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《China Agricultural Informatics》 2018-06
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Remote sensing prediction of grain protein content in regional winter wheat

Wang Qi;Song Xiaoyu;Yang Guijun;Li Zhenghai;Feng Haikuan;Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture,Beijing Research Center for Information Technology in Agriculture Beijing;Shandong Agricultural University;National Engineering Research Center for Information Technology in Agriculture;Beijing Engineering Research Center for Agriculture Internet of Things;  
[Purpose]Grain protein content(GPC) is an important indicator to evaluate the quality of wheat.Predicting the GPC quickly and accurately is beneficial to quality evaluation and hierarchical management.[Method]In this study,several factors that correlated with GPC evaluation,including meteorological factors in study area,wheat plant nitrogen parameters in flowering stage,and satellite spectral parameters for wheat samples,were analyzed using multi-linear regression(MLR),extreme learning machine algorithm(ELM) and geographical weighted regression(GWR) methods.Then the GPC prediction models based on different independent variables and methods were built and evaluated.[Result]The results showed that:(1) The nitrogen parameters at wheat flowering stage and the wheat canopy spectral parameters have significant correlations with wheat GPC.The key meteorological factors affecting the GPC of wheat include rainfall from 26 May to 30 May,sunshine time from mid-May to early June and accumulated temperature from early March to early June.(2) The coefficient of determination(R~2) of MLR model is 0.598,while the accuracies of Normalized root mean squared error(nRMSE) and Mean absolute error(MAE) are 10.36% and 1.091 respectively,which are stable.The R~2 of the GPC model based on ELM is 0.483,while the standard nRMSE and MAE are 10.895%and 1.111 respectively.The R~2 of the GPC GWR model is 0.616,while the standard nRMSE and MAE are 8.58% and 0.956 respectively,which are the optimal.[Conclusion]According to the precision evaluation indicator of the comprehensive analysis model,the multivariate parameter model is superior to the univariate parameter model.The multivariate parameter GWR model that takes the instability of spatial data into account has the best prediction accuracy and can predict the GPC more accurately.This study provides a reliable basis for accurately predicting the GPC in different regions and years,which has a broad application prospect in the future.
【Fund】: 国家重点研发计划(2016YFD0300603 2016YFD0700303);; 国家自然科学基金项目(41371349;41471285)
【CateGory Index】: S512.11;S127
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