
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

71590
10.1038/s41598-024-71590-1
Article
Determination of leaf nitrogen content in apple and jujube by near-infrared spectroscopy
Bao Jianping 1
Yu Mingyang 1
Li Jiaxin 1
Wang Guanli 1
Tang Zhihui 2
Zhi Jinhu zjhzky@163.com

3
1 https://ror.org/05202v862 grid.443240.5 0000 0004 1760 4679 College of Horticulture and Forestry Science, Tarim University, Alar, 843300 Xinjiang People’s Republic of China
2 https://ror.org/023cbka75 grid.433811.c 0000 0004 1798 1482 Institute of Mechanical Equipment, Xinjiang Academy of Agricultural Sciences, Shihezi, 832000 Xinjiang People’s Republic of China
3 https://ror.org/05202v862 grid.443240.5 0000 0004 1760 4679 College of Agriculture, Tarim University, Alar, 843300 Xinjiang People’s Republic of China
6 9 2024
6 9 2024
2024
14 2088412 3 2024
29 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The nitrogen content of apple leaves and jujube leaves is an important index to judge the growth and development of apple trees and jujube trees to a certain extent. The prediction performance of the two samples was compared between different models for leaf nitrogen content, respectively. The near-infrared absorption spectra of 287 apple leaf samples and 192 jujube leaf samples were collected. After eliminating the outliers by Mahalanobis distance method, the remaining spectral data were processed by six different preprocessing methods. BP neural network (BP), random forest regression (RF), least partial squares (PLS), K-Nearest Neighbor (KNN), and support vector regression (SVR) were compared to establish prediction models of nitrogen content in apple leaves and jujube leaves. The results showed that the determination coefficient (R2), root mean square error (RMSE) and residual prediction deviation (RPD) of the models established by different combined pretreatment methods were compared among the five methods. Compared with the performance of the other four models, the modeling method of SG + SD + CARS + RF was suitable for the prediction of nitrogen content in apple leaves, and its modeling set R2 was 0.85408, RMSE was 0.082188, and RPD was 2.5864. The validation set R2 is 0.75527, RMSE is 0.099028, RPD is 2.1956. The modeling method of FD + CARS + PLS was suitable for the prediction of nitrogen content in jujube leaves. The modeling set R2 was 0.7954, RMSE was 0.14558, and RPD was 2.4264; the validation set R2 is 0.81348, RMSE is 0.089217, and RPD is 2.4552.In the prediction modeling of apple leaf nitrogen content in the characteristic band, the model quality of RF was better than the other four prediction models. The model quality of PLS in predictive modeling of nitrogen content of jujube leaves in characteristic bands is superior to the other four predictive models, These results provide a reference for the use of near-infrared spectroscopy to determine whether apple trees and jujube trees are deficient in nutrients.

Keywords

Apple
Jujube
Leaf
Nitrogen content
Near infrared spectroscopy
Machine learning
Subject terms

Plant sciences
Mathematics and computing
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Apple and jujube have a variety of vitamins and mineral elements whchich were needed by the human body1,2. Apple and jujube trees are popular with consumers and have broad market prospects, making them the main fruit tree species in southern Xinjiang. The growth, development, fruit yield, and quality of these trees are closely linked to their nutrition.

Nitrogen plays a crucial role in plant growth and development3, facilitating the growth of new shoots in fruit trees4, enhancing photosynthetic efficiency5, decreasing flower and fruit drop6, and speeding up fruit enlargement7. As fruit trees grow, different organs undergo changes in response to the surrounding environment. Among these organs, leaves are particularly sensitive to variations in nutritional conditions8. The nitrogen content in leaves serves as a key indicator for assessing the tree's nutritional and growth status9.

Currently, the Kjeldahl method10, Coomassie brilliant blue method11, and biuret method12 are commonly used for determining total nitrogen. These traditional methods involve converting nitrogen-containing organic compounds into ammonium nitrogen through complex high-temperature decomposition reactions, providing accurate and reliable data13. However, they are time-consuming, labor-intensive, and involve the use of harmful chemical reagents14. As a result, visible near-infrared spectroscopy was employed to develop a rapid, efficient, and non-invasive method for predicting nitrogen content in apple and jujube leaves, allowing for a quick assessment of the growth status of fruit trees.

