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刘昌华,方征,陈志超,周兰,岳学智,王哲,王春阳,Yuxin Miao.ASD Field Spec3野外便携式高光谱仪诊断冬小麦氮营养[J].农业工程学报,2018,34(19):162-169.DOI:10.11975/j.issn.1002-6819.2018.19.021
ASD Field Spec3野外便携式高光谱仪诊断冬小麦氮营养
投稿时间:2018-05-03  修订日期:2018-08-23
中文关键词:  光谱分析    诊断  冬小麦  模型  氮营养指数
基金项目: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)
作者单位
刘昌华 1. 河南理工大学测绘与国土信息工程学院焦作 454000 
方征 1. 河南理工大学测绘与国土信息工程学院焦作 454000 
陈志超 1. 河南理工大学测绘与国土信息工程学院焦作 454000 
周兰 2. 中国农业大学资源与环境学院北京 100194
 
岳学智 1. 河南理工大学测绘与国土信息工程学院焦作 454000 
王哲 1. 河南理工大学测绘与国土信息工程学院焦作 454000 
王春阳 1. 河南理工大学测绘与国土信息工程学院焦作 454000 
Yuxin Miao 3. Department of Soil, Water, and Climate, University of Minnesota St. Paul, MN, 55108, USA 
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中文摘要: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。
Liu Changhua,Fang Zheng,Chen Zhichao,Zhou Lan,Yue Xuezhi,Wang Zhe,Wang Chunyang,Yuxin Miao.Nitrogen nutrition diagnosis of winter wheat based on ASD Field Spec3[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(19):162-169.DOI:10.11975/j.issn.1002-6819.2018.19.021
Nitrogen nutrition diagnosis of winter wheat based on ASD Field Spec3
Author NameAffiliation
Liu Changhua 1.Institute of Surveying-mapping and Land Information Engineering, Henan Polytechnic UniversityJiaozuo P.R.China 454000, China
 
Fang Zheng 1.Institute of Surveying-mapping and Land Information Engineering, Henan Polytechnic UniversityJiaozuo P.R.China 454000, China
 
Chen Zhichao 1.Institute of Surveying-mapping and Land Information Engineering, Henan Polytechnic UniversityJiaozuo P.R.China 454000, China
 
Zhou Lan 2. Institute of Resources and Environment, China Agricultural UniversityBeijing 100194, China
 
Yue Xuezhi 1.Institute of Surveying-mapping and Land Information Engineering, Henan Polytechnic UniversityJiaozuo P.R.China 454000, China
 
Wang Zhe 1.Institute of Surveying-mapping and Land Information Engineering, Henan Polytechnic UniversityJiaozuo P.R.China 454000, China
 
Wang Chunyang 1.Institute of Surveying-mapping and Land Information Engineering, Henan Polytechnic UniversityJiaozuo P.R.China 454000, China
 
Yuxin Miao 3. Department of Soil, Water, and Climate, University of Minnesota St. Paul, MN, 55108, USA 
Key words:spectrum analysis  nitrogen  diagnosis  winter wheat  diagnose model  nitrogen nutrition index (NNI)
Abstract: 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 0.780, the minimum value of RMSE 0.133, and the minimum of RE 12.3%. It can be known that the regression model constructed by BP neural network is significantly more accurate than the one constructed by PLSR model. The decision coefficient of all the models pretreated by BP neural network was above 0.8, while the average one was increased to 0.834 from 0.683 under PLSR model. All pretreatment modeling accuracy combined with SG, SG+MSC(multiple scatter correction), SG+SNV and SNV+D(De-trending) reached above 0.85, when the predictive effect reached above 0.7 except BC, MSC and SNV. SG pretreatment R2 with the best modeling effect reached 0.861, with mean square root error 0.115, relative error 9.8% and predicted effect R2 0.780 as mentioned above. The NNI estimation model RF algorithm combined with different pretreatments was used to establish an prediction model of NNI, whose average value of model-set model decision coefficient R2 was 0.945, with the maximum one 0.959, the minimum RMSE 0.061, and the minimum of RE 5.3%. The prediction-set model’s mean value of decision coefficient R2 is 0.742, with the maximum 0.795, the minimum value of RMSE 0.125, and the minimum of RE 11.7%. It can be known that the regression model constructed by RF is significantly improved compared with PLSR model and BP neural network. The decision coefficient of all the models by RF are all above 0.9, and the prediction model accuracy R2 is above 0.7. After 10 spectral pretreatment methods and 3 modeling ones have been comprehensively compared in this study, it is found that different pretreatment and modeling methods have great impacts on modeling precision. The optimal hyperspectral modeling method is RF(random forest) algorithm. The average value of decision coefficient R2 for the prediction-set model obtained through RF algorithm was higher than the biased least squares model and BP(back-propagation) neural network respectively, with lower RMSE and RE. Therefore, it can be seen that 10 pretreatment methods combined with RF model have higher prediction accuracy, which is a robust modeling method to invert nitrogen nutrition index. From above, to preprocess the spectrum for winter wheat can improve modeling accuracy. The best spectral pretreatment method in this experiment is SG convolution smoothing. Therefore, by comparing three models, the average R2 increase range of SG convolution smoothing compared with the other nine pre-processed prediction set models is 0.054~0.121, with average RMSE decrease range 0.016~0.032 and average RE decrease range 1.7~3.1 percentage points. In terms of single preprocessing, other preprocessing transformations can be carried out after combining with SG convolution smoothing, which can also improve the prediction accuracy of its single preprocessing model.
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