基于无人机影像特征的冬小麦植株氮含量预测及模型迁移能力分析

doi:10.3864/j.issn.0578-1752.2023.05.004

Abstract

Abstract:

【Objective】Accurate monitoring and rational application of nitrogen are particularly important for healthy growth, yield and quality improvement of wheat, and reduction of environmental pollution and resource waste. The purpose of this study was to develop UAV-based models for accurately and effectively assessment of the plant nitrogen content in the key growth stages of wheat growth, and to explore the transferability of the models constructed based on machine learning methods. 【Method】Winter wheat experiment were conducted from 2020 to 2022 in Shangshui county, Henan province, China. Based on the K6 multichannel imager mounted on DJM600 UAV, 5-band (Red, Green, Blue, Rededge, and Nir) multispectral images were obtained from a UAV system in the stages of jointing, booting, flowering and filling in winter wheat, to calculate 20 vegetation indices and 40 texture features from different band combinations. Correlation analysis was used to screen the sensitive characteristics of nitrogen content in winter wheat plants from the 65 image features. Combining the sensitive spectral features and texture features of the nitrogen content of winter wheat plants, BP neural network (BP), random forest (RF), Adaboost, and support vector machine (SVR) machine learning regression methods were used to build plant nitrogen content models, and compared for the model performance and transferability. 【Result】(1)The correlation coefficients between plant nitrogen content and image features passed the test of 0.01 extremely significant level, including 22 spectral features and 29 texture features. (2) 51 spectral and texture features were adopted to build four machine learning models. The estimates of plant nitrogen by the RF and Adaboost methods were relatively concentrated, mostly close to the 1﹕1 line; while the estimations from the BP and SVR methods were relatively scattered. The RF method was the best, with R², RMSE, and MAE of 0.81, 0.42%, and 0.29%, respectively; The SVR method was the worst, with R², RMSE, and MAE of 0.66, 0.54% and 0.40%, respectively. (3) The prediction effects of the four methods on the nitrogen content of W0 and W1 treatments trained using W1 and W0 treatments were the same as those trained using both W0 and W1 datasets, both of which were closer to the 1﹕1 line for the RF and Adaboost methods. The R² of transfer prediction results for the models constructed by BP, RF, Adaboost, and SVR methods were 0.75, 0.72, 0.72, and 0.66 for the prediction of nitrogen content in W0 treatment and 0.51, 0.69, 0.61 (trained using data under W1 treatment) and 0.45 for the prediction under W1 treatment (trained using data under W0 treatment), respectively.【Conclusion】All models showed strong transferability, especially the RF and Adaboost methods, in predicting winter wheat nitrogen content under rainfed and irrigation water management.

Key words: UAV, spectral feature, textural feature, machine learning, nitrogen content in winter wheat, transferability

GUO Yan, JING YuHang, WANG LaiGang, HUANG JingYi, HE Jia, FENG Wei, ZHENG GuoQing. UAV Multispectral Image-Based Nitrogen Content Prediction and the Transferability Analysis of the Models in Winter Wheat Plant[J].Scientia Agricultura Sinica, 2023, 56(5): 850-865.

Figures/Tables 11

Fig. 1

Table 1

Vegetation index and the calculating formula"

植被指数 Vegetation index	简写Abbreviation	计算公式 Formulas	文献 References
绿波段归一化植被指数 Green-band normalized vegetation index	GNDVI	(R_nir-R_green)/(R_nir+R_green)	[43]
绿波段优化土壤调节植被指数 Green-band optimized soil adjusted vegetation index	GOSAVI	1.16×(R_nir-R_green)/(R_nir+R_green+0.16)	[44]
归一化差异植被指数 Normalized difference vegetation index	NDVI	(R_nir-R_red)/(R_nir+R_red)	[45]
改进简单比值植被指数 Modified simple ratio index	MSR	((R_nir/R_red)-1)/(((R_nir/R_red)+1)^0.5)	[46]
红边优化土壤调节植被指数 Rededge-band optimized soil adjusted vegetation index	REOSAVI	1.16×(R_nir-R_red)/(R_nir+R_red+0.16)	[47]
红边重归一化植被指数 Rededge-band renormalized difference vegetation index	RERDVI	(R_nir-R_{red edge})/((R_nir+R_rededge)^0.5)	[43]
叶绿素吸收比值指数 Chlorophyll absorption ratio index	CARI	(R_{red edge}-R_red)-0.2×(R_rededge+R_red)	[48]
优化土壤调节植被指数 Optimized soil adjusted vegetation index	OSAVI	(R_nir-R_red)/(R_nir+R_red+0.16)	[48]
归一化蓝绿差异指数 Normalized blue-green difference index	NGBDI	(R_green-R_blue)/(R_green+R_blue)	[49]
增强型植被指数 Enhanced vegetation index	EVI	2.5×(R_nir-R_red)/(R_nir+6×R_red-7.5×R_blue+1)	[50]
三角植被指数 Triangle vegetation index	TVI	0.5×(120×(R_nir-R_green)-200×(R_red-R_green))	[51]
大气阻抗植被指数 Atmospherically resistant vegetation index	VARI	(R_green-R_red)/(R_green+R_red-R_blue)	[52]
过绿指数 Excessive green index	EXG	2×R_green-R_red-R_blue	[53]
比值植被指数 Ratio vegetation index	RVI	R_nir/R_red	[52]
修正三角植被指数 Modified triangle vegetation index	MTVI	(1.5×(1.2×(R_nir-R_green)-2.5×(R_red-R_green)))/(((2×R_nir+1)²-6×R_nir-5×(R_red)^0.5-0.5)^0.5)	[54]
土壤调节植被指数Soil adjusted vegetation index	SAVI	1.5×(R_nir-R_red)/(R_nir+R_red+0.5)	[55]
归一化蓝绿波段差值植被指数 Normalized blue-green band difference vegetation index	GBNDVI	(R_nir-(R_green+R_blue))/(R_nir+R_green+R_blue)	[43]
重归一化植被指数Renormalized difference vegetation index	RDVI	(R_nir-R_red)(R_nir+R_red)^0.5	[54]
差值植被指数Difference vegetation index	DVI	R_nir-R_red	[56]
优化植被指数Optimized vegetation index	VIplot	1.45×(R²_nir+1)(R_red+0.45)	[57]