Since the 1990s, near infrared spectroscopy (NIR) has rapidly developed and become a crucial modern rapid analysis technique. Despite the complex nature of equipment detection principles and the utilization of various technical advancements, NIR has emerged as a primary method for assessing different parameters due to its quick detection capabilities. Its key advantages include fast detection speed, convenient data output, qualitative and quantitative results that meet general requirements through auxiliary calculations, easy operation, and no harm to contaminated samples15. Near infrared spectroscopy has found extensive applications in leaf nutrient studies and other areas. For instance, Yang et al.16 utilized near-infrared (NIR) hyperspectral imaging to non-destructively and effectively evaluate nitrate content in spinach (Spinacia oleracea L.) leaves. The mapping results demonstrated the dynamic changes in nitrate content in intact leaf samples under varying storage conditions, highlighting the potential of this non-destructive detection tool for future analysis of nitrate content in vegetables. Most research on establishing detection models using near-infrared spectroscopy has focused on crops like wheat17 and vegetables18. However, there have been fewer studies on fruit tree cultivation. This study aimed to create models for detecting nitrogen content in apple and jujube leaves using near-infrared spectroscopy. This detection can help determine if the tree is nitrogen deficient and make timely decisions accordingly.

When acquiring spectral data, various noises and outliers may be present due to factors such as instrument noise and sample inhomogeneity, which can impact subsequent data analysis and model establishment. As a result, many researchers not only analyze raw spectral data in depth but also consider spectral preprocessing19. For instance, Zhang et al.20 utilized hyperspectral data in combination with the SVM algorithm to develop a classification model for salted sea cucumbers. They employed four preprocessing methods (SG, MSC, SVN, and VSN) to filter out noise and scattering information from the original spectrum, resulting in improved data quality. In this experiment, the MSC preprocessing method was applied to address spectral dispersion issues, while the FD and SD preprocessing methods were used to eliminate baseline interference and enhance resolution and sensitivity. To address various issues such as baseline interference, discoloration, and noise, different preprocessing methods were combined. For example, FD was combined with MSC to reduce diffuse reflection and baseline effects, SG was combined with SD to eliminate noise and baseline effects, and SNV was combined with SD to reduce dispersion effects and baseline effects. Ultimately, three combined preprocessing methods were derived: MSC + FD, SG + SD, and SNV + SD. In summary, the six pretreatment methods employed in this experiment include MSC, FD, SD, MSC + FD, SG + SD, and SNV + SD.

Although the spectral data are preprocessed, large amounts of data can reduce model efficiency and lead to overfitting21. Zhu et al.22 used near-infrared spectroscopy with PLS to detect zearalenone (ZEN) content in wheat. They introduced three feature selection models: competitive adaptive reweighted sampling (CARS), support vector machine recursive feature elimination (SVM-RFE), and multi-feature space integrated least absolute shrinkage and selection operator (MFE-LASSO) for preprocessing near-infrared spectrum data. This approach effectively enhances modeling efficiency through feature extraction. CARS addresses class imbalance by dynamically adjusting sample weights based on training sample importance. SVM-RFE gradually reduces features by training the support vector machine model multiple times and selecting features that minimally impact model performance. MFE-LASSO combines multiple feature spaces to select features and uses LASSO to reduce feature dimensionality and enhance model performance. In this experiment, since the output characteristic value is nitrogen content, and the wavelength position of nitrogen atoms in the spectrum generates diverse data due to different chemical bonds and sample processing methods, the CARS algorithm is used to extract spectral characteristic wavelengths.

Jang et al. 23used spectral imaging technology to quickly predict the nitrogen content in leaves. The total nitrogen content of apple trees was estimated by hyperspectral imaging and regression analysis based on machine learning partial least squares regression (PLSR ), support vector regression (SVR ) and Extreme Gradient Boosting ( XGBoost ). The nutritional status of trunk-shaped apple leaves was rapidly analyzed by near-infrared spectroscopy to provide a basis for timely fertilization in apple orchards. In this study, Support Vector Machine and Partial Least Squares were initially employed for model development. Despite feature extraction efforts, the dataset size remained substantial, leading to challenges in identifying a more suitable modeling algorithm promptly. Consequently, in addition to the aforementioned two algorithms, FR, BP, and KNN were also utilized in this study. These three algorithms are better suited for handling larger datasets compared to Support Vector Machine and Partial Least Squares.

This study utilized a combination of chemical methods and near-infrared spectroscopy to analyze the nitrogen content in apple and jujube leaves. The apple leaf dataset consisted of 287 samples, while the jujube leaf dataset had 192 samples. Each sample in both datasets included 1557 near-infrared spectrum scanning data as the characteristic attribute and 1 nitrogen content as the target attribute. Various preprocessing techniques were employed on the spectra, and the CARS algorithm was utilized to identify characteristic wavelength sites. By using five different machine learning algorithms, the best models for detecting nitrogen content in apple and jujube leaves were developed respectively, facilitating the understanding of tree health and enabling timely and appropriate cultivation and management practices. This research provides valuable insights for the scientific cultivation of fruit trees.

