Estimating wheat grain protein content from ground-based hyperspectral data using a improved detecting method

Chinese Academy of Agricultural Sciences Room 104, Institute of Crop Sciences, No.12 South Street, Zhongguancun, Beijing, 100081,China. luylwheat@hotmail.com *shaokun0004@sina.com.cn. AbstractThat high correlation between nitrogen concentration and grain protein content (GPC) made it possible to predict grain quality indirectly by measuring nitrogen status in wheat. However, former studies all based on the canopy spectral reflectance (Rc) measurement method, by which mixed spectra such as ears, leaves and soil background spectrum were achieved. So it is very difficult to abstract useful information from the canopy spectra, and the precision can not be heightened authentically. Our objective was to test the feasibility of measuring ear-layer spectral reflectance (Rel) using a improved method and to realize prediction of wheat grain quality before harvest. The results showed that Rel measured by the improved method was proved more effective to estimate GPC of wheat than Rc measured by traditional method. In relation to Rc, Rel was much closer to pure ear spectral reflectance(Re), for the Rel measured by the improved method was not disturbed by factors such as soil background and plant-type. In this article, the correlations between ear nitrogen total content (ETNC) and 20 spectral characteristic parameters were also analyzed respectively, and the results indicated that ear layer spectral characteristic parameters have stronger correlativity with ETNC than canopy ones. Rel and GPC all had highly correlative relation with ETNC, so a new model was established to predict indirectly GPC using ratio vegetable index (RVI[890,670]) through two models linking, and by testing, R=0.6617, RSME=0.8509 by the improved method and R=0.8653 RSME=0.7339 by traditional method. it was proved that the improved method produced higher coefficient of determination and lower root mean square error (RMSE) than traditional method under the same condition, and the precision by RVI[890,670] calculated from Rel was 13.75 percent higher than from Rc, which indicated that the predictive model established with the improved measurement method is more reliable and practicability than with the traditional method. This study made it possible to predict wheat grain quality using RVI and laid the foundation for portable nitrogen and protein monitor exploiting.

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