| 基于无人机数码影像的冬小麦氮含量反演 |
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| 引用本文： | 刘帅兵,杨贵军,景海涛,冯海宽,李贺丽,陈鹏,杨文攀.基于无人机数码影像的冬小麦氮含量反演[J].农业工程学报,2019,35(11):75-85. |
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| 作者姓名： | 刘帅兵 杨贵军 景海涛 冯海宽 李贺丽 陈鹏 杨文攀 |
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| 作者单位： | 北京农业信息技术研究中心农业部农业遥感机理与定量遥感重点实验室;武汉大学电子信息学院;国家农业信息化工程技术研究中心;河南理工大学测绘与国土信息工程学院 |
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| 基金项目： | 国家重点研发计划（2016YFD0300602）；国家自然科学基金（41471351，41601346）；北京市自然科学基金（6182011）；北京市农林科学院科技创新能力建设项目（KJCX20170423） |
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| 摘 要： | 准确、快速地获取关键生育期冬小麦氮素含量,对农业管理者进行田间氮素施肥有重要的决策作用。利用无人机(unmannedaerialvehicle,UAV)搭载数码相机,可以短时间内获取冬小麦长势信息,实现对冬小麦氮素含量动态监测。该研究利用2015年北京市小汤山冬小麦无人机数码影像,采用3种阈值分割方法,将田间植株作物与土壤背景分离。对比影像分割方法的时效性与准确性,最终确定可见光波段差异植被指数VDVI(visible-band difference vegetation index)提取植被信息。按照试验方案要求,在不同的氮肥与水分胁迫管理下,将冬小麦3次重复试验分成48个试验小区,依据小区边界提取小区的红、绿和蓝通道的平均DN(digitalnumber)值,选取25个植被指数,同时与各个试验小区冬小麦不同器官氮含量进行相关性分析,筛选数码影像变量。由于植被指数之间耦合度较高,因此采用主成分分析对原始数据进行成分提取,提取特征向量参与建模,最后利用多元线性回归分析建立氮素反演模型,通过决定系数(R2)、均方根误差(RMSE)和归一化的均方根误差(nRMSE)3个指标筛选出最佳模型,探究各器官氮素含量与数码变量的相关性。结果表明,实验室实测氮素含量与UAV数码影像氮素反演结果及基本一致。在反演模型构建精度方面,3种数据处理结果整体部分植被指数,反演效果叶氮植株氮茎氮。以冬小麦挑旗期为例,叶片氮含量整体信息提取验证模型的R2、RMSE和nRMSE分别为0.85、0.235和6.10%,比部分信息提取验证模型的R2高0.14,RMSE和nRMSE分别降低0.068和1.77个百分点;比植被指数信息提取验证模型的R2高0.43,RMSE和nRMSE分别降低0.141和3.67个百分点。研究表明,基于UAV数码影像利用多元线性回归构建冬小麦氮素含量反演模型,对试验小区整体提取作物信息的方式反演冬小麦叶氮含量效果最好,相比传统反演方法,模型稳定性更高,可为冬小麦田间水肥决策管理提供参考。
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| 关 键 词： | 无人机 氮素 冬小麦 主成分分析 多元线性回归 |
| 收稿时间： | 2018/8/27 0:00:00 |
| 修稿时间： | 2019/5/6 0:00:00 |
Retrieval of winter wheat nitrogen content based on UAV digital image |
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| Liu Shuaibing,Yang Guijun,Jing Haitao,Feng Haikuan,Li Heli,Chen Peng and Yang Wenpan.Retrieval of winter wheat nitrogen content based on UAV digital image[J].Transactions of the Chinese Society of Agricultural Engineering,2019,35(11):75-85. |
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| Authors: | Liu Shuaibing Yang Guijun Jing Haitao Feng Haikuan Li Heli Chen Peng and Yang Wenpan |
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| Institution: | 1. Agricultural Remote Sensing Mechanism and Quantitative Remote Sensing Key Laboratory of the Ministry of Agriculture, Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China; 2. Electronic Information School, Wuhan University, Wuhan 430072;,1. Agricultural Remote Sensing Mechanism and Quantitative Remote Sensing Key Laboratory of the Ministry of Agriculture, Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China; 4. National Engineering Research Center for Information Technology in Agricultural, Beijing 100097, China;,3. School of Surveying and Land Information Engineer, Henan Polytechnic University, Jiaozuo 454000, China;,1. Agricultural Remote Sensing Mechanism and Quantitative Remote Sensing Key Laboratory of the Ministry of Agriculture, Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China; 4. National Engineering Research Center for Information Technology in Agricultural, Beijing 100097, China;,1. Agricultural Remote Sensing Mechanism and Quantitative Remote Sensing Key Laboratory of the Ministry of Agriculture, Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China; 4. National Engineering Research Center for Information Technology in Agricultural, Beijing 100097, China;,1. Agricultural Remote Sensing Mechanism and Quantitative Remote Sensing Key Laboratory of the Ministry of Agriculture, Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China; 3. School of Surveying and Land Information Engineer, Henan Polytechnic University, Jiaozuo 454000, China; and 1. Agricultural Remote Sensing Mechanism and Quantitative Remote Sensing Key Laboratory of the Ministry of Agriculture, Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China; 3. School of Surveying and Land Information Engineer, Henan Polytechnic University, Jiaozuo 454000, China; |
