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Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (7): 1342-1350.doi: 10.3724/SP.J.1006.2021.02060


Deep learning models for estimation of paddy rice leaf nitrogen concentration based on canopy hyperspectral data

LI Jin-Min1, CHEN Xiu-Qing1,2, YANG Qi1, SHI Liang-Sheng1,*()   

  1. 1National Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, Hubei, China
    2Changjiang Survey Planning, Design and Research Co., Ltd., Wuhan 430010, Hubei, China
  • Received:2020-08-24 Accepted:2020-12-01 Online:2021-07-12 Published:2021-01-04
  • Contact: SHI Liang-Sheng E-mail:liangshs@whu.edu.cn
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(51861125202)


Rapid and nondestructive detection of crop nitrogen status is crucial to precision agriculture management. Hyperspectral remote sensing has been proposed to be a powerful tool for expediently monitoring crop nitrogen status. However, conventional regression methods and machine learning (ML) are difficult to utilize the full information of hyperspectral data, and deep neural networks (DNN) usually require a huge number of training data. Therefore, we attempt to construct deep learning models with a small amount of data and achieve accurate estimation of leaf nitrogen concentration (LNC). A two-year field experiment of paddy rice with four nitrogen levels were conducted at Jianli, Hubei province, China. A total of 216 samples containing canopy hyperspectral data and rice LNC were measured during two growing seasons. Based on the first derivative of hyperspectral data, a new deep learning model (deep forest, DF) was constructed for LNC estimation and compared with two traditional machine learning models (random forest, RF and support vector machine, SVM) and one deep neural network model (multi-layer perceptron, MLP). The results showed that, based on a small number of hyperspectral data, deep forest acquired higher accuracy than MLP. And the optimal estimation (R2 = 0.919, RMSE = 0.327) was obtained by the deep forest model based on full-wave band spectrum (350-2500 nm). Between two classical machine learning models, random forest achieved better results than SVM, but both methods were unstable. In conclusion, deep forest improved the prediction accuracy and model robustness for all band situations, and alleviated the degree of overfitting by multi-grained scanning. These results can provide a deep insight to detect crop nitrogen status rapidly when confronted with limited data.

Key words: leaf nitrogen concentration, deep learning, machine learning, hyperspectral remote sensing, paddy rice

Table 1

Areas and treatments of two-year experiment in paddy rice"

2018年试验Experiment in 20182019年试验Experiment in 2019
Plot number
Nitrogen level
Area (m2)
Plot number
Nitrogen level
Area (m2)

Fig. 1

Distribution map of experiment plots in 2018 (a) and 2019 (b)"

Table 2

Leaf nitrogen concentration of two-year experiment in paddy rice"

Sample size
Mean (g g-1)
Max. (g g-1)
Min. (g g-1)

Fig. 2

Overall procedure of deep forest model using multi-grained scanning and cascade forest"

Table 3

List of key hyperparameters of the four models"

Random forest
Multi-layer perceptron
Deep forest
Number of trees
Penalty coefficient
Activation function
Forest in cascade
Maximum depth of decision tree
Kernel function
Number of hidden units
[100, 50, 20]滑动窗口大小
Sliding window size
{[d/16], [d/8], [d/4]}
Kernel coefficient
Sliding step
Loss function

Fig. 3

Canopy hyperspectral reflectance under different nitrogen treatmentsNitrogen level: N0 (0 kg hm-2), N1′ (50 kg hm-2), N2′ (100 kg hm-2), N3′ (150 kg hm-2)."

Table 4

Results of model performance analysis by random forest and support vector machine (SVM)"

Regression model
Range (nm)
Calibration set
Prediction set
Random forest
全波段Full-wave band350-25000.9820.1490.8910.378
可见光波段Visible waveband400-7600.9760.1710.8040.507
近红外波段Near-infrared waveband760-25000.9800.1560.8900.380
全波段Full-wave band350-25000.9600.2230.7250.601
可见光波段Visible waveband400-7600.8430.4400.7550.568
近红外波段Near-infrared waveband760-25000.9360.2810.6970.631

Fig. 4

Comparison between predicted and measured leaf nitrogen concentration using random forest (a) and support vector machine model (b) based on full-wave band"

Table 5

Model performance analysis by two deep learning models"

Regression model
Range (nm)
Calibration set
Prediction set
Multi-layer perceptron
全波段Full-wave band350-25000.9370.2820.8720.392
可见光波段Visible waveband400-7600.8760.3970.8580.412
近红外波段Near-infrared waveband760-25000.9320.2930.8600.408
Deep forest
全波段Full-wave band350-25000.9810.1510.9190.327
可见光波段Visible waveband400-7600.9780.1650.9030.358
近红外波段Near-infrared waveband760-25000.9790.1620.9170.330

Fig. 5

Comparison between predicted and measured leaf nitrogen concentration using multi-layer perceptron (a) and deep forest model (b) based on full-wave band"

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