Material and methods

Overview of the test site

The study utilized 6-year-old 'Yanfu No.3' apples from the apple orchard of the fifth regiment of the first division of Xinjiang, along with 6-year-old jujubes from the jujube orchard of the ninth regiment of the first division of Xinjiang as test subjects. The apple orchard had a row spacing of 1.8 m × 3.5 m, oriented in the east–west direction, with begonia rootstock and a trunk fruit tree shape. The trees had a height of 3.5 m, a crown diameter of 1.25 ~ 1.75 m, and a 3.5 m wide working road. The soil was described as clay, flat, and deep. The jujube orchards primarily consisted of trunk-shaped jujubes, with a plant-row spacing of 1.5 m x 3 m, oriented north–south, using sour jujube rootstocks, and sandy loam soil. Both the apple and jujube trees were irrigated.

Test method

Different ratio fertilization test scheme

The experimental design consisted of mixed and fertilizer-only treatments, each divided into 16 treatment groups. The processing method is outlined in Table 1: The mixed treatment comprised 7 groups (1, 2, 3, 4, 5, 6, 7), while the pure fertilizer treatment included variations in nitrogen fertilizer application (8, 9, 10), phosphorus fertilizer application (11, 12, 13), and potassium fertilizer application (14, 15, 16).Table 1 Fertilization treatment grouping.

Processing number	One set of single fertilization dosage/(g · plant-1)			
	CH4N2O	P2O5	K2O	
1	524.57	326.08	588.23	
2	460.77	489.13	588.23	
3	396.97	652.17	588.23	
4	524.57	326.08	882.35	
5	524.57	326.08	1029.41	
6	524.57	326.08	1176.46	
7	524.57	326.08	441.17	
8	326.08			
9	652.17			
10	978.26			
11		326.08		
12		652.17		
13		978.26		
14			294.12	
15			588.23	
16			882.35	

Apple trees are fertilized three times at different stages of growth: during the bud break period (S1 phase, March 30th), the young fruit enlargement period (S2 phase, June 15th), and the rapid fruit enlargement period (S3 phase, July 20th).

Jujube trees, particularly the 'gray jujube' variety, undergo fertilization at three key growth stages: prior to bud emergence (April 15th, S1 period), while young fruits are expanding (May 30th, S2 period), and during the rapid fruit enlargement phase (July 20th, S3 period).

Spectral acquisition of apple leaves and jujube leaves

In order to obtain more accurate spectral data, the measured leaves were dusted, and the Fourier Transform Infrared Spectrometer (Antaris II FT-NIR, Thermo fisher, WI, USA) was used to scan these spectra. After diffuse reflection correction, the leaves were fixed, and the leaves were measured with the veins as the boundary. Leaves were measured at six different areas, selected at the upper, middle, and lower ends on both the left and right sides of the leaf.The different numbers and spectral curves collected in each region were distinguished by different colors. Each region was scanned three times, and the collection range was 10 000 ~ 4 000 cm-1; the resolution is 8 cm-1; the gain is 2x ; scan 32 times(Fig. 1-A).Fig. 1 Flowchart of the test (Note: A is the flowchart of data acquisition, and the red circle marks the location of the sample or data acquisition; B is the flowchart of modeling; C is the important parameter settings of the relevant algorithms).

Determination of leaf nitrogen content

Nitrogen content in leaves was determined using the micro Kjeldahl method24. A 1.0 g dried sample was placed in a digestion tube and concentrated sulfuric acid was added for digestion. Once the solution turned uniformly black, hydrogen peroxide was added to ensure even mixing and digestion continued until the color lightened. This process was repeated 3 to 5 times, decreasing the amount of hydrogen peroxide with each repetition until the solution clarified. Heating was continued until excess hydrogen peroxide was removed, followed by cooling. The digested solution was then transferred to a 100 mL volumetric flask, allowed to settle, and the supernatant was extracted. Subsequently, either 5.0 mL of the digested solution was quantitatively extracted with a pipette and distilled in a semi-micro distiller, or 15.0 mL of the solution was distilled in a 150 mL Kjeldahl flask to form ammonium salts under alkaline conditions. The distilled solution was then absorbed with boric acid, and the endpoint of titration was determined using a mixture of bromocresol green and methyl red indicators, followed by standard acid titration.

Outlier elimination and sample set division

The modeling process is illustrated in Fig. 1B. In this experiment, the Mahalanobis distance method was applied to remove outliers. This method, a statistical measure, assesses the deviation of sample points from the distribution center, particularly in high-dimensional spaces. It considers correlations between features, not solely the absolute differences of each feature. The primary formula for the Mahalanobis distance method is as follows:1 MD(K)=(XK-μ)T∑-1(XK-μ)

where MD(k) is the martensitic distance of different spectral curves in k-band; Xk is the difference matrix of different spectral curves in k-band; μ is the mean vector; and Σ-1 is the covariance matrix.

