Given the nonlinearity of the interaction between weather and soil variables,
a novel deep neural network regressor (DNNR) was carefully designed with
considerations 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) was proposed to address the
shortcomings of root mean square error (RMSE) and mean absolute error (MAE)
while combining their strengths. Using the ARSE metric, the random forest
regressor (RFR) and the extreme gradient boosting regressor (XGBR), were
compared with DNNR. The RFR and XGBR achieved yield errors of 0.0000294 t/ha,
and 0.000792 t/ha, respectively, compared to the DNNR(s) which achieved 0.0146
t/ha and 0.0209 t/ha, respectively. All errors were impressively small.
However, with changes to the explanatory variables to ensure generalizability
to unforeseen data, DNNR(s) performed best. The unforeseen data, different from
unseen data, is coined to represent sudden and unexplainable change to weather
and soil variables due to climate change. Further analysis reveals that a
strong interaction does exist between weather and soil variables. Using
precipitation and silt, which are strong-negatively and strong-positively
correlated with yield, respectively, yield was observed to increase when
precipitation was reduced and silt increased, and vice-versa.

通过设计深度神经网络回归器（DNNR），提出了一种新颖的 DNNR 模型，用于解决天气和土壤变量之间的非线性相互作用问题，同时引入了平均绝对平方根误差（ARSE）指标以综合评估预测准确性，在与随机森林回归器（RFR）和极端梯度提升回归器（XGBR）进行比较后，DNNR 在处理突发的气候和土壤变化数据时表现最佳，进一步的分析揭示了天气和土壤变量之间存在着强烈的相互作用。