基于新型植被指数对冬小麦蛋白质含量的估算研究

小麦蛋白质含量是衡量小麦价格的一项重要指标。本文使用三年冬小麦蛋白质含量和光谱指数数据，用2008/2009和2009/2010年数据构建新的比率指数和乘积指数，并将灰色关联算法-偏最小二乘法(GRA-PLS)进行整合，尝试提高对冬小麦蛋白质含量估算的精度，用2011年/2012年数据进行验证。研究结果表明：比率指数与冬小麦蛋白质含量的相关系数要优于单一指数，单一指数和比率指数最高相关系数(r)分别为0.726和0.751，乘积指数也可改善部分单一指数的相关系数。通过GRA-PLS方法可以提高对冬小麦蛋白质含量的估算精度，单一指数、比率指数和乘积指数的决定系数(R2)分别为0.537，0.631和0.521，对应的均方根误差(RMSE)分别为0.665%，0.564%和0.574%。结果说明用新构建的比率指数和乘积指数，并使用GRA-PLS方法对冬小麦蛋白质含量估算是可行的。

Estimation of Winter Wheat Protein Content Based on New Indexes

Wheat protein content is an important indicator often employed in wheat sale price. Spectral indexes and concurrent winter wheat protein content (WWPC) samples were obtained across three years. Data from 2008/2009 and 2009/2010 were utilized to build the new ratio indexes and product indexes, and then selected grey relational method and partial least squares method were used to improve the estimation accuracy of WWPC, data from 2011/2012 was utilized to validate model. The results showed that the correlation coefficients between ratio indexes and WWPC were better than that between single indexes and WWPC. The r of single indexes and ratio indexes were 0.726 and 0.751, respectively, and the product indexes were used to improve the parts of single indexes. The estimation accuracy of WWPC was improved by using GRA-PLS, the determination coefficients (R2) of single indexes, ratio indexes and product indexes were 0.537, 0.631 and 0.521, respectively, and corresponding root mean square errors (RMSE) were 0.665%, 0.564% and 0.574%, respectively. The results indicated that it was feasible to estimate WWPC by building new ratio indexes and product indexes, and then applying the GRA-PLS.