利用相关矩阵法优化光谱指数的冬油菜氮素营养诊断
S365%S127; 近年来高光谱技术由于无损和高效等优点成为了现代精准农业发展的必要手段方法.为实现冬油菜无损、快速的氮素盈亏诊断,该研究以连续两年(2022-2023年)不同覆盖及施氮处理下冬油菜蕾薹期采集的90份植物样品(地上部生物量和植株氮浓度)和高光谱实测数据为数据源,根据原始光谱和一阶微分(first-orderdifferential,FD)光谱与氮营养指数(nitrogen nutrition index,NNI)的相关系数计算了 8种(共16个)典型的光谱指数,随后利用相关矩阵法提取最佳光谱组合,并根据与NNI相关系数的计算结果筛选最优光谱指数,最后将最优光谱指数分为3组模型...
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| Published in | 农业工程学报 Vol. 39; no. 17; pp. 97 - 106 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
西北农林科技大学旱区节水农业研究院,杨凌712100
01.09.2023
西北农林科技大学旱区农业水土工程教育部重点实验室,杨凌 712100 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.202305096 |
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| Abstract | S365%S127; 近年来高光谱技术由于无损和高效等优点成为了现代精准农业发展的必要手段方法.为实现冬油菜无损、快速的氮素盈亏诊断,该研究以连续两年(2022-2023年)不同覆盖及施氮处理下冬油菜蕾薹期采集的90份植物样品(地上部生物量和植株氮浓度)和高光谱实测数据为数据源,根据原始光谱和一阶微分(first-orderdifferential,FD)光谱与氮营养指数(nitrogen nutrition index,NNI)的相关系数计算了 8种(共16个)典型的光谱指数,随后利用相关矩阵法提取最佳光谱组合,并根据与NNI相关系数的计算结果筛选最优光谱指数,最后将最优光谱指数分为3组模型输入变量,分别采用支持向量机(support vector machine,SVM)、随机森林(random forest,RF)、极限学习机(extreme learning machine,ELM)和反向神经网络(back propagation neural network,BPNN)构建冬油菜蕾薹期 NNI 估算模型.结果表明一阶微分光谱指数与NNI的相关系数均大于原始光谱指数,3个组合选择的光谱指数与NNI的相关系数均较高且波长组合位置均在红边(670～760 nm)内,与NNI相关系数最高的光谱指数为FDSAVI,为0.674,波长组合位于712和678 nm;冬油菜NNI估算模型的最优输入变量与最优建模方法相结合建立的模型为组合2(输入变量为一阶微分光谱指数)与RF模型相结合,其中最优模型验证集的决定系数为0.823,均方根误差为0.079,平均相对误差为7.513％,表明模型精度较高,预测结果将为遥感技术在作物生产中植物氮素营养监测和诊断的潜在应用提供技术依据. |
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| AbstractList | S365%S127; 近年来高光谱技术由于无损和高效等优点成为了现代精准农业发展的必要手段方法.为实现冬油菜无损、快速的氮素盈亏诊断,该研究以连续两年(2022-2023年)不同覆盖及施氮处理下冬油菜蕾薹期采集的90份植物样品(地上部生物量和植株氮浓度)和高光谱实测数据为数据源,根据原始光谱和一阶微分(first-orderdifferential,FD)光谱与氮营养指数(nitrogen nutrition index,NNI)的相关系数计算了 8种(共16个)典型的光谱指数,随后利用相关矩阵法提取最佳光谱组合,并根据与NNI相关系数的计算结果筛选最优光谱指数,最后将最优光谱指数分为3组模型输入变量,分别采用支持向量机(support vector machine,SVM)、随机森林(random forest,RF)、极限学习机(extreme learning machine,ELM)和反向神经网络(back propagation neural network,BPNN)构建冬油菜蕾薹期 NNI 估算模型.结果表明一阶微分光谱指数与NNI的相关系数均大于原始光谱指数,3个组合选择的光谱指数与NNI的相关系数均较高且波长组合位置均在红边(670～760 nm)内,与NNI相关系数最高的光谱指数为FDSAVI,为0.674,波长组合位于712和678 nm;冬油菜NNI估算模型的最优输入变量与最优建模方法相结合建立的模型为组合2(输入变量为一阶微分光谱指数)与RF模型相结合,其中最优模型验证集的决定系数为0.823,均方根误差为0.079,平均相对误差为7.513％,表明模型精度较高,预测结果将为遥感技术在作物生产中植物氮素营养监测和诊断的潜在应用提供技术依据. |
| Abstract_FL | Accurate and timely nitrogen status diagnosis is crucial for the nitrogen application management and yield prediction of winter rapeseed.Traditional,destructive manual measurement cannot fully meet the large-scale production in recent years,due to the tedious,time-consuming and laborious.Fortunately,the non-destructive and efficient hyperspectral technology can serve as the necessary means in modern precision agriculture.This study aims to realize the rapid and accurate diagnosis for the prediction of the nitrogen nutrition status of winter rapeseed.Data sources were collected from 90 plant samples(above-ground biomass and plant nitrogen concentration)and hyperspectral measured data at the bud stage of winter oilseed rape under different mulching and nitrogen application treatments for two consecutive years(2022-2023).Eight typical spectral indices(16 in total)were first calculated using the correlation coefficients between the original and the first-order differential(FD)spectrum and the nitrogen nutrition index(NNI).Secondly,the correlation matrix was used to extract the best spectral combination.The optimal spectral index was then selected from the correlation coefficient with NNI.Finally,the optimal spectral index was divided into three groups of input variables for the model(combination 1:the five original spectral indices;combination 2:the five first-order differential spectral indices;combination 3:the five spectral indices,with the highest correlation coefficient with NNI).Support vector machine(SVM),random forest(RF),extreme learning machine(ELM),and back propagation neural network(BPNN)were used to construct the NNI estimation model of winter oilseed rape.Determination coefficient(R2),root mean square error(RMSE),and mean relative error(MRE)were used to evaluate the accuracy of the model.The results show that the correlation coefficient between the first-order differential spectral index and NNI was greater than that of the original,indicating a better NNI prediction using the first-order differential spectral index.There were high correlation coefficients between the spectral indices selected by the three combinations and NNI.The wavelength combinations were located in the red edge(670-760 nm),indicating the NNI variation representing the characteristic information in the red edge.The FDSAVI spectral index shared the highest correlation coefficient(0.674)with NNI.Among them,the wavelength combination was located at 712 and 678 nm.An optimal combination of the RF model and combination 2 were achieved to combine the optimal input variables of the NNI estimation model.The R2 value,RMSE,and MRE on the validation set were 0.823,0.079,and 7.513％,respectively,indicating the high accuracy of the optimal model.The finding can provide a technical basis for the potential application of remote sensing for the monitoring and diagnosis of plant nitrogen nutrition. |
| Author | 陈俊英 向友珍 张富仓 唐子竣 张威 李志军 王辛 |
| AuthorAffiliation | 西北农林科技大学旱区农业水土工程教育部重点实验室,杨凌 712100;西北农林科技大学旱区节水农业研究院,杨凌712100 |
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| Author_FL | LI Zhijun ZHANG Fucang TANG Zijun ZHANG Wei CHEN Junying XIANG Youzhen WANG Xin |
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| DocumentTitle_FL | Nitrogen nutrition diagnosis of winter oilseed rape using spectral indexes optimized by correlation matrix method |
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| Keywords | models nitrogen nutrition index 模型 hyperspectral winter oilseed rape 相关矩阵法 remote sensing 遥感 高光谱 冬油菜 氮营养指数 correlation matrix method |
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