基于高光谱的水稻叶片氮含量估计的深度森林模型研究

doi:10.3724/SP.J.1006.2021.02060

Abstract

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

LI Jin-Min, CHEN Xiu-Qing, YANG Qi, SHI Liang-Sheng. Deep learning models for estimation of paddy rice leaf nitrogen concentration based on canopy hyperspectral data[J].Acta Agronomica Sinica, 2021, 47(7): 1342-1350.

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2018年试验Experiment in 2018			2019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)
B1	N3	353.8	B1	N3′	296.70
B2	N2	352.2	B2	N2′	293.09
B3	N0	347.2	B3	N1′	317.83
B4	N1	338.3	B4	N0	333.10
B5	N0	338.3	B5	N3′	329.80
B6	N3	337.5	B6	N2′	383.78
B7	N1	340.5	B7	N1′	306.47
B8	N2	342.6	B8	N0	295.22
B9	N3	341.3	B9	N3′	313.16
B10	N1	340.4	B10	N2′	291.99
B11	N2	327.6	B11	N1′	299.94
B12	N0	201.8	B12	N0	292.74

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)
2018	120	3.04	4.90	0.70
2019	96	3.38	5.37	1.63

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

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
回归模型 Regression model	光谱波段 Band	范围 Range (nm)	R²_c	RMSE_c	R²_p	RMSE_p
随机森林 Random forest	全波段Full-wave band	350-2500	0.982	0.149	0.891	0.378
	可见光波段Visible waveband	400-760	0.976	0.171	0.804	0.507
	近红外波段Near-infrared waveband	760-2500	0.980	0.156	0.890	0.380
支持向量机 SVM	全波段Full-wave band	350-2500	0.960	0.223	0.725	0.601
	可见光波段Visible waveband	400-760	0.843	0.440	0.755	0.568
	近红外波段Near-infrared waveband	760-2500	0.936	0.281	0.697	0.631

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
回归模型 Regression model	光谱波段 Band	范围 Range (nm)	R²_c	RMSE_c	R²_p	RMSE_p
多层感知器 Multi-layer perceptron	全波段Full-wave band	350-2500	0.937	0.282	0.872	0.392
	可见光波段Visible waveband	400-760	0.876	0.397	0.858	0.412
	近红外波段Near-infrared waveband	760-2500	0.932	0.293	0.860	0.408
深度森林 Deep forest	全波段Full-wave band	350-2500	0.981	0.151	0.919	0.327
	可见光波段Visible waveband	400-760	0.978	0.165	0.903	0.358
	近红外波段Near-infrared waveband	760-2500	0.979	0.162	0.917	0.330

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

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