Scientia Agricultura Sinica ›› 2023, Vol. 56 ›› Issue (5): 850-865.doi: 10.3864/j.issn.0578-1752.2023.05.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY · AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

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

GUO Yan1,2,3(), JING YuHang1,4, WANG LaiGang1,2,3, HUANG JingYi5, HE Jia1,2,3, FENG Wei4, ZHENG GuoQing1,2,3()   

  1. 1 Institute of Agricultural Economy and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002
    2 Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou 450002
    3 Henan Engineering Laboratory of Crop Planting Monitoring and Warning, Zhengzhou 450002
    4 College of Agronomy, Hennan Agricultural University/State Key Laboratory of Wheat and Maize Crop Science, Zhengzhou 450046
    5 Department of Soil Science, University of Wisconsin-Madison, Madison, WI 53706, USA
  • Received:2022-08-02 Accepted:2022-09-08 Online:2023-03-01 Published:2023-03-13

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

Fig. 1

Location of the study area and the experiment design"

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 (Rnir-Rgreen)/(Rnir+Rgreen) [43]
绿波段优化土壤调节植被指数 Green-band optimized soil adjusted vegetation index GOSAVI 1.16×(Rnir-Rgreen)/(Rnir+Rgreen+0.16) [44]
归一化差异植被指数 Normalized difference vegetation index NDVI (Rnir-Rred)/(Rnir+Rred) [45]
改进简单比值植被指数 Modified simple ratio index MSR ((Rnir/Rred)-1)/(((Rnir/Rred)+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×(Rnir-Rred)/(Rnir+Rred+0.16) [47]
红边重归一化植被指数 Rededge-band renormalized difference vegetation index RERDVI (Rnir-Rred edge)/((Rnir+Rrededge)0.5) [43]
叶绿素吸收比值指数 Chlorophyll absorption ratio index CARI (Rred edge-Rred)-0.2×(Rrededge+Rred) [48]
优化土壤调节植被指数 Optimized soil adjusted vegetation index OSAVI (Rnir-Rred)/(Rnir+Rred+0.16) [48]
归一化蓝绿差异指数 Normalized blue-green difference index NGBDI (Rgreen-Rblue)/(Rgreen+Rblue) [49]
增强型植被指数 Enhanced vegetation index EVI 2.5×(Rnir-Rred)/(Rnir+6×Rred-7.5×Rblue+1) [50]
三角植被指数 Triangle vegetation index TVI 0.5×(120×(Rnir-Rgreen)-200×(Rred-Rgreen)) [51]
大气阻抗植被指数 Atmospherically resistant vegetation index VARI (Rgreen-Rred)/(Rgreen+Rred-Rblue) [52]
过绿指数 Excessive green index EXG 2×Rgreen-Rred-Rblue [53]
比值植被指数 Ratio vegetation index RVI Rnir/Rred [52]
修正三角植被指数
Modified triangle vegetation index
MTVI (1.5×(1.2×(Rnir-Rgreen)-2.5×(Rred-Rgreen)))/(((2×Rnir+1)2-6×Rnir-5×(Rred)0.5-0.5)0.5) [54]
土壤调节植被指数Soil adjusted vegetation index SAVI 1.5×(Rnir-Rred)/(Rnir+Rred+0.5) [55]
归一化蓝绿波段差值植被指数
Normalized blue-green band difference vegetation index
GBNDVI (Rnir-(Rgreen+Rblue))/(Rnir+Rgreen+Rblue) [43]
重归一化植被指数Renormalized difference vegetation index RDVI (Rnir-Rred)(Rnir+Rred)0.5 [54]
差值植被指数Difference vegetation index DVI Rnir-Rred [56]
优化植被指数Optimized vegetation index VIplot 1.45×(R2nir+1)(Rred+0.45) [57]

Table 2

Parameters for BP, RF, AdaBoost, and SVR methods"

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

Yes
数据洗牌
Data shuffling

Yes
数据洗牌
Data shuffling

Yes
数据洗牌
Data shuffling

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

Table 3

The correlation analysis between spectral features and nitrogen content in winter wheat plants"

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**

Table 4

The correlation analysis between texture features and nitrogen content in winter wheat plants"

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**

Fig. 2

Relationship between the predicted and measured nitrogen content in winter wheat plant with different machine learning methods"

Fig. 3

Comparison of evaluation indices of different machine learning methods for winter wheat plant nitrogen content prediction models"

Fig. 4

Transferability of models constructed by the four machine learning methods under rainfed (W0) and irrigation (W1) treatments"

Fig. 5

Comparison of the transferability of models constructed by the four machine learning methods under rainfed (W0) and irrigation (W1) treatments"

Table 5

Evaluation of the nitrogen content models constructed by BP, RF, Adaboost and SVR methods"

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

Fig. 6

Curve fitting effects of the test datasets"

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