基于光谱指数的琯溪蜜柚叶片钙素含量估测模型研究

doi:10.3864/j.issn.0578-1752.2025.07.006

Abstract

Abstract:

【Objective】By constructing the estimation model for calcium (Ca) content in honey pomelo leaves based on spectral analysis, it could provide a theoretical basis for monitoring and rapid non-destructive diagnosis of Ca content in honey pomelo leaves.【Method】The original spectral and first-order derivative spectral characteristic bands and spectral characteristic indices (difference spectral index (DSI), ratio spectral index (RSI), and normalized difference spectral index (NDSI)) were analyzed and extracted. Single variable estimation model, partial least squares estimation model (PLS), backpropagation neural network estimation model (BPNN), random forest estimation model (RF), and support vector machine estimation model (SVM) for honey pomelo leaf calcium content were established, and the optimal spectral estimation model for honey pomelo leaf calcium content was evaluated and verified. 【Result】There was a significant multi band correlation between the original spectrum and first-order derivative spectrum of pomelo leaves and calcium content. Based on the correlation coefficients of the original spectrum and first-order derivative spectrum, the maximum wavelengths were 553, 714 nm and 528, 699, 602 nm, respectively. The spectral indices with significant correlation between the original spectrum, first-order derivative of pomelo leaves and calcium content were DSI_790,1040, RSI_910,990, NDSI_900,990 and NDSI′_350,580, DSI′_560,570, RSI′_350,580. The polynomial estimation model constructed with spectral indices such as RSI_910,990, NDSI_900,990, NDSI′_350,580, DSI_790,1040, DSI′_560,570, RSI′_350,580, DSI′_528,602 as independent variables had relatively high determination coefficient R² (R²>0.60). A hyperspectral estimation model for calcium content in honey pomelo leaves was established using the above four machine learning methods. The R²of PLS, BPNN, RF and SVM estimation models were 0.79, 0.82, 0.85 and 0.84, respectively, and the root mean square errors (RMSE) were 4.33, 4.11, 3.81 and 3.93, respectively; the R² of the validation models were 0.77, 0.80, 0.87 and 0.83, respectively, and the RMSE were 4.50, 4.28, 3.67 and 3.90, respectively. The order of estimating the accuracy of the model was RF>SVM>BPNN>PLS.【Conclusion】The accuracy comparison analysis of four models for calcium content in honey pomelo leaves showed that the RF estimation model had better predictive performance than the other three estimation models. This result could provide a new method for rapid diagnosis of calcium content in honey pomelo leaves for reference.

Key words: spectral index, estimation model, hyperspectrum, honey pomelo, Calcium

LI FangLiang, KONG QingBo, ZHANG Qing. Research on the Estimation Model of Calcium Content in Guanxi Honey Pomelo Leaves Based on Spectral Index[J].Scientia Agricultura Sinica, 2025, 58(7): 1321-1332.

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组别 Group	样本数 Sample number	最小值 Minimum value (g·kg^-1)	最大值 Maximum value (g·kg^-1)	平均值 Average value (g·kg^-1)	标准偏差 Standard deviation
Ca1	30	6.45	23.05	13.48	4.93
Ca2	30	24.41	30.67	27.54	2.19
Ca3	30	30.97	40.92	34.37	3.27
YZ	30	6.46	40.90	25.23	9.58

光谱指数Spectral index	高光谱参数Hyperspectal parameter	估测模型Estimation model	R²
差值光谱指数 DSI	DSI_553,714	y=40.74+107.395x	0.366
	DSI_790,1040	y=0.04-0.003x+2.205e-05x²	0.707
	DSI′_528,699	y=41.956e^83.27^x	0.552
	DSI′_528,602	y=37.859e^-195.191^x	0.642
	DSI′_560,570	y=0.002-8.919e-05x+8.736e-07x²	0.704
	DSI′_699,602	y=40.994e^-59.127^x	0.586
比值光谱指数 RSI	RSI_553,714	y=85.817+0.042^x	0.358
	RSI_910,990	y=1.047-0.001x	0.745
	RSI′_350,580	y=10.276+0.055^x	0.663
	RSI′_528,699	y=20.744+235.55x-966.837x²	0.439
	RSI′_528,602	y=e3.121+0.001/x	0.119
	RSI′_699,702	y=e3.954+22.644/x	0.409
归一化光谱指数 NDSI	NDSI_553,714	y=e^4.311+0.469/^x	0.352
	NDSI_900,990	y=-0.025-90.001x	0.736
	NDSI′_350,580	y=0.826+0.007x-7.925e-05x²	0.724
	NDSI′_528,699	y=-319.242-934.362x-617.407x²	0.441
	NDSI′_528,602	y=110.479-105.326x+31.409x²	0.122
	NDSI′_699,602	y=-2922.251+5739.173x-2786x²	0.519

估测模型 Estimation model	估算模型R²Estimation model R²	估算模型RMSE Estimation model RMSE	估算模型RE（%） Estimation model RE (%)	验证模型R²Validation model R²	验证模型RMSE Validation model RMSE	验证模型RE（%）Validation model RE (%)
偏最小二乘法 PLS	0.79	4.33	17.13	0.77	4.50	18.04
BP神经网络 BPNN	0.82	4.11	15.56	0.80	4.28	16.92
随机森林法 RF	0.85	3.81	13.91	0.87	3.67	13.05
支持向量机 SVM	0.84	3.93	13.53	0.83	3.90	12.69

Research on the Estimation Model of Calcium Content in Guanxi Honey Pomelo Leaves Based on Spectral Index

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