Detection of SPAD Content in Leaves of Grey Jujube Based on Near Infrared Spectroscopy

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors,

 

This manuscript is dealing with the investigation of near infrared spectroscopy application in detection of SPAD content.

 

In the abstract explain abbreviations when you introduce for the first time.

 

In the abstract are missing values of the most important results. Insert it in manuscript.

 

In the subsection 2.1. are missing information regarding agriculture technique applied during the plant growing.

 

Did you use any fertilizers? If yes what kind.

 

Did you use any substances for the protections against pest?

 

What is practical significance of proposed method? Explain it in manuscript.

 

Numbers on the figure 2 are not visible. Correct it.

 

What is the advancement of proposed method in comparison with other? Explain it in manuscript.

 

Conclusion is too long. Make it more direct and do not repeat which you already mentioned in the manuscript before.

Author Response

Dear Reviewer：

Thank you for your letter. First of all, please allow me to extend our team’s sincerest apologies for the oversight in our work, which has caused inconvenience during the review process. We truly appreciate your valuable guidance on our research, as it has provided us with an opportunity to refine our work. In accordance with your suggestions, we have made the following revisions to address the issues in the paper. The detailed feedback is as follows:

 

Question 1：In the abstract explain abbreviations when you introduce for the first time. In the abstract are missing values of the most important results. Insert it in manuscript.

 

Modification and reply 1：Thank you very much for your valuable comments on our manuscript. In the abstract, we have now defined abbreviations at their first occurrence and added specific numerical values for the most important results that were previously missing. The revised abstract reads as follows:Grey jujubeleaf Chlorophyll content efficient, non-destructive inspection is of great significance for its growth surveillance and nutritional diagnosis. Near-Infrared Spectroscopy combined with Chemometric methods provides an effective approach to achieve this goal. This study takes grey jujube leaves as the research object, systematically collected Near-Infrared Spectral data in the range of 4000–10000 cm⁻¹, and simultaneously measured their Soil and Plant Analyzer Development (SPAD) value as a reference index for Chlorophyll content. Through various Pretreatment and their combination methods on the Original spectrum: Smooth、Standard normal variable transformation ( SNV )、First derivative（FD）、Second derivative（SD）、Smooth+First derivative（Smooth+FD）、Smooth+Second derivative（Smooth+SD）、Standard normal variable transformation +First derivative（SNV+FD）、Standard normal variable transformation+Second derivative（SNV+SD）,to compare the effects of different methods on the quality of Spectrum and its Correlation with SPAD value. The Competitive adaptive reweighted sampling algorithm (CARS) was adopted to extract Characteristic wavelength, aiming to reduce data dimensionality and optimize model input. Both BP neural network and RBF neural network prediction models were established, and the Model performance under different training functions was compared.The results indicate that after Smooth+FD Pretreatment, followed by CARS screening of Characteristic wavelength, the BP neural network model trained using the Lbfgs algorithm demonstrated the best performance, with its Coefficient of determination (R²) being 0.87 (Training set) and 0.85 (validation set), Root mean square error (RMSE) being 1.36 (Training set) and 1.35 (validation set), and Residual prediction deviation (RPD) being 2.81 (Training set) and 2.56 (validation set), showing good prediction accuracy and robustness.Research indicates that by combining Near-Infrared Spectroscopy with Feature extraction and Machine learning methods, rapid and non-destructive inspection of grey jujube Leaf SPAD value can be achieved, providing reliable technical support for Real-time monitoring of the Nutritional status of jujube trees.

We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

Question 2：In the subsection 2.1. are missing information regarding agriculture technique applied during the plant growing.

 Did you use any fertilizers? If yes what kind.Did you use any substances for the protections against pest?

 

Modification and reply 2：Thank you very much for your valuable suggestions on our paper. We have incorporated relevant information regarding the agricultural techniques applied during plant growth in subsection 2.1. The experimental site was an orchard with a blank control setup, where neither fertilizers nor any substances for pest and disease control were applied. The added description in subsection 2.1 is as follows: The orchard was irrigated using the Flood irrigation method, and no fertilizers were applied or pest control measures were implemented on the experimental trees. In subsequent studies, we will include samples subjected to different fertilizer applications and pest control treatments for comparison.

We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

Question 3：What is practical significance of proposed method? Explain it in manuscript.