The sample set partitioning based on joint x–y distance (SPXY) algorithm is utilized for sample set division. The algorithm begins by selecting the first centroid randomly from all data points during initialization. Subsequent centroid selection is based on the square of its average XY distance to the currently selected centroid, preventing bias towards clusters with higher dispersion initially. The main formula of the SPXY algorithm is as follows:2 dx(p,q)=∑J=1J[xP(j)-xq(j)]2),(p,q∈[1,N])

3 dy(p,q)=(yp-yq)2,(p,q∈[1,N])

4 dxy(p,q)=dx(p,q)maxp,q∈[1,N]dx(p,q)+dy(p,q)maxp,q∈[1,N]dx(p,q)

Equation (3) where J denotes the number of bands in the spectrum. xp(j) and xq(j) denote the values of the spectral reflectance of the two samples p,q in the Jth band, respectively, and N is the total number of samples. dx(p,q) stands for the Euclidean distance of the two samples in the x-space (spectral eigenspace).

In Eq. (4), yp and yq denote the physicochemical eigenvalues of the samples to be described, respectively, and are the Euclidean distances of the two samples in y-space.

In Eq. (5) dxy(p,q) is shown to consider the Euclidean distance in both spaces, which represents the maximum value of the Euclidean distance in x and y spaces for two samples, p and q, respectively.

Spectral preprocessing procedures

Utilize the derivative method to analyze the near-infrared spectrum of the sample for enhanced removal of baseline interference, as well as improved resolution and sensitivity25. Light scattering correction is mainly aimed at the image difference of the uneven particle size distribution of the sample in the diffuse reflection data sampling32.

In this study, six pretreatment methods were applied to the two samples: multiple scattering correction (MSC), first derivative (FD), multiple scattering correction + first derivative (MSC + FD), second derivative (SD), S-G smoothing + second derivative (SG + SD), standard normal variable transformation + second derivative (SNV + SD). The parameters for each algorithm are detailed in Fig. 1C.

Modeling method

As shown in Fig. 1C, In this experiment, five models were used for the two samples, namely BP neural network ( BP ), random forest regression ( RF ), Partial Least Squares ( PLS ), K-Nearest Neighbor ( KNN ), Support vector regression ( SVR ).

Data processing

The spectral information was preprocessed by MATLAB R2023a software, and the outliers were eliminated. The mathematical model of near infrared spectrum and chemical content was established. The chemical value and spectral mean value were calculated by Excel 2016, and Origin2021 was used for drawing.

Results and analysis

Analysis of nitrogen content in apple leaves and jujube leaves

The leaf nitrogen content was analyzed using the micro Kjeldahl nitrogen determination method. The results revealed significant differences in the average nitrogen content of apple leaf and jujube leaf samples at each stage under every treatment, as depicted in Fig. 2. The differences observed were distinct and representative. Specifically, Fig. 2a illustrates the comparison of average nitrogen content in apple leaf samples across different periods and treatments, while Fig. 2b displays the comparison for jujube leaf samples. These figures emphasize noteworthy variations in mean nitrogen content between the two sample groups, providing valuable insights for model development.Fig. 2 Mean values of nitrogen content of apple and jujube leaves for each period under each treatment (Note: a is the mean value of nitrogen content of apple leaves for each period under each treatment; b is the mean value of nitrogen content of jujube leaves for each period under each treatment).

Near infrared spectroscopy analysis of apple leaves and jujube leaves

The near-infrared spectrum is primarily produced when molecular vibrations transition from the ground state to a higher energy level, as a result of non-resonance in molecular vibrations. This process mainly captures the frequency doubling and combined frequency absorption of the X—H (X═C, N, O) vibration in hydrogen-containing groups. By analyzing the near-infrared spectra absorbed by a sample, valuable information about different functional groups can be obtained, offering deeper insights into the nature and quality characteristics of the sample. Consequently, the attribution or content of functional groups can be swiftly assessed based on the band position and intensity of the absorption peak displayed in the spectrum33,34. Figures 3 and 4 illustrate that both apple leaves and jujube leaves exhibit distinct absorption peaks within the range of 4000 to 10,000 cm-1. Specifically, prominent absorption peaks are observed in the regions of 6039.96 to 7613.59 cm-1 and 4493.33 to 5430.36 cm-1. Additionally, there are three absorption peaks showing slight fluctuations in the ranges of 7613.59 to 8801.52 cm-1, 5430.36 to 6039.96 cm-1, and 4312.05 to 4493.33 cm-1.Fig. 3 Original spectra of 6-year-old ' Yanfu 3 'Apple leaves.

Fig. 4 Original spectrum of 6-year-old jujube leaves.

Eliminating abnormal samples

In order to enhance the stability and accuracy of the nitrogen content prediction model and minimize the impact of measurement errors35, this study employed the Mahalanobis distance36 method to identify and remove outliers in the spectral data of apple and jujube leaves. Specifically, 5 outliers were eliminated from the apple leaf spectrum data and 4 outliers from the jujube leaf spectrum data. As a result, 282 apple leaf samples and 188 jujube leaf samples were ultimately retained for further analysis.