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| Abstract: | Accurate and rapid acquisition of nitrogen content of winter wheat in critical growth period plays an important role in decision-making of nitrogen fertilization in field. Using unmanned aerial vehicle (UAV) with digital camera, the growth information of winter wheat can be obtained in a short time, and the dynamic monitoring of nitrogen content of winter wheat can be realized. In this study, three thresholding segmentation methods were used to separate the field plant crops from the soil background based on the digital image of winter wheat UAV in Xiaotangshan, Beijing, in 2015. By comparing the timeliness and accuracy of image segmentation methods, the visible-band difference vegetation index (VDVI) was finally determined to extract vegetation information. According to the requirements of the experiment scheme, winter wheat was divided into 48 material plots by three repeated experiments under different nitrogen and water stress management. In order to increase the difference of crop nitrogen content in each experimental plot, two different varieties of wheat were planted, and different water and nitrogen supply were added at the same time. Each treatment scheme was repeated three times. A total of 48 experimental plots were designed. The planting area of each plot was 48 m². Draw lessons from construction method of hyperspectral vegetation Index, 25 vegetation indices were constructed according to the average DN (digital number) values of red, green and blue channels extracted from the plot boundary. The correlation analysis was used to screen the digital image variables between the constructed vegetation index and the nitrogen content of different components of winter wheat in each material plot. Because of the high coupling degree between vegetation indices, principal component analysis was used to reduce the dimension of original data and extract feature vectors to participate in modeling. Various factors affecting the selection of modeling parameters and modeling were discussed. The nitrogen retrieval model was established by multiple linear regression analysis, and the best model was selected by determining coefficient (R2), root mean square error (RMSE) and normalized root mean square error (nRMSE) to explore the sensitivity of nitrogen content and digital variables. Using the model and UAV digital image, the retrieval image of winter wheat nitrogen was drawn, which visually display the spatial distribution of winter wheat nitrogen content. The results showed that the estimated value of winter wheat nitrogen content retrieved from UAV digital image had high fitting accuracy with the measured data. In terms of the accuracy of the inversion model, the three data processing results were integral segmentation > partial segmentation > segmentation by VDVI. The inversion effect of nitrogen content in different organs of winter wheat was different. Taking the flag-flying period of winter wheat as an example, the R2 and nRMSE of the verification model of integral segmentation was 0.85 which was 0.14 and 0.43 higher than that of the partial segmentation and VDVI segmentation, RMSE and nRMSE of the verification model of integral segmentation was 0.235 and 6.1% respectively, which was 0.068 and 1.77 percentage points lower than those of the partial segmentation, 0.141 and 3.67 percentage points lower than those of VDVI segmentation, respectively. The results can provide reference for decision-making and management of water and fertilizer in winter wheat field. |
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| Keywords: | UAV nitrogen winter wheat principal component analysis multiple linear regression |
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