Rice Yield Prediction in Hubei Province Based on Deep Learning and the Effect of Spatial Heterogeneity  
[PERSON]  
1 National Engineering Research Center for Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China 1, [PERSON]  
1 National Engineering Research Center for Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China 1, and [PERSON]
data, Landsat and Sentinel-2 satellite data [10]. Soil and climate-related variables are also commonly used for yield forecasting, such as land cover [11], an integrated drought index [12], soil pH, soil moisture [13], precipitation, air temperature and humidity [14], etc. Based on remote sensing data, there are two main approaches to crop yield prediction: crop process simulation models and empirical statistical models [15]. The agricultural process simulation model can effectively replicate the physical processes of plant growth based on physiological knowledge of plant growth. It is the basic crop yield prediction model. Due to the inclusion of growth and environmental elements, these models are challenging to employ for large-scale forecasts. Empirical statistical models do not directly consider the physiological mechanisms of the plant. It is relatively simple and requires a smaller set of parameters and input variables. Empirical statistical models are more widely used in the field of crop yield prediction. Machine learning and deep learning algorithms can improve the accuracy of crop yield prediction compared to traditional linear regression methods. These more sophisticated and intelligent algorithms extract information automatically. Algorithms, such as support vector machines (SVM) [16], decision tree regression (DTR), and convolutional neural networks (CNN) [17], have been successfully applied. [PERSON] presented a comparative review of statistical, physical and artificial intelligence methods for spatiotemporal forecasting problems. Statistical methods are not conducive to advanced feature extraction and long-term memory modeling. The model structure and parameterization of physical models are imperfect and computationally intensive. Artificial intelligence models require elaborate training but have advantages for complex nonlinear and non-Gaussian problems [18]. For linear regression methods, machine learning methods and deep learning methods, [PERSON] et al. selected EVI and SIF (solar-induced chlorophyll fluorescence) as feature factors for comparison experiments to predict rice yield in China [19]. The results showed that the machine learning (RF) and deep learning (LSTM) methods performed significantly better than the linear regression. Compared with traditional linear regression methods, machine learning methods and deep learning methods can better extract the nonlinear relationship between input variables and rice yield. In addition, the result showed that the deep learning method outperformed the machine learning method in rice yield estimation, in part because the recurrent neural network structure in LSTM can fuse the nonlinear relationship between rice yield and environmental factors. [PERSON] used five regression models to forecast wheat yields in two provinces of Algeria [20]. It was found that ANN, RF and ELM based on machine learning methods outperformed DNN regression models in terms of prediction performance when the input variables were fewer, and when the number of input variables was more, the DNN model performed better than the other models. This is because deep learning methods typically outperform ordinary machine learning models for datasets with larger amounts of data. [PERSON] et al. also compared traditional machine learning methods (RF) with deep learning methods (DNN, 1D-CNN and LSTM) based on winter wheat production areas in China [21]. The results showed that machine learning methods were not always worse than deep learning methods at the county and regional scales.