基于无人机高光谱的马铃薯冠层叶片全氮含量反演

doi:10.3969/j.issn.1004-1524.20221475

浙江农业学报 ›› 2023, Vol. 35 ›› Issue (8): 1904-1914.DOI: 10.3969/j.issn.1004-1524.20221475

基于无人机高光谱的马铃薯冠层叶片全氮含量反演

郭发旭(), 冯全^*(), 杨森, 杨婉霞

甘肃农业大学机电工程学院,甘肃兰州 730070

收稿日期:2022-10-24 出版日期:2023-08-25 发布日期:2023-08-29
作者简介:郭发旭(1997—),男,甘肃永昌人,硕士研究生,主要从事遥感图像处理研究。E-mail:guofax@gsau.edu.cn
通讯作者: *冯全,E-mail:fquan@sina.com
基金资助:
国家自然科学基金(32160421);甘肃省教育厅产业支撑项目(2021CYZC-57);甘肃省优秀研究生“创新之星”项目(2022CXZXS-012);甘肃省高等学校青年博士基金(2021QB-033);甘肃省青年科技基金(20JR10RA544)

Inversion of leaf nitrogen content in potato canopy based on unmanned aerial vehicle hyperspectral images

GUO Faxu(), FENG Quan^*(), YANG Sen, YANG Wanxia

Mechanical and Electrical Engineering College, Gansu Agriculture University, Lanzhou 730070, China

Received:2022-10-24 Online:2023-08-25 Published:2023-08-29

摘要/Abstract

摘要：

为实现大田马铃薯冠层叶片全氮含量(LNC)的快速反演,利用低空无人机平台搭载成像光谱仪获取马铃薯冠层光谱数据,在综合比较原始反射率(R)、倒数变换反射率(1/R)、一阶微分变换反射率[D(R)]、二阶微分变换反射率[D(2R)]、倒数之对数变换反射率[lg(1/R)]的基础上,选择[D(2R)]用于后续试验。分别使用相关性分析(CA)、竞争性自适应重加权(CARS)、无信息变量消除(UVE)3种算法筛选特征光谱波段,使用偏最小二乘回归(PLSR)、支持向量机(SVM)构建马铃薯冠层LNC估测模型。结果表明:CA、CARS、UVE算法分别筛选出26、12、19个特征波段。在构建的PLSR模型中,用UVE筛选的特征波段建立的预测模型[UVE-D(2R)-PLSR]效果最好,在验证集上的决定系数(R²)和均方根误差(RMSE)分别为0.806 8和0.193 2;在构建的SVM模型中,用CARS筛选的特征波段建立的预测模型[CARS-D(2R)-SVM]效果最好,在验证集上的R²和RMSE分别为0.831 6和0.183 0。两模型对比,CARS-D(2R)-SVM模型的效果更好。采用CARS-D(2R)-SVM模型逐点估算马铃薯冠层LNC,绘制反演图,可使种植者直观掌握大田马铃薯生长情况,为马铃薯大田的精细化管理提供数据支持。

关键词: 无人机, 高光谱, 马铃薯, 支持向量机, 反演

Abstract:

To realize the rapid inversion of leaf nitrogen content (LNC) in the canopy of field potatoes, the spectral data of potato canopy leaves were obtained by an imaging spectrometer of a low-altitude unmanned aerial vehicle (UAV) platform. Based on the comprehensive comparison of original reflectance (R), reciprocal transformation reflectance (1/R), first-order differential transformation reflectance [D(R)], second-order differential transformation reflectance [D(2R)], and logarithm of reciprocal transformation reflectance [lg(1/R)], [D(2R)] was selected for the subsequent experiment. Correlation analysis (CA), competitive adaptive reweighed sampling (CARS) and uninformative variables elimination (UVE) algorithms were introduced to screen the characteristic spectral bands, and partial least squares regression (PLSR) and support vector machine (SVM) algorithms were used to construct the LNC estimation model. It was shown that 26, 12 and 19 characteristic bands were screened out by CA, CARS and UVE algorithms, respectively. Among all the established PLSR models, the one based on characteristic bands sreend out by UVE [UVE-D(2R)-PLSR for short] had the best performace, as its determinatino coefficient (R²) and root mean square error (RMSE) on the validatin set were 0.806 8 and 0.193 2, respectively. Among all the established SVM models, the one based on characteristic bands screened out by CARS [CARS-D(2R)-SVM for short] had the best performance, as its R² and RMSE on the validation set were 0.831 6 and 0.183 0, respectively. Compared with UVE-D(2R)-PLSR, CARS-D(2R)-SVM showed better modeling effect. The constructed CARS-D(2R)-SVM model was used to estimate LNC based on the spectral image of potato canopy, and the inverse diagram of LNC was plotted, which could help the growers intuitively grasp the potato growth in the field and provide data support for the potato field management.

Key words: unmanned aerial vehicles, hyperspectrum, potato, support vector machine, inversion

中图分类号:

S127

郭发旭, 冯全, 杨森, 杨婉霞. 基于无人机高光谱的马铃薯冠层叶片全氮含量反演[J]. 浙江农业学报, 2023, 35(8): 1904-1914.

GUO Faxu, FENG Quan, YANG Sen, YANG Wanxia. Inversion of leaf nitrogen content in potato canopy based on unmanned aerial vehicle hyperspectral images[J]. Acta Agriculturae Zhejiangensis, 2023, 35(8): 1904-1914.