 

Modification and reply 3：Thank you very much for your valuable comments on our manuscript. In the Introduction, we elaborated on the practical significance of the proposed method and highlighted the importance of rapid, non-destructive monitoring of jujube leaf SPAD value—which reflects chlorophyll status—in the context of precision agriculture. The relevant section in the Introduction reads as follows: Although NIR spectroscopy has made significant progress in crop nutrition surveillance, existing research has primarily focused on staple food crops such as rice and wheat, as well as some fruit trees. For the important economic tree species of gray jujube, its leaf structure and biochemical composition may possess uniqueness, making the direct application of models from other crops less universally applicable. Currently, research on the systematic assessment of SPAD value in gray jujube leaves using NIR spectroscopy remains very limited, particularly in exploring the optimal spectral pre-processing workflow, Feature band selection, and the combined effects of different neural network training strategies, where in-depth and systematic reports are still lacking.

Therefore, this study aims to fill this research gap by taking jujube leaves as the research object and systematically investigating the quantitative analysis model of SPAD value based on NIR spectroscopy. The specific objectives of this study include: (1) collecting NIR spectra and SPAD value of jujube leaves to construct a Dataset; (2) comparing the enhancement effects of various spectral pre-processing methods on Model performance; (3) applying the Competitive Adaptive Reweighted Sampling algorithm to screen Characteristic wavelength and optimize model inputs; (4) constructing BPNN and RBFNN models and evaluating the predictive efficacy of different training function; (5) determining the optimal model combination suitable for the non-destructive inspection of SPAD value in jujube leaves. This study is expected to provide a reliable technical solution and theoretical basis for the rapid and non-destructive diagnosis of the nutritional status of jujube.

We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

Question 4：Numbers on the figure 2 are not visible. Correct it.

 

Modification and reply 4：Thank you very much for your valuable feedback on our manuscript. In accordance with your suggestions, we have revised Figure 2 by adding a grid with numerical scales to clearly indicate the position corresponding to each digit. We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

Question 5：What is the advancement of proposed method in comparison with other? Explain it in manuscript.

 

Modification and reply 5：We sincerely appreciate your valuable suggestions for improving our paper. Following your guidance, we have elaborated on the advancements of the proposed method in both the Introduction and Conclusion sections. Currently, NIRSpectrum research is predominantly focused on field crops such as wheat and rice, while its application in other fruit trees remains in the early stages. This study systematically applies NIRSpectrum to detect the SPAD value of a specific tree species—gray jujube—and establishes a high-performance prediction model, thereby providing a theoretical foundation and algorithmic support for the development of a portable nutrient detection device for gray jujube. The revised Introduction and Conclusion are as follows:

Introduction

Gray jujube (Ziziphus jujuba Mill.) is an important economic fruit tree unique to China, with its fruits being rich in nutrients and having broad market prospects (1). In the high-quality and high-yield cultivation management of gray jujube, the plant's Nitrogen nutrition status is a key limiting factor. Chlorophyll, as the core pigment of plant photosynthesis, has its content highly Positive correlation with the leaf nitrogen content, and is therefore regarded as a reliable indicator for assessing plant physiological health status and nitrogen nutrition levels(2, 3). The SPAD-502 chlorophyll meter, by measuring the light transmittance of leaves at specific wavelengths, can quickly and non-destructively obtain the relative chlorophyll content (SPAD value), and has become an important tool for field nutrition diagnosis (4). However, as a point-based Measurement Equipment, the SPAD meter is less efficient in characterizing large-area canopy with spatial heterogeneity, and is difficult to integrate into future high-throughput field phenotyping platforms.Therefore, the development of a new technology capable of achieving rapid and comprehensive surveillance of the nitrogen status in grey jujube is of great significance for realizing its precise fertilization and intelligent management.

Near-Infrared Spectroscopy, as an efficient and environmentally friendly means of non-destructive inspection, has its analytical foundation in the double frequency and combination band vibrations of Hydrogen-containing group such as C-H, O-H, and N-H in organic compounds (5, 6). This technology has been attested to have great potential in the quantitative inversion of plant Leaf Biochemical parameters (such as nitrogen, chlorophyll, and moisture). In recent years, numerous studies have been devoted to applying NIR spectroscopy to estimate the Chlorophyll content of various crops. For example, Zhang et al.(7)successfully used NIR spectroscopy to predict the SPAD value of rice leaves and found that after Standard Normal Variate (SNV) Pretreatment, the Model performance was significantly improved; Li et al.(8)compared multiple Modeling methods in the study of citrus leaves and confirmed the superiority of the machine learning algorithm in such nonlinear problems; Prattana Lopin et al.(9)further effectively screened the Characteristic wavelength related to chlorophyll through the competitive adaptive reweighted sampling algorithm, simplifying the model.These studies have laid a solid foundation for the application of NIR technology in plant nutrition surveillance. However, NIR spectra are susceptible to interference from environmental noise, light scattering, and sample baseline drift. Therefore, selecting appropriate spectral pre-processing methods (such as Smooth, Standard Normal Variate, Derivative processing, etc.) to extract effective information, combined with Characteristic wavelength selection algorithms (such as Competitive Adaptive Reweighted Sampling) to eliminate redundant variables, is key to constructing robust and high-precision quantitative models (10-12).

