## 1 Introduction
[17] estimated winter wheat yields in North China using multiple linear regression (MLR) and ANN models with satellite-derived vegetation indices and climate data, and showed that ANN outperformed MLR. [PERSON] et al. [18] predicted global and regional yields of wheat, corn, and potatoes using climate, soil, and hydrological variables; the overall results of RF were more efficient than those of MLR. [PERSON] and [PERSON] [19; 20] estimated corn yields in the United States (US) using satellite-based vegetation indices and climate data, and found that the prediction results from DNN were better than that from SVM. [PERSON] et al. [21] used meteorological and satellite-based vegetation index data to predict rice yield in Korea, and the results showed that the convolutional neural network (CNN) had a higher accuracy than ANN. However, these studies did not conduct a comprehensive and objective evaluation of the performance of various AI models available for crop yield prediction. In addition, previous studies using DNN or CNN did not present the model configuration sufficiently--such as the hyper-parameter setting and option tuning, which can greatly affect the model performance. Indeed, the existing research is somewhat limited in terms of input data. Most studies extracted input data from the entire period of crop growth (e.g., May to September) and used an average value of all months, although monthly variation of input variables may account for the effect of phenology on crop yield. Also, approaches based on data averaged over all months cannot predict crop yield for the year in advance (i.e., at the late August or early September stage). Early prediction of crop yields for the major producer countries are important because they can affect world crop prices. The objective of this study was to develop an optimized DNN crop yield prediction model using optimized input variables from satellite products and meteorological datasets, following comprehensive and objective comparisons of various AI models. We first prepared input data for crop yield prediction, such as satellite-based vegetation indices, and meteorological and hydrological data, and constructed a matchup database on the Cropland Data Layer (CDL), a high-resolution map classifying crop types. Then, we selected an optimal period for crop yield estimation by considering the effect of phenology; for example, (1) May to September (entire growing season), (2) July to August (major productive period), and (3) a combination of all months showing high correlations between input variables and crop yield. We assumed that the prediction could be done based only on the July to August dataset, which can allow for yield prediction for the year, before harvesting. Using the optimized input dataset, we built six major AI models, including multivariate adaptive regression splines (MARS), SVM, RF, extremely randomized trees (ERT), ANN, and DNN, and comprehensively and objectively compared them. In particular, the DNN model was optimized by adjusting the hyper-parameters, layer structure, loss function, optimizer, activation function, and drop-out ratio to improve the accuracy of the crop yield prediction. Our experiment focused on corn and soybean yields in the Midwestern US, 2006-2015.