A New Framework for Winter Wheat Yield Prediction Integrating Deep Learning and Bayesian Optimization  
[PERSON] 1  
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs,  
Beijing 100081, China Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences,  
Beijing 100081, China Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China  
1  
[PERSON] 1  
[PERSON] 1  
[PERSON] 1  
required sampling frequency and sample size, and the variability of the climate from year to year makes traditional yield forecasts inaccurate and unstable [5]. The development of remote sensing technology has enabled large-scale crop yield prediction. Researchers have widely used remotely sensed vegetation indices and crop yields to build statistical regression models for yield estimation. Commonly used vegetation indices (VIs) are normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), near-infrared reflectance of vegetation (NIRv), and leaf area index (LAI) [6; 7; 8]. Some researchers have also used the wide dynamic range vegetation index (WDRVI) and solar induced chlorophyll fluorescence (SIF) to model crop yield estimates [9; 10]. Researchers are now utilizing non-linear models more frequently for yield estimation because crop yield is intimately tied to external environmental factors; meteorological variables affect crop growth, development, and final yield in a non-linear manner and often have complicated relationships [11; 12; 13; 14]. Machine learning and deep learning methods are often used to achieve accurate predictions of various crop yields, especially deep learning, and the application of deep learning to estimate crop yields has been a popular algorithm among researchers in recent years. Different deep learning models have so far been used to estimate yield for various crops, highlighting the importance of deep learning network frameworks. Mainstream deep learning models include convolutional neural network (CNN) and recurrent neural network (RNN) [15; 16; 17; 18]. CNN models can better handle the spatial autocorrelation of remote sensing images, but they cannot adequately consider the complex temporal correlation [19]. And RNN is a sort of neural network that primarily models sequence-type data by taking sequence data as input and using the correlation between sequence data to execute recursion in the direction of sequence evolution [20]. RNN, however, suffers from gradient disappearance, gradient explosion, and inadequate long-term memory capacity with time step iteration. Long short-term memory (LSTM) is an excellent variant of the RNN model, which not only has the characteristics of the RNN model, but also effectively avoids the undesirable situations such as gradient explosion and gradient disappearance, etc. It introduces the \"gate\" control structure, sets the internal gate mechanism, trains the weights of the input gate, forget gate and output gate, realizes the automatic screening and fusion of temporal features, and has better prediction and fitting performance for data [21; 22]. [PERSON] et al. [23] showed that the LSTM neural network model has much higher yield estimation accuracy than RNN. [PERSON] et al. [24] estimated county-level maize yields based on an LSTM neural network model using maize growth period as a time series by combining crop phenology information, meteorological data, and remote sensing data. The results showed that the LSTM neural network model was able to extract the implied relationships contained in the data series, achieve accurate county-level crop yield estimation, and provide robust yield estimation under extreme weather conditions. [PERSON] et al. [25] used LSTM to estimate wheat yield with different time steps, evaluating the comparison with back propagation neural network (BPNN) and support vector machine.