ASD Field Spec3野外便携式高光谱仪诊断冬小麦氮营养  

Nitrogen nutrition diagnosis of winter wheat based on ASD Field Spec3

作　　者：刘昌华[1] 方征 陈志超[1] 周兰 岳学智 王哲 王春阳[1] Yuxin Miao Liu Changhua;Fang Zheng;Chen Zhichao;Zhou Lan;Yue Xuezhi;Wang Zhe;Wang Chunyang;Yuxin Miao(Institute of Surveying-mapping and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454000,China;College of Resources and Environmental Sciences,China Agricultural University,Beijing 100193,China;Department of Soil,Water,and Climate,University of Minnesota,St.Paul,MN,55108,USA)

机构地区：[1]河南理工大学测绘与国土信息工程学院,焦作454000 [2]中国农业大学资源与环境学院,北京100193 [3]Department of Soil, Water, and Climate, University of Minnesota

出　　处：《农业工程学报》2018年第19期162-169,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金资助项目(41371105);河南省软科学研究计划项目(162400410058);河南省高等学校重点科研项目(18A420001);河南省智慧中原地理信息技术协同创新中心"开放课题(2016A002)

摘　　要：氮素营养诊断关键在于氮营养指数(nitrogen nutrient index,NNI)预测。对于冬小麦氮营养指数预测模型而言,如何选取预处理方法和建模方法不一而足,不同预处理和模型选取对预测结果精度的影响程度目前还不清楚。该研究以ASD Field Spec3野外便携式高光谱仪采集乐陵市冬小麦冠层高光谱数据,采用10种光谱预处理方法并结合3种模型(偏最小二乘回归、BP神经网络和随机森林算法)建立多种冬小麦氮营养指数高光谱预测模型。对比模型预测精度表明最佳的高光谱建模方法为随机森林算法结合SG卷积平滑预处理所建模型(预测集R2=0.795,RMSE=0.125,RE=11.7%)精度高、可靠性强,是筛选出最佳的冬小麦氮营养指数高光谱预测模型。该研究结果对冬小麦氮营养指数高光谱预测建模具有科学价值,为筛选最优高光谱预处理方法和预测模型提供技术参考。For crop’s prediction model of nitrogen nutrition index(NNI),how to select the pretreatment and modeling method is unclear as well as different pretreatments and their influence degrees on prediction accuracy.So it is of great significance to take more systematic related research for building crop nitrogen nutrition diagnosis rapidly and accurately,which can provide important technical support for precision agriculture management,and realize high yield with high efficiency of nitrogen utilization.Taking Nanxia Village,Laoling City in North China Plain as the research area,based on ASD Field Spec3,the prediction model of winter-wheat nitrogen nutrition index was established with hyperspectral technology in this study.PLSR combined with different pretreatments was applied to establish an prediction model of winter-wheat nitrogen nutrition index,whose average value of model-set model decision coefficient R2 was 0.683,with the maximum one 0.789,the minimum root mean square error(RMSE)0.142,and the minimum of relative medium error(RE)12.3%.The prediction-set model’s mean value of decision coefficient R2 is 0.588,with the maximum one 0.717,the minimum value of root mean square error(RMSE)0.150,and the minimum of relative medium error(RE)12.8%.The comparison shows that the pretreatment methods with SG(Savitzky-Golay),SNV(standard normal variate transformation),SG+SNV and SG+BC(baseline correction)are effective when partial least square method is used to build the model,especially SG smoothing is the optimal one as mentioned above with the R2 of 0.789,the RMSE of 0.142,the RE of 12.3%,and the R2 of the prediction accuracy of 0.717.Meanwhile,BP neural network method combined with different pretreatments was used to establish an prediction model of nitrogen nutrition index,whose average value of model-set model decision coefficient R2 was 0.834,with the maximum one 0.861,the minimum RMSE 0.115,and the minimum of RE 9.8%.The prediction-set model’s mean value of decision coefficient R2 was 0.714,with the maximum one 0.

关 键 词：光谱分析 氮 诊断 冬小麦 模型 氮营养指数 

分 类 号：S127[农业科学—农业基础科学]