Division of sample set results

In this study, a total of 282 apple leaf samples and 188 Gy jujube leaf samples were included after removing outliers. The samples were then divided using the SPXY algorithm, which splits the samples into calibration and validation sets at a 3:1 ratio. The SPXY algorithm, a statistical-based sample partitioning method, is known for its effectiveness in covering multidimensional vector space, thereby enhancing the prediction accuracy of the model31. Following this ratio, the calibration set for apple leaf samples consisted of 212 samples, while the validation set had 70 samples. Similarly, the calibration set for jujube leaf samples comprised 141 samples, with the validation set containing 47 samples. The input data included nitrogen content and spectral data for both apple and jujube leaf samples. For apple leaf samples, the output consisted of 212 sets of sample chemical value content and corresponding spectral data in the calibration set, and 70 sets in the validation set. For jujube leaf samples, the output included 188 sets of sample chemical value content and corresponding spectral data in the calibration set, and 47 sets in the validation set. The SPXY algorithm was used to partition the spectra of apple and jujube leaves. The results in Table 2 show that the range of sample content in the calibration set for both types of leaves is wider than that in the validation set, supporting the rationale behind the division of samples into calibration and validation sets.Table 2 Statistics on the content of each leaves in apple and jujube leaves samples and the results of sample group division.

Species	Sample	Number of samples	Maximum value/(mg·g-1)	Minimum value/(mg·g-1)	Mean value/(mg·g-1)	Standard deviation	
apple leaves	Overall	282	2.85	1.61	2.16	0.21	
	Sample correction set	212	2.85	1.61	2.15	0.24	
	Sample validation set	70	2.48	1.73	2.18	0.12	
jujube leaves	Overall	188	3.99	2.12	2.89	0.32	
	Sample correction set	141	3.99	2.12	2.88	0.35	
	Sample validation set	47	3.24	2.33	2.91	0.20	

Selection of spectral pre-processing and extraction of characteristic wavelengths

Selection of spectral pre-processing

Six different combinations of pretreatment methods were applied to the spectra of both apple leaves and jujube leaves. Following preprocessing, a predictive model using randomized forest regression was developed to estimate nitrogen content in apple leaves, while a least squares nitrogen content prediction model was created for jujube leaves. The selection of spectral preprocessing techniques and model establishment methods was based on the determination coefficient R2 and root mean square error parameters of both the calibration and validation sets. A smaller difference between the root mean square errors (RMSEC and RMSEP) and a determination coefficient R2 closer to 1 indicate a more significant correlation effect26–28. Additionally, a model with a smaller difference between RMSEC (RMSE of calibration set) and RMSEP (RMSE of validation set) demonstrates better robustness29,30. The residual prediction deviation (RPD) serves as a measure of the model's predictive ability, with a higher RPD indicating better performance. Figure 5 presents the results of spectral pre-processing for apple leaves and jujube leaves. The combination of SG + SD for apple leaf spectrum showed higher R2 values for the calibration set (0.85153) and test set (0.75624) compared to other methods, with RMSE values of 0.082903 and 0.098832, respectively. Upon comprehensive comparison, the SG + SD combination was identified as the optimal pretreatment method for apple leaves. For jujube leaf spectrum, the FD pretreatment method was compared with others, resulting in R2 values of 0.7954 and 0.81348 for the calibration set and test set, with RMSE values of 0.14558 and 0.089217, respectively.Fig. 5 Modeling results of different pretreatment methods of apple leaves and jujube leaves.

Comparison of the six pre-processed apple leaves spectra (Fig. 6-A, C, E, G, I, K) and the original apple leaf spectrum (Fig. 3) reveals notable differences. Similarly, a comparison of the spectrum of six pre-treated jujube leaves (Fig. 7-A, C, E, G, I, K) with the spectrum of original jujube leaves (Fig. 4) highlights distinct changes. Upon visual inspection, it is evident that the original spectrum of apple leaves post SG + SD pretreatment and the original spectrum of jujube leaves post FD pretreatment exhibit significant alterations. Specifically, spectral noise is reduced and peaks are more pronounced compared to the original images.Fig. 6 Six kinds of spectral preprocessing images of apple leaves and their characteristic wavelength extraction images. Note: A, C, E, G, I, and K refer to different preprocessing methods used in spectral data analysis. A represents MSC preprocessing (MSC), C represents FD preprocessing (FD), E represents a combination of MSC + FD preprocessing (MSC + FD), G represents SD preprocessing (SD), I represents SG + SD preprocessing (SG + SD), and K represents SNV + SD preprocessing (SNV + SD). B, D, F, H, J, and L represent the extraction of characteristic wavelengths in various preprocessing techniques applied to spectra. These techniques include MSC, FD, MSC + FD, SD, SG + SD, and SNV + SD.

