Rice Yield Estimation Based on Continuous Wavelet Transform With Multiple Growth Periods

Abstract

Yield is an important indicator in evaluating rice planting, and it is the collective result of various factors over multiple growth stages. To achieve a large-scale accurate prediction of rice yield, based on yield estimation models using a single growth stage and conventional spectral transformation methods, this study introduced the continuous wavelet transform algorithm and constructed models under the premise of combined multiple growth stages. In this study, canopy reflectance spectra at four important stages of rice elongation, heading, flowering and milky were selected, and then, a rice yield estimation model was constructed by combining vegetation index, first derivative and wavelet transform based on random forest algorithm or multiple stepwise regression. This study found that the combination of multiple growth stages significantly improved the model accuracy. In addition, after two validations, the optimal model combination for rice yield estimation is first derivative-wavelet transform-vegetation index-random forest model based on four growth stages, with the coefficient of determination (R2) of 0.86, the root mean square error (RMSE) of 35.50 g·m−2 and the mean absolute percentage error (MAPE) of 4.6% for the training set, R2 of 0.85, RMSE of 33.40 g.m−2 and MAPE 4.30% for the validation set 1, and R2 of 0.80, RMSE of 37.40 g·m−2 and MAPE of 4.60% for the validation set 2. The research results demonstrated that the established model could accurately predict rice yield, providing technical support and a foundation for large-scale statistical estimating of rice yield.