Table 1

Table 2

Table 3

Table 4

Fig. 2

Fig. 3

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Fig. 5

Table 5

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BP		RF		Adaboost		SVR
参数名 Parameters	参数值 Parameter value	参数名 Parameters	参数值 Parameter value	参数名 Parameters	参数值 Parameter value	参数名 Parameters	参数值 Parameter value
数据切分 Data cut	0.5	数据切分 Data cut	0.5	数据切分 Data cut	0.5	数据切分 Data cut	0.5
数据洗牌 Data shuffling	是 Yes	数据洗牌 Data shuffling	是 Yes	数据洗牌 Data shuffling	是 Yes	数据洗牌 Data shuffling	是 Yes
交叉验证 Cross validation	3折 3-fold cross validation	交叉验证 Cross validation	3折 3-fold cross validation	交叉验证 Cross validation	3折 3-fold cross validation	交叉验证 Cross validation	3折 3-fold cross validation
激活函数 Activation function	Identity	节点分裂评价准则 Identity node split evaluation criterion	mse	基分类器数量 Number of base classifiers	100	惩罚系数 Penalty factor	1
求解器 Sovler	lbfgs	内部节点分裂最小样本数 Minimum number of samples for internal node splitting	2	损失函数 Loss function	linear	核函数 Kernel function	linear
学习率 Learning rate	0.1	叶子节点最小样本数 Minimum number of samples of leaf nodes	1	基分类器 Base classifier	决策树 Decision tree	核函数系数 Kernel function coefficient	scale
L2正则项 L2 regular term	1	树的最大深度 Maximum depth of tree	10	学习率 Learning rate	1	核函数最高项次数 Maximum number of terms in kernel function	3
迭代次数 Number of iterations	1000	叶子节点的最大数量 Maximum number of leaf nodes	50			误差收敛条件 Error convergence condition	0.001
隐藏第1层神经元数量 Number of hidden layers 1st neurons	100	决策树数量 Number of decision trees	100			最大迭代次数 Maximum number of iterations	1000

反射率 Reflectance		植被指数 Vegetation index		植被指数 Vegetation index		植被指数 Vegetation index		植被指数 Vegetation index
B	-0.59**	GNDVI	-0.02	RERDVI	0.80**	TVI	0.57**	SAVI	0.79**
G	-0.09**	GOSAVI	-0.06	CARI	0.79**	VARI	0.76**	GBNDVI	-0.30**
R	-0.68**	NDVI	0.68**	OSAVI	0.57**	EXG	0.47**	RDVI	0.79**
Rededge	-0.20**	MSR	0.66**	NDRGI	0.60**	RVI	0.64**	DVI	0.35**
Nir	0.78**	REOSAVI	0.57**	EVI	0.46**	MTVI	0.80**	Viopt	-0.26**

波段 Band	con	cor	dis	ent	hom	mean	sm	var
B	-0.22**	0.21**	-0.25**	-0.24**	0.26**	-0.05	0.25**	-0.16**
G	-0.22**	-0.58**	-0.22**	-0.16**	0.22**	-0.32**	0.16**	-0.20**
R	-0.33**	0.09	-0.36**	-0.38**	0.36**	-0.22*	0.37**	-0.33**
Rededge	-0.08	-0.63**	-0.07	0.04	0.06	0.05	-0.05	-0.06
Nir	0.12*	-0.56**	0.11*	0.40**	-0.20**	0.79**	-0.39**	0.21**

方法Method	数据Dataset	R²	RMSE (%)	MAE (%)
BP	训练集Training dataset	0.84	0.40	0.30
BP	测试集Test dataset	0.71	0.48	0.37
RF	训练集Training dataset	0.96	0.17	0.13
RF	测试集Test dataset	0.81	0.42	0.29
Adaboost	训练集Training dataset	1.00	0.02	0.01
Adaboost	测试集Test dataset	0.79	0.44	0.32
SVR	训练集Training dataset	0.70	0.52	0.40
SVR	测试集Test dataset	0.66	0.54	0.40

UAV Multispectral Image-Based Nitrogen Content Prediction and the Transferability Analysis of the Models in Winter Wheat Plant

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