nonlinear. The Penman-Monteith equation recommended by the Food and Agriculture Organization (FAO) is the most commonly used equation for calculating ET [6]. Soil Water Content (SWC) is the volume of water per unit volume of soil. It can be measured by weighing a soil sample, drying the sample to remove the water, and then weighing the dried soil or by remote sensing methods. Measuring SWC is important for studying the statistics of available water and scheduling irrigation [5]. ET and SWC can be measured by remote sensing methods. [PERSON] et al. [7] lists some advantages and disadvantages of some remote sensing methods, for example, [PERSON] et al. [8] used Eddy Covariance (EC) Systems in South Africa to determine evapotranspiration in a forested area. The system EC uses sensors (such as an anemometer, Infrared Sensor, quality air sensor, precision flowmeter, or vapor flow meter) to collect data from the field and calculate evapotranspiration. The study points out some disadvantages of using this system in remote areas, such as the relatively high power requirement, which requires careful and frequent attention, and the verification, correction, and analysis of the data for complete records. However, the machine learning algorithm is able to extract the feature from the raw data and calculate ET and SWC without any further effort. In recent years, many studies have been conducted on the use of machine learning methods for irrigation scheduling. Support vector machine, single-layer neural network, and deep neural network are the most commonly used methods for simulating soil moisture, and ET [7; 9; 10; 11; 12; 13]. [PERSON] [5] used machine learning techniques including three support vector regressions (Radial Basis Function (RBF), linear and polynomial kernel SVM), a single-layer Artificial Neural Network (ANN), and eight multilayer Deep Neural Networks (DNN) to simulate soil water content. The results showed that the RBF-based SVR outperformed the other models to simulate soil water content. [PERSON] et al. [14] used CNN to predict soil moisture status from images. The experimental result on the test images showed that CNN performed best with a normalized mean absolute error of 3.4% compared to SVM, RF, and two-layer Neural Networks. [PERSON] et al. [15] used a Macroscopic Cellular Automata (MCA) model by combining the deep belief network (DBN) to predict the SWC. Compared to the multilayer perceptron, the DBN-MCA model resulted in an 18% reduction in mean squared error. [PERSON] [16] developed a generalized wavelet neural network (WNN)-based model to estimate the reference evapotranspiration. The proposed models were compared with ANN, linear regression (LR), wavelet regression (WR), and HG methods. The WNN and ANN models performed better compared to the LR, WR, and HG methods. [PERSON] and [PERSON] [17] used a Deep Learning-Multilayer Perceptron (MLP) to determine the daily ETo for Hoshiarpur and Patiala Districts from Punjab.