In terms of Modeling algorithms, machine learning methods capable of handling complex Nonlinear relationships have demonstrated significant advantages. Back Propagation (BP) neural network and radial basis function neural network, among others(13, 14), have been widely applied in the field of spectral analysis due to their powerful function approximation capabilities.Different training algorithms affect both the convergence speed and prediction accuracy by altering the optimization path of the model, and systematic comparison of these algorithms is crucial for constructing the optimal model(15).

Although NIR spectroscopy has made significant progress in crop nutrition surveillance, existing research has primarily focused on staple food crops such as rice and wheat, as well as some fruit trees. For the important economic tree species of gray jujube, its leaf structure and biochemical composition may possess uniqueness, making the direct application of models from other crops less universally applicable. Currently, research on the systematic assessment of SPAD value in gray jujube leaves using NIR spectroscopy remains very limited, particularly in exploring the optimal spectral pre-processing workflow, Feature band selection, and the combined effects of different neural network training strategies, where in-depth and systematic reports are still lacking.

Therefore, this study aims to fill this research gap by taking jujube leaves as the research object and systematically investigating the quantitative analysis model of SPAD value based on NIR spectroscopy. The specific objectives of this study include: (1) collecting NIR spectra and SPAD value of jujube leaves to construct a Dataset; (2) comparing the enhancement effects of various spectral pre-processing methods on Model performance; (3) applying the Competitive Adaptive Reweighted Sampling algorithm to screen Characteristic wavelength and optimize model inputs; (4) constructing BPNN and RBFNN models and evaluating the predictive efficacy of different training function; (5) determining the optimal model combination suitable for the non-destructive inspection of SPAD value in jujube leaves. This study is expected to provide a reliable technical solution and theoretical basis for the rapid and non-destructive diagnosis of the nutritional status of jujube.

Conclusions

This study successfully validated the feasibility of using Near-Infrared Spectroscopy for rapid and non-destructive inspection of SPAD value in gray jujube Leaf. Compared with traditional destructive chemical Measurement methods, this method achieves non-destructive and rapid analysis; compared with the single-point Measurement of SPAD meters, spectroscopy technology more easily enables high-throughput Assessment at the canopy scale.

 

Through systematic comparison, the combination of Smooth with First Derivative and Competitive Adaptive Reweighted Sampling characteristic wavelength selection was determined to be the optimal scheme. On this foundation, the established BP neural network model based on the Lbfgs algorithm achieved the best prediction performance (Validation set Coefficient of determination (R²) > 0.84, RPD > 2.5), confirming the model's excellent accuracy and robustness.

 

In summary, the technical system constructed in this research provides an advanced and reliable solution for the rapid field diagnosis of gray jujube nitrogen nutrition and precision fertilization management.The successful application of this method will directly serve production practice, providing strong technical support for achieving the precision agriculture goals of improving fertilizer utilization efficiency, reducing production costs, decreasing non-point source pollution, and guaranteeing high-quality and high-yield jujube fruits.

We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

Question 6：Conclusion is too long. Make it more direct and do not repeat which you already mentioned in the manuscript before.

 

Modification and reply 6：Thank you very much for your valuable feedback on our manuscript. In accordance with your suggestions, we have revised the conclusion section by removing redundant content already discussed earlier, condensing its length, and enhancing its logical rigor. The findings of this study are now presented more directly and clearly. The revised conclusion is as follows:

Conclusions

This study successfully validated the feasibility of using Near-Infrared Spectroscopy for rapid and non-destructive inspection of SPAD value in gray jujube Leaf. Compared with traditional destructive chemical Measurement methods, this method achieves non-destructive and rapid analysis; compared with the single-point Measurement of SPAD meters, spectroscopy technology more easily enables high-throughput Assessment at the canopy scale.

 

Through systematic comparison, the combination of Smooth with First Derivative and Competitive Adaptive Reweighted Sampling characteristic wavelength selection was determined to be the optimal scheme. On this foundation, the established BP neural network model based on the Lbfgs algorithm achieved the best prediction performance (Validation set Coefficient of determination (R²) > 0.84, RPD > 2.5), confirming the model's excellent accuracy and robustness.

