  We investigate the predictive performance of two novel CNN-DNN machine
learning ensemble models in predicting county-level corn yields across the US
Corn Belt (12 states). The developed data set is a combination of management,
environment, and historical corn yields from 1980-2019. Two scenarios for
ensemble creation are considered: homogenous and heterogeneous ensembles. In
homogenous ensembles, the base CNN-DNN models are all the same, but they are
generated with a bagging procedure to ensure they exhibit a certain level of
diversity. Heterogenous ensembles are created from different base CNN-DNN
models which share the same architecture but have different levels of depth.
Three types of ensemble creation methods were used to create several ensembles
for either of the scenarios: Basic Ensemble Method (BEM), Generalized Ensemble
Method (GEM), and stacked generalized ensembles. Results indicated that both
designed ensemble types (heterogenous and homogenous) outperform the ensembles
created from five individual ML models (linear regression, LASSO, random
forest, XGBoost, and LightGBM). Furthermore, by introducing improvements over
the heterogeneous ensembles, the homogenous ensembles provide the most accurate
yield predictions across US Corn Belt states. This model could make 2019 yield
predictions with a root mean square error of 866 kg/ha, equivalent to 8.5%
relative root mean square, and could successfully explain about 77% of the
spatio-temporal variation in the corn grain yields. The significant predictive
power of this model can be leveraged for designing a reliable tool for corn
yield prediction which will, in turn, assist agronomic decision-makers.
