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作物学报 ›› 2021, Vol. 47 ›› Issue (7): 1342-1350.doi: 10.3724/SP.J.1006.2021.02060

• 耕作栽培·生理生化 • 上一篇    下一篇

基于高光谱的水稻叶片氮含量估计的深度森林模型研究

李金敏1, 陈秀青1,2, 杨琦1, 史良胜1,*()   

  1. 1武汉大学水资源与水电工程科学国家重点实验室, 湖北武汉 430072
    2长江勘测规划设计研究有限责任公司, 湖北武汉 430010
  • 收稿日期:2020-08-24 接受日期:2020-12-01 出版日期:2021-07-12 网络出版日期:2021-01-04
  • 通讯作者: 史良胜
  • 作者简介:E-mail: jinmlee@whu.edu.cn
  • 基金资助:
    本研究由国家自然科学基金项目资助(51861125202)

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 Published:2021-07-12 Published online:2021-01-04
  • Contact: SHI Liang-Sheng
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(51861125202)

摘要:

高光谱遥感已经成为快速诊断作物水氮状态的一种有效手段。然而, 传统的回归方法和机器学习往往难以挖掘高光谱的全部信息, 深度神经网络又通常需要大量的训练数据, 因此本研究试图探索在少量数据条件下构建深度学习模型并实现叶片氮含量的精准估计。通过在湖北省监利县开展了连续2年不同氮素胁迫水平的水稻试验, 测量了作物全生育期内的216组冠层光谱和叶片氮含量。基于一阶导数光谱, 本文构建了一种新的深度学习模型(深度森林DF)来进行叶片氮含量的反演, 并与2种经典机器学习模型(随机森林RF和支持向量机SVM)和一种深度神经网络模型(多层感知器MLP)进行比较。结果表明, 在基于少量高光谱数据的情况下, DF对水稻叶片氮含量的估算精度要高于MLP, 其中预测精度最高的模型为全波段光谱反演的DF模型(R2=0.919, RMSE=0.327)。在2种经典机器学习模型中, RF的估计效果优于SVM, 但2种模型结果都不够稳定。研究表明, 深度森林可以提升高光谱反演叶片氮含量的精度和稳定性, 并且可以通过多粒度扫描相对减轻过拟合程度。该研究结果可为少量数据条件下快速监测作物叶片氮含量提供参考。

关键词: 叶片氮含量, 深度学习, 机器学习, 高光谱遥感, 水稻

Abstract:

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

表1

2年水稻试验小区的面积和处理水平"

2018年试验Experiment in 20182019年试验Experiment in 2019
小区编号
Plot number
氮素水平
Nitrogen level
小区面积
Area (m2)
小区编号
Plot number
氮素水平
Nitrogen level
小区面积
Area (m2)
B1N3353.8B1N3′296.70
B2N2352.2B2N2′293.09
B3N0347.2B3N1′317.83
B4N1338.3B4N0333.10
B5N0338.3B5N3′329.80
B6N3337.5B6N2′383.78
B7N1340.5B7N1′306.47
B8N2342.6B8N0295.22
B9N3341.3B9N3′313.16
B10N1340.4B10N2′291.99
B11N2327.6B11N1′299.94
B12N0201.8B12N0292.74

图1

2018年(a)和2019年(b)试验小区分布图"

表2

2年水稻试验叶片氮含量的统计特征"

年份
Year
样本数量
Sample size
平均值
Mean (g g-1)
最大值
Max. (g g-1)
最小值
Min. (g g-1)
20181203.044.900.70
2019963.385.371.63

图2

采用多粒度扫描和级联森林结构的深度森林模型整体预测流程图"

表3

4种模型关键超参数设置"

随机森林
Random forest
支持向量机
SVM
多层感知器
Multi-layer perceptron
深度森林
Deep forest
树的数量
Number of trees
100正则化参数
Penalty coefficient
50激活函数
Activation function
“relu”级联结构中森林数量
Forest in cascade
10
决策树最大深度
Maximum depth of decision tree
“None”核函数
Kernel function
“RBF”隐藏层神经元数量
Number of hidden units
[100, 50, 20]滑动窗口大小
Sliding window size
{[d/16], [d/8], [d/4]}
核函数系数
Kernel coefficient
100优化器
Optimizer
“adam”窗口滑动步长
Sliding step
25
损失函数
Loss function
“mse”
迭代次数
Iterations
500

图3

不同氮素水平下水稻冠层光谱反射率氮素水平: N0 (0 kg hm-2), N1′ (50 kg hm-2), N2′ (100 kg hm-2), N3′ (150 kg hm-2)。"

表4

随机森林和支持向量机对水稻叶片含氮量估算效果分析"

回归模型
Regression model
光谱波段
Band
范围
Range (nm)
建模集
Calibration set
预测集
Prediction set
R2cRMSEcR2pRMSEp
随机森林
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
支持向量机
SVM
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

图4

随机森林(a)和支持向量机(b)模型对全波段光谱反演叶片氮含量结果对比"

表5

2种深度学习模型对水稻叶片含氮量估算效果分析"

回归模型
Regression model
光谱波段
Band
范围
Range (nm)
建模集
Calibration set
预测集
Prediction set
R2cRMSEcR2pRMSEp
多层感知器
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
深度森林
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

图5

多层感知器(a)和深度森林(b)模型对全波段光谱反演叶片氮含量结果对比"

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