Fig. 7 Six kinds of spectral preprocessing images of jujube leaves and their characteristic wavelength extraction images. Note: A, C, E, G, I, and K refer to different preprocessing methods used in spectral data analysis. A represents MSC preprocessing (MSC), C represents FD preprocessing (FD), E represents a combination of MSC + FD preprocessing (MSC + FD), G represents SD preprocessing (SD), I represents SG + SD preprocessing (SG + SD), and K represents SNV + SD preprocessing (SNV + SD). B, D, F, H, J, and L represent the extraction of characteristic wavelengths in various preprocessing techniques applied to spectra. These techniques include MSC, FD, MSC + FD, SD, SG + SD, and SNV + SD.

Extraction of characteristic wavelength

The Competitive Adaptive Reweighted Sampling method (CARS) is utilized for wavelength extraction. This method involves feature variable selection by combining Monte Carlo sampling with PLS model regression coefficients. It mimics the concept of 'survival of the fittest' in Darwin's theory, progressing from rough selection to fine selection36–38. Figures 8 and 9 are the original spectral data of apple leaves and jujube leaves using CARS operation to select the characteristic wavelength results. It can be seen from the figure that the number of sampling times in this experiment is set to 100 times. It can be seen from the change trend diagram of variables that at first, when the sampling times of apple leaves and jujube leaves are low, the modeling variables are large. With the increase of sampling times, the modeling variables decrease. Finally, with the increase of sampling times, the amplitude of variables gradually flattens out. According to the images of sampling times and RMSECV, when the spectra of apple leaves and jujube leaves were extracted, the value of RMSECV decreased first and then increased slowly with the increase of sampling times. When the sampling times of apple leaves were 60 times, the minimum value of RMSECV was 0.0000273981. The minimum value of RMSECV was 0.0000253853 when the jujube leaf samples were sampled 62 times. It can be seen from the number of sampling times and the regression coefficient value image that the apple leaf and the jujube leaf samples have a star vertical line at 60 times and 62 times respectively, indicating that the root mean square error was minimized at that location. The experiment effectively extracted characteristic wavelengths using the CARS algorithm in preprocessed images of apple leaves, with 1557 wavelength points in each image. The number of characteristic wavelength sites obtained in the processed images were 11, 34, 5, 38, 26, and 41 for MSC, FD, SD, MSC + FD, SG + SD, and SNV + SD, respectively (Fig. 6-B, D, F, H, J, and L). Similarly, for jujube leaves, the number of characteristic wavelength sites obtained in the preprocessed images were 8, 7, 31, 7, 36, and 20 for MSC, FD, SD, MSC + FD, SG + SD, and SNV + SD, respectively (Fig. 7-B, D, F, H, J, and L).Fig. 8 The principle of extracting variables by CARS operation of apple leaves.

Fig. 9 The principle of extracting variables by CARS operation of jujube leav.

Determination and verification of the model

Model validation

Common predictive modeling methods utilized for analyzing nitrogen content in apple and jujube leaves include RF (Random Forest), BP (BP neural network), PLS (Partial Least Squares), KNN (K-Nearest Neighbor), and SVR (Support Vector Regression). The results of the apple leaves nitrogen content modeling, as depicted in Fig. 10, indicate that the pretreatment method achieved a validation set determination coefficient R2 of 0.75624, a prediction root mean square RMSE of 0.098832, and a residual prediction deviation RPD of 2.1048. The PLS model with FD pretreatment method was found to be more suitable for analyzing the near-infrared spectrum of jujube leaves. The optimal results of the nitrogen content model for jujube leaves, illustrated in Fig. 11, reveal a validation set determination coefficient R2 of 0.81348, a prediction root mean square RMSE of 0.089217, and a residual prediction deviation RPD of 2.4552. It is commonly accepted that a model is deemed unreliable when RPD is less than 1.4, moderately reliable when RPD falls between 1.4 and 2.0, and highly reliable when RPD exceeds 2.0, making it suitable for in-depth model analysis39.Fig. 10 The results of apple leaf near infrared under RF model.

Fig. 11 The results of jujube leaf near infrared under PLS mode.

The experimental results showed that the validation set achieved an R2 of 0.81348 and an RMSE of 0.089217. The highest RPD value of 2.4552 was obtained in the prediction model when using various spectral pretreatments for jujube leaves. Based on these findings, it is recommended to model apple leaves using the SG + SD + CARS + RF combined method, while jujube leaves should be modeled using the FD + CARS + PLS combined approach for optimal results. Calibration sets for apple and jujube leaves are depicted in Fig. 12A,C, respectively, with the x-axis representing measured values and the y-axis representing predicted values. The correlation between measured and predicted values can be observed in the figures. The determination coefficients R2 were calculated to be 0.90987 and 0.82893, respectively, indicating a high level of stability in the reduced model.Fig. 12 Correlation between measured value and predicted value (A: The correlation between the measured value and the predicted value of the apple leaf correction set ; B : The correlation between the measured value and the predicted value of the apple leaf validation set; C : The correlation between the measured value and the predicted value of the jujube leaf correction set; D : Correlation between measured value and predicted value of jujube leaf validation set).