图/表 12

表1 样本的冠层叶片全氮含量(LNC)统计值

Table 1 Statistics of leaf nitrogen content (LNC) of samples

样本类型 Sample category	样本数量 Sample size	LNC/%
样本类型 Sample category	样本数量 Sample size	最大值 Maximum value	最小值 Minimum value	平均值 Mean value	标准偏差 Standard deviation
总样本Whole set	110	4.850 0	2.580 0	3.960 5	0.531 6
建模集Calibration set	73	4.850 0	2.720 0	3.967 4	0.524 5
验证集Validation set	37	4.790 0	2.580 0	3.946 8	0.545 1

图1 原始光谱与经过变换的光谱反射率曲线 R,原始光谱;1/R,取倒数;D(R),取一阶微分;D(2R),取二阶微分;lg(1/R),取倒数之常用对数。

Fig.1 Spectral reflectance curves before and after transformation R, Raw; 1/R, Reciprocal; D(R), First-order differential; D(2R), Second-order differential; lg(1/R), Common logarithm of reciprocal value.

表2 基于原始光谱与经过变换的光谱反射率曲线构建的模型效果对比

Table 2 Comparison of modeling effects based on spectral reflectance curves before and after transformation

数据 Data	PLSR		SVM
数据 Data	R²	RMSE	R²	RMSE
R	0.672 0	0.300 4	0.713 1	0.281 3
1/R	0.638 0	0.320 9	0.704 9	0.291 0
D(R)	0.733 6	0.270 8	0.701 3	0.290 1
D(2R)	0.856 9	0.201 7	0.777 2	0.268 0
lg(1/R)	0.642 5	0.313 7	0.721 0	0.277 5

图2 叶片LNC与D(2R)的相关系数

Fig.2 Correlation coefficient between LNC and D(2R)

图3 竞争性自适应重加权(CARS)算法的运行结果 a,变量个数与采样运行次数的关系;b,交叉验证的均方根误差(RMSECV)与采样运行次数的关系;c,回归系数与采样运行次数的关系,“*”表示回归系数最小时所对应的采样次数,不同颜色的曲线代表不同光谱变量回归系数的变化情况。

Fig.3 Running results of competitive adaptive reweighted sampling (CARS) algorithm a, The relationship between the number of variables and the number of sampling runs; b, The relationship between the root mean square error of cross-validation (RMSECV) and the number of sampling runs; c, The relationship between regression coefficient and the number of sampling runs, in which the column marked with an asterisk (*) indicates the number of sampling runs associated with the minimum regression coefficient, and the various colored curves reflect the fluctuations in regression coefficients of distinct spectral variables.

图4 无信息变量消除(UVE)算法的运行结果

Fig.4 Running results of uninformative variables elimination (UVE) algorithm

表3 特征波段筛选结果

Table 3 Characteristic band screening result

方法 Method	特征波段数 Characteristic band number	特征波长 Characteristic wavelength/nm
CA	26	643.8、647.3、640.3、654.2、650.7、657.7、592.0、633.4、636.8、588.6、595.5、571.5、661.2、568.1、585.2、470.6、473.9、699.7、703.3、692.7、696.2、564.7、520.7、517.4、514.0、689.2
CARS	12	431.0、557.9、592.0、633.4、657.7、668.2、682.2、735.1、738.6、749.3、788.7、810.3
UVE	19	557.9、581.8、592.0、602.3、633.4、643.8、647.3、654.2、657.7、668.2、678.7、682.2、731.5、738.6、770.8、788.7、835.7、890.6、901.7

表4 基于不同波段的PLSR建模结果比较

Table 4 Comparison of PLSR modeling results based on different bands

波段 Band	特征波段数量 Number of characteristic bands	建模集Calibration set		验证集Validation set
波段 Band	特征波段数量 Number of characteristic bands	R²	RMSE	R²	RMSE
D(2R)全波段Whole bands of D(2R)	176	0.856 9	0.201 7	0.712 0	0.235 8
CA-D(2R)	26	0.669 7	0.306 5	0.699 3	0.241 0
CARS-D(2R)	12	0.825 4	0.222 9	0.783 9	0.204 3
UVE-D(2R)	19	0.780 1	0.250 1	0.806 8	0.193 2

图5 基于UVE-D(2R)构建的PLSR模型的验证结果

Fig.5 Validation of constructed PLSR model based on UVE-D(2R)

表5 基于不同波段的SVM建模结果比较

Table 5 Comparison of SVM modeling results based on different bands

波段 Band	C	λ	ε	特征波段数量 Number of characteristic bands	建模集Calibration set		验证集Validation set
波段 Band	C	λ	ε	特征波段数量 Number of characteristic bands	R²	RMSE	R²	RMSE
D(2R)全波段	1	0.002 76	0.01	176	0.777 2	0.268 0	0.618 7	0.342 0
Whole bands of D(2R)
CA-D(2R)	4	0.001 95	0.01	26	0.687 6	0.299 8	0.708 4	0.240 6
CARS-D(2R)	91	0.001 95	0.01	12	0.839 0	0.215 2	0.831 6	0.183 0
UVE-D(2R)	512	0.000 98	0.01	19	0.852 6	0.205 4	0.786 9	0.206 2

图6 基于CARS-D(2R)构建的SVM模型的验证结果

Fig.6 Validation of constructed SVM model based on CARS-D(2R)

图7 2021-07-28(a)和2021-08-19(b)马铃薯冠层的LNC反演估测图

Fig.7 Inversion and estimation of potato canopy LNC on 2021-07-28 (a) and 2021-08-19 (b)

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基于无人机高光谱的马铃薯冠层叶片全氮含量反演

Inversion of leaf nitrogen content in potato canopy based on unmanned aerial vehicle hyperspectral images

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