基于无人机多光谱影像和机器学习方法的玉米叶面积指数反演研究

doi:10.3724/SP.J.1006.2023.33001

Abstract

Abstract:

To make an accurate estimation of leaf are index (LAI) based on machine learning methods and images from UAV, we compared the several mainstream machine learning methods for maize LAI prediction, such as Artificial Neural Network method (ANN), Gaussian Process Regression method (GPR), Support Vector Regression method (SVR), and Gradient Boosting Decision Tree (GBDT). For this purpose, field experiments that considering apply of different amount of organic fertilizer, different amount of inorganic fertilizer, different amount of crop residue, and different planting density were carried out. Based on these experiments, field campaign were conducted to obtain UAV multispectral images and LAI data at different growth stages in maize. Based on above data, firstly, correlation analysis was used to select LAI-sensitive spectral indices, and then the Partial Least Squares Regression method (PLSR) and ANN, GPR, SVR, GBDT were coupled to design the LAI prediction models, respectively, and their performance for LAI prediction were compared. The results showed that the LAI prediction model constructed by PLSR+GBDT method had the highest accuracy and the best stability. The models of R² and RMSE values were 0.90 and 0.25, and the verified R² and RMSE values were 0.90 and 0.29 during validation, respectively. The model based on PLSR+GPR model was followed, with R² and RMSE values of 0.86 and 0.30 during calibration, and R² and RMSE values of 0.89 and 0.29 during validation, respectively. Besides, it had faster training speed and could give the uncertainty of the prediction. The model designed by PLSR+ANN method had R² and RMSE values of 0.85 and 0.31 during calibration, and R² and RMSE values of 0.89 and 0.30 during validation, respectively. The model designed by PLSR+SVR method had R² and RMSE values of 0.86 and 0.32, and R² and RMSE values of 0.90 and 0.33, respectively. Therefore, PLSR+GBDT method and PLSR+GPR method are recommended as the optimal methods for designing maize LAI prediction models.

Key words: LAI, machine learning, UAV, multispectral image, maize

MA Jun-Wei, CHEN Peng-Fei, SUN Yi, GU Jian, WANG Li-Juan. Comparing different machine learning methods for maize leaf area index (LAI) prediction using multispectral image from unmanned aerial vehicle (UAV)[J].Acta Agronomica Sinica, 2023, 49(12): 3364-3376.

Figures/Tables 15

Fig. 1

Table 1

Table 2

Spectral indices selected in this study"