Model verification

Based on the data presented in Fig. 13A, it is evident that the absolute error between the measured and predicted values of nitrogen content in apple leaves falls within the range of 0.3. The determination coefficient for the predicted values is R2 = 0.80772, with RMSE = 0.099028 and RPD = 2.1956 (Fig. 12B), suggesting that the random forest (RF) model is more accurate for predicting nitrogen content in apple leaf samples. This model demonstrates stability and precision in predicting nitrogen levels in apple leaf samples. Notably, the absolute error for one group of validation set samples in jujube leaves was 0.379546549, while the remaining groups had absolute error values within the 0.3 range (Fig. 13B). The coefficient of determination for the measured and predicted values of the validation set is R2 = 0.83934, with RMSE = 0.089217 and RPD = 2.4552 (Fig. 12D). The Partial Least Squares (PLS) method proves to be more accurate in predicting nitrogen content in jujube leaf samples, with a stable model that enhances the precision of nitrogen content predictions.Fig. 13 Absolute deviation between measured and predicted values of the validation set (Note: A is the absolute deviation between the measured value and the predicted value of apple leaves based on the SG + SD + CARS + RF algorithm combination model; B is the absolute deviation between the measured value and the predicted value of jujube leaves based on the FD + CARS + PLS algorithm combination model.

Discussion

Methodological considerations and practical issues

In recent years, the continuous advancement of science and technology has led to the widespread use of non-destructive testing technology in assessing the nutritional status of fruit trees. Near-infrared spectroscopy, as opposed to traditional chemical detection methods, offers advantages such as safety, minimal environmental impact, cost-effectiveness, and efficiency40. This method is particularly useful in determining the main elements present in plant leaves. Studies on nitrogen content in citrus41 and cucumber leaves42 have successfully utilized near-infrared spectral models to accurately and quickly predict element content. Li et al.43 conducted research on nitrogen detection in cotton canopy leaves using infrared technology, resulting in the establishment of a mathematical model with an impressive R2 value of 0.8363 and an RMSE of 0.018767. This innovative approach presents a promising method for the rapid assessment of nitrogen nutrition in plant leaves. Wang et al.44 developed a model utilizing near-infrared spectral information of nitrogen elements in pear leaves. The calibration set yielded an R2 value of 0.86, while the validation set showed an R2 value of 0.85, providing a solid basis for non-destructive diagnosis of nitrogen nutrition in pear trees. The presence of abnormal samples during model operation can impact prediction model performance. Lin et al.45 investigated the hyperspectral reflectance of Populus euphratica and other trees in the lower reaches of the Tarim River and the lower reaches of the Turpan Desert Botanical Garden. They employed the Mahalanobis distance method to identify significant difference bands between the original spectrum and the transformed spectrum of the measured tree species. Additionally, the stepwise discriminant method was utilized to evaluate the recognition effect of the selected difference bands. The findings demonstrated that the Mahalanobis distance method serves as an effective feature band extraction technique, enhancing the recognition accuracy of the preprocessing method. The elimination of abnormal samples can significantly enhance the predictive capability of the model, underscoring the importance of removing abnormal value samples during model establishment. In this research, the Mahalanobis distance method was applied to eliminate 5 abnormal samples out of 287 samples of apple leaves and 4 abnormal samples out of 192 samples of jujube leaves, facilitating preprocessing and model development.

The spectral signal of the sample can significantly impact the qualitative and quantitative analysis results by being affected by stray light, noise, baseline drift, and other factors. Therefore, it is crucial to mitigate these interferences before modeling. To effectively minimize these effects, enhance the signal-to-noise ratio, and create a more dependable model, preprocessing the collected spectral information is essential46. Gao et al.47 developed a model for detecting apple soluble solids content (SSC) using near-infrared spectroscopy combined with Partial Least Squares-Linear Discriminant Analysis (PLS-LDA). They applied SG, MSC, SNV, FD, and SD preprocessing algorithms to the original spectra, with SG and SNV proving to be the most effective. This research highlights the importance of preprocessing spectral information in improving model development. A comparison of six pretreatment methods for apple and jujube leaves indicated that the optimal combination for apple leaf samples was SG + SD, while FD yielded the best results for jujube leaf samples.

