Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (2): 252-265.doi: 10.3864/j.issn.0578-1752.2025.02.004

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

Inversion of Nitrogen Content in Chili Pepper Leaves Based on Hyperspectral Analysis

LIU Jing1(), WANG Hong1(), ZHANG Lei2, XIAO JiuJun3,4, WU JianGao1, GONG MingChong1   

  1. 1 Mining College of Guizhou University, Guiyang 550025
    2 Institute of Surveying and Mapping Guizhou Geology and Mineral Exploration Bureau, Guiyang 550025
    3 Institute of Mountain Resources, Guizhou Academy of Sciences, Guiyang 550001
    4 Guizhou Province Land Green Improvement Engineering Research Center, Guiyang 550001
  • Received:2024-05-04 Accepted:2024-08-01 Online:2025-01-21 Published:2025-01-21
  • Contact: WANG Hong

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Abstract:

【Objective】Nitrogen is one of the essential nutrients for plant growth and development, and it plays an important role in strengthening chlorophyll synthesis in crops, enhancing plant resistance, and improving yield and quality. This study harnessed hyperspectral technology to swiftly, precisely, and non-invasively monitor nitrogen levels in pepper foliage throughout its growth cycle, delving into the correlation between leaf nitrogen content (LNC) and spectral reflectance characteristics. 【Method】The study was based on the hyperspectral data of pepper leaves collected from Guanzhuang Demonstration Base in Pepper Research Institute of Guizhou Academy of Agricultural Sciences in 2021. The research encompassed four pepper varieties (Qianjiao No. 8, Hongla No. 18, Layan 101, and Hong Global) and five different nitrogen fertilizer application rates (0, 120, 240, 360, and 480 kg·hm-2). The pepper leaf spectral data were processed, involving Multiple Scatter Correction (MSC), Savitzky-Golay (SG) and First Derivative (FD), followed by the selection of sensitive bands using Pearson correlation coefficient, Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS). Subsequently, three machine learning algorithms, such as Partial Least Squares Regression (PLSR), Random Forest (RF) and Radial Basis Function Neural Network (RBFNN), were employed to construct models for monitoring nitrogen levels in pepper leaves, to achieve the goals of enhancing agricultural production efficiency and accuracy, and realizing intelligent management and precise fertilization. 【Result】After preprocessing, the original spectra improved correlation coefficients significantly. Among these, the spectral data's inversion performance was notably superior after SG processing, with the effectiveness ranking as SG>FD>MSC>original spectra. Contrasting various band selection methods, the employing Pearson correlation coefficient for band selection resulted in bands being overly concentrated, leading to either redundant information or incomplete information extraction. While CARS algorithm selected bands across a broad range and in large quantities, its effectiveness was inferior to SPA due to containing more redundant information and noise. SPA-selected nitrogen content characteristic bands effectively reduced collinearity and redundant information, yielding the optimal model with the highest R² and the smallest RMSE. The performance of different modeling methods for pepper LNC estimation was as follows: RBFNN performed the best, followed by PLSR, with RF exhibiting the poorest performance. Among these, the SG-SPA-RBFNN combined model demonstrated the best inversion accuracy, with modeling results of R² =0.98 and RMSE =0.62, and validation results of R² =0.98 and RMSE =1.21, with an RPD of 3.08. RBFNN model excelled in handling high-dimensional spectral data, surpassing traditional PLSR and RF models. 【Conclusion】The hyperspectral reflectance characteristics were utilized to establish nitrogen content prediction models, which could effectively monitor nitrogen levels in pepper leaves, thereby enhancing agricultural management efficiency and providing the technical support for precise management and variable fertilization in pepper cultivation.

Key words: hyperspectral, chili peppers, nitrogen, machine learning, SPA, RBFNN, spectrum analysis

Fig. 1

Location diagram of the test area"

Fig. 2

ASD FieldSpec4 ground object spectrometer"

Fig. 3

Photos of chili field test data collection"

Table 1

Results of statistical analysis of nitrogen content of chili peppers"

样本类型
Sample type		 样本量
Sample size		 最小值
Minimum
(g·kg-1)		 最大值
Maximum
(g·kg-1)		 平均值
Mean
(g·kg-1)		 标准差
Standard deviation (g·kg-1)		 变异系数
Coefficient of variation
总样本Total sample 60 18.18 43.06 30.07 5.33 17.71
建模集Calibration set 45 18.18 43.06 30.40 5.75 18.91
验证集Validation set 15 23.34 34.43 29.10 3.61 12.41

Fig. 4

Spectrum of pepper leaves with different nitrogen content"

Fig. 5

Correlation between spectral reflectance and LNC of chili pepper leaves"

Table 2

Sensitive feature wavelengths extracted for spectral samples"

