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Abstract Preharvest yield estimates can be used for harvest planning, marketing, and prescribing in‐season fertilizer and pesticide applications. One approach that is being widely tested is the use of machine learning (ML) or artificial intelligence (AI) algorithms to estimate yields. However, one barrier to the adoption of this approach is that ML/AI algorithms behave as a black block. An alternative approach is to create an algorithm using Bayesian statistics. In Bayesian statistics, prior information is used to help create the algorithm. However, algorithms based on Bayesian statistics are not often computationally efficient. The objective of the current study was to compare the accuracy and computational efficiency of four Bayesian models that used different assumptions to reduce the execution time. In this paper, the Bayesian multiple linear regression (BLR), Bayesian spatial, Bayesian skewed spatial regression, and the Bayesian nearest neighbor Gaussian process (NNGP) models were compared with ML non‐Bayesian random forest model. In this analysis, soybean (Glycine max) yields were the response variable (y), and spaced‐based blue, green, red, and near‐infrared reflectance that was measured with the PlanetScope satellite were the predictor (x). Among the models tested, the Bayesian (NNGP;R²‐testing = 0.485) model, which captures the short‐range correlation, outperformed the (BLR;R²‐testing = 0.02), Bayesian spatial regression (SRM;R²‐testing = 0.087), and Bayesian skewed spatial regression (sSRM;R²‐testing = 0.236) models. However, associated with improved accuracy was an increase in run time from 534 s for the BLR model to 2047 s for the NNGP model. These data show that relatively accurate within‐field yield estimates can be obtained without sacrificing computational efficiency and that the coefficients have biological meaning. However, all Bayesian models had lowerR²values and higher execution times than the random forest model. 

Abstract Because the manual counting of soybean (Glycine max) plants, pods, and seeds/pods is unsuitable for soybean yield predictions, alternative methods are desired. Therefore, the objective was to determine if satellite remote sensing‐based artificial intelligence (AI) models could be used to predict soybean yield. In the study, multiple remote sensing‐based AI models were developed for soybean growth stage ranging from VE/VC (plant emergence) to R6/R7 (full seed to beginning maturity). The ability of the deep neural network (DNN), support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), and AdaBoost to predict soybean yield, based on blue, green, red, and near‐infrared reflectance data collected by the PlanetScope satellite at six growth stages, was determined. Remote sensing and soybean yield monitor data from three different fields in 2 years (2019 and 2021) were aggregated into 24,282 grid cells that had the dimensions of 10 m by 10 m. A comparison across models showed that the DNN outperformed the other models. Moreover, as crops matured from VE/VC to R4/R5, theR²value of the models increased from 0.26 to over 0.70. These findings indicate that remote sensing data collected at different growth stages can be combined for soybean yield predictions. Moreover, additional work needs to be conducted to assess the model's ability to predict soybean yield with vegetation indices (VIs) data for fields not used to train the model.