缩写 Abbreviation	全称 Full name	公式 Formula	来源 Source
NDVI	归一化植被指数 Normalized Difference Vegetation Index	$\left( \text{NIR}-\text{R} \right)/\left( \text{NIR}+\text{R} \right)$	[22]
RVI	比值植被指数 Ratio Vegetation Index	$\text{NIR}/\text{R}$	[23]
DVI	差值植被指数 Difference Environmental Vegetation Index	$\text{NIR}-\text{R}$	[24]
EVI	增强植被指数 Enhanced Vegetation Index	$2.5\left( \text{NIR}-\text{R} \right)/\left( \text{NIR}+6\text{R}-\text{7}\text{.5B}+\text{1} \right)$	[25]
GNDVI	绿色归一化植被指数 Green Normalized Difference Vegetation Index	$\left( \text{NIR}-\text{G} \right)/\left( \text{NIR}+\text{G} \right)$	[26]
MSAVI	调整型土壤调节植被指数 Modified Soil Adjusted Vegetation Index	$\left( \text{2NIR}+1-\text{sqrt}\left( {{\left( 2\text{NIR+1} \right)}^{2}}-\text{8}\left( \text{NIR}-\text{R} \right) \right) \right)/\text{2}$	[27]
OSAVI	优化型土壤调节植被指数 Optimized Soil Adjusted Vegetation Index	$\text{1}\text{.16}\left( \text{NIR}-\text{R} \right)/\left( \text{NIR}+\text{R}+\text{0}\text{.16} \right)$	[28]
TVI	三角形植被指数 Triangular Vegetation Index	$\text{60}\left( \text{NIR}-\text{G} \right)-\text{100}\left( \text{R}-\text{G} \right)$	[29]
GRVI	绿色比值植被指数 Green Ratio Vegetation Index	$\text{NIR}/\text{G}-\text{1}$	[30]
SAVI	土壤调节植被指数 Soil Adjusted Vegetation Index	$\text{1}\text{.5}\left( \text{NIR}-\text{R} \right)/\left( \text{NIR}+\text{R}+\text{0}\text{.5} \right)$	[31]
RENDVI	红边归一化差值植被指数 Red Edge Normalized Difference Vegetation Index	$\left( \text{RE}-\text{R} \right)/\left( \text{RE}+\text{R} \right)$	[32]
RESR	红边比值植被指数 Red-Edge Simple Ratio	RE/R	[33]
MCARI	改进叶绿素吸收指数 Modified Chlorophyll Absorption Ratio Index	$\left( \left( \text{RE}-\text{R} \right)-0.2\left( \text{RE}-\text{G} \right) \right)\left( \text{RE}/\text{R} \right)$	[34]
TCARI	转换叶绿素吸收指数 Transformed Chlorophyll Absorption in Reflectance Index	$3\left( \left( \text{RE}-\text{R} \right)-0.2\left( \text{RE}-\text{G} \right)\left( \text{RE}/\text{R} \right) \right)$	[35]
TCARI/OSAVI	组合植被指数 Combined Spectral Index	TCARI/OSAVI	[36]
VARI	抗大气指数 Visible Atmospherically Resistant Index	$\left( \text{G}-\text{R} \right)/\left( \text{G}+\text{R}-\text{B} \right)$	[37]
RDVI	重归一化植被指数 Re-normalized Difference Vegetation Index	$\left( \text{NIR}-\text{R} \right)/\text{sqrt}\left( \text{NIR}+\text{R} \right)$	[38]
MSR	改进比值植被指数 Modified Simple Ratio	$\left( \text{NIR/R}-1 \right)/\text{sqrt}\left( \text{NIR/R}+1 \right)$	[39]
NGI	归一化绿色指数 Normalized Green Index	$G/\left( \text{NIR}+\text{G}+\text{RE} \right)$	[40]
NDRE	归一化差值红边指数 Normalized Difference Red Edge Index	$\left( \text{NIR}-\text{RE} \right)/\left( \text{NIR}+\text{RE} \right)$	[41]

Table 2

Fig. 2

Table 3

Table 4

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Table 5

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波段名称 Band name	中心波长 Central wavelength	波宽 Bandwidth
蓝光波段Blue band	475	20
绿光波段Green band	560	20
红光波段Red band	668	10
红边波段Red-edge band	717	10
近红外波段Near infrared band	840	40

生育期 Growth stage	样本数 Number of samples	最小值 Min. value	最大值 Max. value	平均值 Average value	标准差 Standard deviation	方差 Variance	变异系数 Coefficient of variation (%)
四叶期V4 stage	70	0.37	2.20	0.83	0.34	0.11	40.96
九叶期V9 stage	70	1.61	3.19	2.31	0.34	0.12	14.72

光谱指数 Spectral index	相关系数 Correlation coefficient	光谱指数 Spectral index	相关系数 Correlation coefficient
NDVI	0.84^**	RENDVI	0.81^**
RVI	0.85^**	RESR	0.80^**
DVI	0.89^**	MCARI	0.79^**
EVI	0.88^**	TCARI	0.67^**
GNDVI	0.88^**	TCARI/OSAVI	-0.78^**
MSAVI	0.88^**	VARI	0.82^**
OSAVI	0.87^**	RDVI	0.88^**
TVI	0.88^**	MSR	0.86^**
GRVI	0.89^**	NGI	-0.88^**
SAVI	0.88^**	NDRE	0.90^**

Comparing different machine learning methods for maize leaf area index (LAI) prediction using multispectral image from unmanned aerial vehicle (UAV)

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模型 Models	建模Calibration		验证Validation
模型 Models	R_cal²	RMSE_cal	R_val²	RMSE_val
PLSR+ANN	0.85	0.31	0.89	0.30
PLSR+GPR	0.86	0.30	0.89	0.29
PLSR+SVR	0.86	0.32	0.90	0.33
PLSR+GBDT	0.90	0.25	0.90	0.29

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