Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (7): 1321-1332.doi: 10.3864/j.issn.0578-1752.2025.07.006

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

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

LI FangLiang(), KONG QingBo(), ZHANG Qing   

  1. Institute of Resources, Environment and Soil Fertilizer, Fujian Academy of Agricultural Sciences/Fujian Key Laboratory of Plant Nutrition and Fertilizer, Fuzhou 350013
  • Received:2024-08-10 Accepted:2024-12-23 Online:2025-04-08 Published:2025-04-08
  • Contact: KONG QingBo

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 DSI790,1040, RSI910,990, NDSI900,990 and NDSI′350,580, DSI′560,570, RSI′350,580. The polynomial estimation model constructed with spectral indices such as RSI910,990, NDSI900,990, NDSI′350,580, DSI790,1040, 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 R2 of 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

Table 1

Analysis of calcium content characteristics in honey pomelo leaves"

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

Fig. 1

Correlation between original spectral reflectance and Ca contents in honey pomelo leaves"

Fig. 2

Correlation between first-order derivative spectral reflectance and Ca content in honey pomelo leaves"

Fig. 3

Contour map of correlation analysis between Ca content in honey pomelo leaves and original and first-order derivative spectral indices"

Table 2

Single variable estimation models of spectral indices for Ca content in honey pomelo leaves"

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
DSI553,714 y=40.74+107.395x 0.366
DSI790,1040 y=0.04-0.003x+2.205e-05x2 0.707
DSI′528,699 y=41.956e83.27x 0.552
DSI′528,602 y=37.859e-195.191x 0.642
DSI′560,570 y=0.002-8.919e-05x+8.736e-07x2 0.704
DSI′699,602 y=40.994e-59.127x 0.586
比值光谱指数 RSI
RSI553,714 y=85.817+0.042x 0.358
RSI910,990 y=1.047-0.001x 0.745
RSI′350,580 y=10.276+0.055x 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
NDSI553,714 y=e4.311+0.469/x 0.352
NDSI900,990 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

Table 3

Estimation and validation of different estimation models for Ca contents in honey pomelo leaves"

估测模型
Estimation model
估算模型R2 Estimation
model R2
估算模型RMSE Estimation
model RMSE
估算模型RE(%) Estimation
model RE (%)
验证模型R2 Validation
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

Fig. 4

Relationship between measured and predicted Ca content in honey pomelo leaves"

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