预处理
Pretreatment		 波段筛选方法
Band selection method		 特征波段数
Characteristic bands number		 特征波长
Characteristic wavelength (mm)
Original spectrum Pearson 10 651、652、653、654、655、656、657、658、659、660
SPA 12 358、391、405、444、492、533、595、700、724、778、991、1067
CARS 18 393、394、395、397、398、503、504、506、508、509、666、699、734、762、815、896、898、1097
MSC Pearson 10 618、619、620、621、622、623、624、625、626、627
SPA 10 455、517、609、674、735、762、920、933、954、1070
CARS 11 510、512、513、660、664、703、714、904、932、951、986
SG Pearson 10 659、660、661、662、663、664、665、666、667、668
SPA 10 350、422、550、607、678、696、722、760、937、1001
CARS 16 390、391、503、506、656、658、659、661、693、694、723、815、904、905、945、1085
FD Pearson 10 360、361、374、379、461、868、882、911、970、998
SPA 10 375、380、386、395、424、691、779、870、892、998
CARS 18 353、354、355、357、360、370、519、656、779、923、924、926、952、957、965、994、996、1013

Table 3

Analysis of the effect of PLSR on the estimation of LNC in chili peppers"

预处理
Pretreatment		 特征选择
Feature selection		 训练集 Calibration set 验证集 Validation set
决定系数
R2		 均方根误差
RMSE (g·kg-1)		 决定系数
R2		 均方根误差
RMSE (g·kg-1)		 相对分析误差
RPD
Original spectrum Pearson 0.22 5.08 0.21 3.21 1.16
SPA 0.22 5.09 0.27 3.09 1.21
CARS 0.53 3.94 0.51 2.54 1.47
MSC Pearson 0.22 5.07 0.18 3.27 1.15
SPA 0.40 4.46 0.38 2.85 1.31
CARS 0.39 4.50 0.28 3.06 1.22
SG Pearson 0.85 2.22 0.57 2.37 1.58
SPA 0.97 1.01 0.88 1.26 2.97
CARS 0.98 0.80 0.93 0.93 4.02
FD Pearson 0.59 3.69 0.40 2.81 1.33
SPA 0.82 2.41 0.75 1.81 2.07
CARS 0.91 1.77 0.74 1.84 2.04

Fig. 6

Validation of LNC estimation model for chili peppers based on PLSR algorithm a, b, c, and d represent the original spectrum and the spectrum after Multivariate scattering correction, Savitzky-Golay, and First derivative preprocessing, respectively. The same as below"

Table 4

Analysis of the effect of RF on LNC estimation of chili peppers"

预处理Pretreatment 特征选择
Feature selection		 训练集 Calibration set 验证集 Validation set
决定系数
R2		 均方根误差
RMSE (g·kg-1)		 决定系数
R2		 均方根误差
RMSE (g·kg-1)		 相对分析误差
RPD
Original spectrum Pearson 0.32 4.74 0.18 3.28 1.14
SPA 0.65 3.39 0.35 2.91 1.29
CARS 0.67 3.30 0.34 2.94 1.27
MSC Pearson 0.40 4.45 0.37 2.86 1.31
SPA 0.58 3.72 0.37 2.87 1.31
CARS 0.54 3.90 0.32 2.98 1.26
SG Pearson 0.65 3.39 0.65 2.14 1.75
SPA 0.73 2.97 0.66 2.09 1.79
CARS 0.67 3.30 0.63 2.20 1.70
FD Pearson 0.74 2.93 0.52 2.50 1.49
SPA 0.77 2.74 0.75 1.82 2.05
CARS 0.89 1.93 0.73 1.88 1.99

Fig. 7

Model validation of LNC estimation for chili peppers based on RF algorithm"

Table 5

Analysis of the effect of RBFNN on LNC estimation of chili peppers"

预处理Pretreatment 特征选择
Feature selection		 训练集 Calibration set 验证集 Validation set
决定系数
R2		 均方根误差
RMSE (g·kg-1)		 决定系数
R2		 均方根误差
RMSE (g·kg-1)		 相对分析误差
RPD
Original spectrum Pearson 0.34 4.67 0.37 3.52 1.06
SPA 0.73 2.97 0.56 3.26 1.15
CARS 0.58 3.74 0.43 2.86 1.31
MSC Pearson 0.31 4.76 0.52 2.66 1.41
SPA 0.44 4.30 0.44 2.88 1.30
CARS 0.37 4.57 0.40 2.88 1.29
SG Pearson 0.92 1.61 0.81 1.92 1.95
SPA 0.98 0.62 0.98 1.21 3.08
CARS 0.99 0.38 0.97 1.18 3.17
FD Pearson 0.59 3.66 0.48 3.01 1.24
SPA 0.83 2.37 0.89 2.08 1.80
CARS 0.95 1.33 0.88 1.91 1.96

Fig. 8

Validation of LNC estimation model for chili peppers based on RBFNN algorithm"

Fig. 9

Five-fold cross validation results of SG-SPA-RBFNN model"

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