## 2 Review of Literature  
Numerous studies have shown that massive spatio-temporal data obtained from the maps and satellite images produced by satellite spectroscopy can be combined with regional meteorological data to accurately predict crop yield on different scales using statistical models [11; 14; 15; 16; 17; 18]. The global problems during the recent decades have forced many researchers to pay more attention to clean energy sources for economic growth and environmental issues [19; 20; 21], therefore using rainfed agriculture would help to reduce pollutions. To predict crop yield, climate data such as temperature, precipitation, sunshine hours, humidity, etc. must be given to these models as input data. While climate data very well describe the environmental conditions that affect crop growth, they are not the only parameters that affect crop yield and cannot fully represent growth conditions. Climate data are more useful in crop yield estimations when combined with plant growth data [17]. Satellite images taken with remote sensing tools, such as spectral images in different bands, can offer direct information about the growth condition of plants. These spectral bands include optical spectra (visible and infrared) and microwave and thermal bands. Among these, light spectra such as infrared and red are widely used in the monitoring of plant growth parameters. Given the nonlinear nature of the empirical equations for the relationship between crop yield, crop growth indices, and climatic variables and the massive volume of spatio-temporal data that must be processed in statistical models to predict crop yield, these predictions require extensive computation. However, such computations can now be done easily thanks to advanced computer technology. In recent years, researchers have shown growing interest in the use of machine learning methods to predict crop yield [22]. Support vector machine (SVM), random forest (RF), and K-nearest neighbors (KNN) are three of the machine learning methods that have been successfully used to predict crop yield through classification and regression analysis of remote sensing data and climatic measurements. For example, [PERSON] et al. [10] used an SVM-based method to predict rice crop yield in order to facilitate crop selection in 27 districts of Maharashtra state in India. In a study by [PERSON] et al. [14], support vector regression (SVR) was used to predict the sugarcane yield in Karnataka, India. These researchers used a combination of climate and soil data with the satellite maps pertaining to the 2008-2018 period as the remote sensing data. [PERSON] et al. [15] used SVM to predict the yield of paddy rice in Chongqing, China, based on the weather data of 34 stations in this region for the 1985-2012 period. After compared the results of SVM with those of back-propagation neural network (BPNN) and multiple linear regression (MLR), this study reported that SVM is a better method for determining the effects of climate variables on crop yield. In a study by [PERSON] et al. [13], three methods including artificial neural network (ANN), linear regression (LR), and SVR were used to predict wheat yield in Punjab, Pakistan. These researchers used fertilizer and pesticide data as well as weather and soil data. All of these data, which were related to a 21-year period, were collected from the office of statistics and agriculture in Sargodha. [PERSON] et al. [16] used an ANN to predict the yield of robusta coffee in Vietnam based on SOM data and then compared the results with the results of RF and MLR. The data used in this study included soil fertility and soil yield data randomly collected from 44 farms in five districts of Lam Dong Province and the monthly averages of weather data for a 30-year period. In a study by [PERSON] & [PERSON] [17], corn yield in Iowa, USA was estimated by the use of SVM, RF, ERT, and DL.