Monitoring of amylose content in rice based on spectral variables at the multiple growth stages

Amylose content is one of the important indexes for evaluating rice quality. Large-scale and rapid monitoring of rice quality is of great significance for measuring rice commodity value and regulating food crop production. Because amylose is wrapped in rice grains and cannot be directly expressed in the canopy spectrum, the accuracy of its canopy prediction model is often low. Considering that the accumulation and growth of rice starch granules are the result of multiple growth stages and multiple factors, this study attempts to introduce multiple growth stages information to improve the model, while most existing studies only use single growth stage information. The research area was located in Deqing County, Zhejiang Province, China The experiment spanned two rice growing seasons from 2016 to 2017, with five nitrogen levels and three rice varieties. Correlation relationships between the original spectra and first derivative spectra of rice canopy at booting stage, heading stage, milking stage and maturity stage and the grain amylose content were analyzed, then four types of vegetation indices and 23 hyperspectral features for further correlation analysis were computed. According to results of correlation analysis, the suitable spectral variables with high correlation coefficient were selected for amylose content modeling by stepwise regression method. The prediction models were established for different single growth stages to obtain the best growth stage of amylose prediction. Then, by combining the information of different growth stages, the amylose content prediction models based on the combination of different growth stages were established, and the effect of comprehensive application of multiple growth stage information on the amylose content prediction model was analyzed to get the best prediction model and its growth stages combination. The results showed that the first derivative, Difference Vegetation Index (DVI), Ratio Vegetation Index (RVI ) and the hyperspectral features at maturity stage were highly sensitive to amylose content. The derivative of 1 649 nm and 1 610 nm showed a good explanation for amylose content, 1 600~1 700 nm might be the sensitive sepctral bands of rice amylose prediction. In addition, the characteristic parameters of maturity-stage spectrum showed a strong explanatory ability in the maturity-stage model of this study, especially the blue edge position (λb), but this variable rarely appeared in other related research prediction models, and its principle and stability with strong prediction ability in maturity-stage model need further study and verification. The results of single growth stage modeling showed that the accuracy of the maturity and heading stages models was significantly higher than that of booting and milking stages，the most suitable growth stage for predicting amylose content was maturity stage, with the modeling coefficient of determination (R2)=0.558, Root Mean Square Error (RMSE)=0.896%, Mean Absolute Percent Error (MAPE)=4.49%, and validation R2=0.629, RMSE=0.864%, MAPE=4.59%. The comprehensive utilization of multi-growth stage information could significantly improve the prediction accuracy of the model, and the best multi-growth stage prediction model was the combination model of booting-heading-maturity stage, with the modeling R2=0.708, RMSE=0.711%, MAPE=3.22%, and validation R2=0.631, RMSE=0.768%, MAPE=3.99%, which proved that the model could accurately predict amylose content in grains.