The near-infrared spectral data points of apple leaves and jujube leaves were 1557 and 287, and 192 samples, respectively. During the modeling process, significant collinearity was observed due to the large amount of near-infrared data, which could potentially lead to subpar modeling outcomes. To address this issue, wavelength extraction was deemed necessary. In a study by Luo et al.35 the CARS method was employed to extract characteristic wavelengths, revealing the N—H overtone range associated with protein values to be between 5754.5–7864.0, 9075 ~ 9450 cm-1. This experiment yielded similar results, pinpointing a protein-related group —CONH2 within the range of 5430.36 ~ 7613.59, 9075 ~ 9450 cm-1.

Azadnia et al.48 collected Vis/NIR spectra of apple leaves and used the chemical content of N, P, and K elements as a reference point. Various pretreatment techniques were applied to preprocess the spectra, The study revealed that the prediction model established by MSC + SD pretreatment and RF exhibited the most accurate estimation for nitrogen (N), phosphorus (P), and potassium (K) elements. This finding aligns with the optimal prediction model RF identified through comparison with apple leaf samples in this experiment. On the other hand, Tuerxun et al.49 utilized jujube leaves as test samples and employed the least absolute shrinkage and selection operator (LASSO) and elastic network (EN) to identify important bands in the original spectrum (OS), first derivative spectrum (FD), and second derivative spectrum (SD). Support vector regression (SVR) was utilized to establish the estimation model. The results estimation model constructed based on LASSO/EN using FD and SD exhibited excellent R2 values, with SD-EN-SVR being identified as the most optimal model combination in this experiment. Furthermore, a comparison of five models including RF, BP, PLS, KNN, and SVR revealed that PLS was more suitable for developing the prediction model of nitrogen content in jujube leaves, possibly due to variations in the substances measured leading to differences in the optimal model.

This study focuses on the nutritional needs of apples and dates, both woody plants, highlighting the importance of nitrogen sources for storage reabsorption and root absorption50. The goal is to assist local fruit farmers in monitoring the nutritional status of fruit trees to optimize cultivation and management practices, ultimately enhancing fruit tree growth and fruit development. The study also underscores the significance of non-destructive nitrogen status diagnosis for timely fertilization to improve fruit quality and yield.

Conclusions

The study aims to investigate the potential of using near-infrared spectroscopy and chemometric methods at a wavelength range of 4000–10,000 cm-1 for rapid detection of nitrogen content in apple and jujube leaves. The CARS algorithm extracted characteristic wavelengths primarily related to nitrogen, enhancing modeling efficiency and detection performance. The RF algorithm successfully modeled the SG + SD preprocessed spectral data of apple leaves, yielding strong performance with R2 of 0.75527, RMSE of 0.099028, and RPD of 2.1956. Similarly, the PLS algorithm modeled the FD preprocessed spectral data of jujube leaves with R2 of 0.81348, RMSE of 0.089217, and RPD of 2.4852. The study demonstrates that the prediction algorithm combination model of SG + SD + CARS + RF is effective for predicting nitrogen content in apple leaves, while the model of FD + CARS + PLS is suitable for jujube leaves. Future research will expand to include more fruit tree species to develop a near-infrared rapid detection model applicable to a wider range of fruit trees and explore the creation of a rapid detection system for accurate measurement of nitrogen content in fruit trees at various growth stages.

Supplementary Information

Supplementary Information.

Abbreviations

MSC Multiple Scattering Correction

FD First Order Derivative

SD Second Order Derivative

MSC + FD Multiple Scattering Correction + First Order Derivatives

SG + SD Savitzky-Golay + Second-Order derivatives

SNV + SD Standard Normal Variable Transformation + Second Order Derivative

SPXY Sample set Partitioning based on joint X–Y distance

CARS Competitive Adapative Reweighted Sampling

BP BP neural network

RF Random forest regression

PLS Partial Least Squares

KNN K-Nearest Neighbor

SVR Support vector regression

R2 Coefficient of determination

RMSECV Root Mean Square Error of Cross-Validation

RMSE Root Mean Square Error

RDP Relative Percent Difference

VSN Variable Neighborhood Search

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71590-1.

Acknowledgements

This work was funded by the Major Science and Technology Project of Xinjiang Production and Construction Corps (2021AA00502-02).

Author contributions

J.B.: Design Research Methods; Manuscript Review; Manuscript Editing. M.Y.: Data Analysis; Data Visualization; Manuscript Writing. J.L., G.W.: Experimental Data Collection. Z.T.: Funding Acquisition. J.Z.: Design Research Methodology; Funding Acquisition; Project Supervision; Project Management.

Funding

This study was supported by the Major Science and Technology Project of Xinjiang Production and Construction Corps (grant number 2021AA00502-02).

Data availability

All the data analyzed in this study are included in this article. Since these data are also part of an ongoing research project, the data sets analyzed in this study are not publicly available.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Co-first authors: Jianping Bao and Mingyang Yu.
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