Rice Haun stage estimation based on mechanistic and machine learning methods

Haun stage (HS), a continuous numerical phenological indicator of cereal crops, is widely used in agronomic management. However, few models have been developed to estimate HS considering the diverse environmental and agronomic influences. In this study, a dataset comprising 2350 HS observations of two rice (Oryza sativa L.) cultivars (Longjing31 and Suijing18) and variables including planting spatiotemporal information, transplanting day of year (TDOY), accumulated air temperature (AcTem), and remote-sensing vegetation indices (VIs) were collected from 226 field plots. Two mechanistic phenology models, Streck and Phyllochron, and three machine learning (ML) models, including the generalized linear model (GLM), gradient boosting machine (GBM), and deep learning (DL), were developed to predict the HS with different combinations of inputs. The results indicate that three ML models outperformed two mechanistic models, even when using simple spatiotemporal data, the relative root mean square error (RRMSE) decreased by more than 0.023. Especially for GBM and DL models exhibiting similar prediction accuracy (RRMSE from 0.0336 to 0.0543), GBM performs relatively better when VIs are included as input factors. The relative error density distributions (REDDs) of estimated HS in the three ML models were relatively spread out when using limited predictive information of spatiotemporal and VIs, especially during the late rice growth stage and for the Suijing18 cultivar. The inclusion of crop cultivar information enhanced the consistency of REDD, and either VIs or (TDOY, AcTem) provided sufficient information for accurate HS estimation. These findings can provide valuable insights for crop phenology estimation and agronomic practices under varying environments.