## 2 Review of Literature
The data used in this study were extracted from MODIS images of the Terra satellite and SM data obtained from ESA, CCI, which offer comprehensive global information with a resolution of 0.25\({}^{\circ}\) from active and passive microwave sensors. For modeling, these researchers defined 13 time periods consisting of 5 months in which the crop is grown and harvested. [PERSON] and [PERSON] [18] used RF to predict the rice yield in Andhra Pradesh, India. They used three types of data including temperature, precipitation, and perception data to estimate the rice yield in the studied area. The accuracy of their predictions with RF was 85.89%. [PERSON] [23] used two methods, SVR and MLR, to predict barley yield in three Iranian provinces with arid, semi-arid, and humid climates. The results of this study showed that SVR is a better method for making a prediction for the considered 36-year period. In a study by [PERSON] and [PERSON] [24], SVR and DL were used to predict corn yield in Illinois, USA. In this study, DL was said to be preferred because of its advantage in the automatic selection of important features and also its greater flexibility in dealing with predefined features. [PERSON] and [PERSON] [25] conducted a similar study with BPNN and compared their training times and errors. These researchers used precipitation data and growth indices for yield prediction. [PERSON] et al. [22] used 8 machine learning algorithms to predict the yield of winter wheat in 629 districts of a province of China. For this purpose, they defined multiple time windows and introduced an integrated framework of weather, remote sensing, and soil data for yield prediction based on the Google Earth Engine. These researchers used three groups of weather data and remote sensing data and soil data for their predictions. In a study by [PERSON] [26], which aimed to predict barley yield in Boshruyeh, Iran, this objective was pursued in three stages: (1) building a framework for predicting barley yield based on five-year weather data such as temperature and precipitation and remote sensing data such as NDVI And EVI for 24 districts; (2) identifying the best machine learning method for predicting barley yield by comparing four methods, namely GPR, KNN, DT, BPNN, which GPR to be the best method; and (3) identifying the best time for predicting crop yield before harvest. Tables 1 and 2 compares the past studies on the prediction of crop yield with machine learning methods. [PERSON] [23] investigated prediction of barley yield in Iran, which is one of the rainfed crops that can be grown in the region, NDVI, EVI, temperature and evaporation indicators were considered. Although, other factors such as soil moisture and rainfall can be considered in the growth of rain-fed barley. The irrigation method based on ground water, which means that farmers use water pumps to pump water was also investigated. Studies such as [13; 17; 20] have considered crops that are grown by irrigation. There have been other studies related to crops that are mainly located in areas where there is a lot of rain, such [10; 14; 15; 16; 18; 21]. [PERSON] et al. [22] studies winter wheat, criteria such as air pressure that have slight changes in the growing season and can be considered almost constant. In irrigation agriculture, due to the fact that water resources are available and there is no vital need for rainfall, indicators such as rainfall can be ignored, such as [10]. In another study conducted in Iran for barley, rainfall index, temperature, and speed wind were considered as indicators affecting plant growth. It was found that wind speed index except in stormy areas, has no effect on forecasting and can be ignored. SVR has been widely used in related studies such as [10; 13; 14; 15; 17; 19; 20; 22]. The SVR that is an optimized model of the SVM method, can be used to predict problems with continuous data like what we use in this study.