Stabilizing the Predictive Performance for Ear Emergence in Rice Crops Across Cropping Regions

Several studies have demonstrated a good predictive performance of ear emergence in rice crops. However, significant regional variations in performance have been discovered and they remain unsolved. In this study, we aim to realize a stable predictive performance for ear emergence in rice crops regardless of its regional variations. Although a variety of data that represents regional characteristics have been adopted as the variables for prediction in related work, stability of the predictive performance has not been attained. These results imply that explicit regional data is insufficient for stabilizing the regional variances of the prediction. This study proposes to use engineered variables that uncover hidden regional characteristics behind the explicit regional data. Pre-examinations of the regional data indicate distinctive patterns of time dependency according to each region. Based on the findings, hidden Markov models are applied to the micro climate data to create engineered variables that represent the implicit time dependent regional characteristics. The efficiency of these variables is empirically studied, and the results show a significant improvement in the regional predictive variance.