  Crop yield prediction has been modeled on the assumption that there is no
interaction between weather and soil variables. However, this paper argues that
an interaction exists, and it can be finely modelled using the Kendall
Correlation coefficient. Given the nonlinearity of the interaction between
weather and soil variables, a deep neural network regressor (DNNR) is carefully
designed with consideration to the depth, number of neurons of the hidden
layers, and the hyperparameters with their optimizations. Additionally, a new
metric, the average of absolute root squared error (ARSE) is proposed to
combine the strengths of root mean square error (RMSE) and mean absolute error
(MAE). With the ARSE metric, the proposed DNNR(s), optimised random forest
regressor (RFR) and the extreme gradient boosting regressor (XGBR) achieved
impressively small yield errors, 0.0172 t/ha, and 0.0243 t/ha, 0.0001 t/ha, and
0.001 t/ha, respectively. However, the DNNR(s), with changes to the explanatory
variables to ensure generalizability to unforeseen data, DNNR(s) performed
best. Further analysis reveals that a strong interaction does exist between
weather and soil variables. Precisely, yield is observed to increase when
precipitation is reduced and silt increased, and vice-versa. However, the
degree of decrease or increase is not quantified in this paper. Contrary to
existing yield models targeted towards agricultural policies and global food
security, the goal of the proposed corn yield model is to empower the
smallholder farmer to farm smartly and intelligently, thus the prediction model
is integrated into a mobile application that includes education, and a
farmer-to-market access module.