 

In summary, the technical system constructed in this research provides an advanced and reliable solution for the rapid field diagnosis of gray jujube nitrogen nutrition and precision fertilization management.The successful application of this method will directly serve production practice, providing strong technical support for achieving the precision agriculture goals of improving fertilizer utilization efficiency, reducing production costs, decreasing non-point source pollution, and guaranteeing high-quality and high-yield jujube fruits.

We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

 

 

 

Finally, please allow me to express our deepest gratitude on behalf of our team. Your valuable guidance has not only enriched our work but also enabled every member to grow significantly.  

We will always carry your teachings with us in our future endeavors. Wishing you all the best!

Jun kai , Zeng

                                                     October 12, 2025

 

 

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript entitled: ` Detection of SPAD content in leaves of Grey jujube based on near infrared spectroscopy´ (horticulturae-3901030) has an interesting proposal, validating innovative methodology such as NIR technology for estimating SPAD content in leaves of Grey jujube. The number of leaves used for determination and recording is adequate (although the total number of records is not specified); it appears to be four per leaf, but it is unclear whether they use the average for subsequent calibration. The results presented in this manuscript are good, with excellent statistical indicators (such as R² and RPD) for some of the applied treatments, reinforcing the potential of this technology.

The manuscript is well written; the methodology and the results are adequate. Although there are some formatting errors, such as title typology, review the author guide and the journal's paper template. Some comments or questions below I describe to take into account before publication:

-Keywords: some keywords are in the title. Change.

-Line 45: italics are missing in: Ziziphus jujuba

-Introduction:

There is little scientific content in the introduction. The authors describe theoretical concepts of NIR spectroscopy, processing methods for smoothing spectra, application characteristics, and general limitations. However, the authors should conduct a literature search for findings published to date on the estimation or calibration of this parameter in leaves. They should highlight the novelty of the topic addressed based on the scientific knowledge provided. They should provide more information on the importance of estimating the SPAD value.

-Material and Methods

- The number of leaf samples recorded for data acquisition and subsequent statistical processing is noteworthy. The spectral preprocessing methods are well explained in the text. The statistical parameters used to analyze model performance (R², RMSE, and RPD) are considered the most important and are properly detailed in the methodology.

- It's not clear whether the leaves were stored at a temperature (4°C or -4°C; see line 136 and line 139); or whether all leaves were actually frozen before the NIR recording, there's some confusion. It would be more accurate to analyze both SPAD and NIR on fresh, unmanipulated leaves. Please verify.

- Review lines 254 through 257, it does not match the results shown in Figure 1.

-Results

- It is difficult to visualize the results in Figures 4 and 5; it is recommended to enlarge the axis values.

- For a better understanding, indicate the meaning of BP and RBF in the Figure5.

- In Figure 6, it is more appropriate to use two decimal places in numerical values (for all indicators: R², RMSE, RPD). Also, in the text, unify the decimal places according to the results in the figures.

-Discussion and conclusions

The conclusions are somewhat detailed (sometimes they seem more like results). But overall, both the discussion and the conclusions are appropriate for the authors' stated objectives and the topic addressed.

-References

Check the references, there are some errors in the journals, some italics are missing...

 

Author Response

Dear Reviewer：

Thank you for your letter. First of all, please allow me to extend our team’s sincerest apologies for the oversight in our work, which has caused inconvenience during the review process. We truly appreciate your valuable guidance on our research, as it has provided us with an opportunity to refine our work. In accordance with your suggestions, we have made the following revisions to address the issues in the paper. The detailed feedback is as follows:

 

Question 1：Keywords: some keywords are in the title. Change.

 

Modification and reply 1：We sincerely appreciate your valuable feedback on our manuscript. In response, we have revised the keywords and replaced those that overlapped with the title. The updated keywords are as follows: Spectral analysis; Soil and Plant Analyzer Development value; competitive adaptive reweighted sampling; Back Propagation neural network; radial basis function neural network。We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

Question 2：Line 45: italics are missing in: Ziziphus jujuba

 

Modification and reply 2：Thank you very much for your valuable feedback on our manuscript. We have revised the term "Ziziphus jujuba" in line 45 to be italicized( Ziziphus jujuba).We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

Question 3：There is little scientific content in the introduction. The authors describe theoretical concepts of NIR spectroscopy, processing methods for smoothing spectra, application characteristics, and general limitations. However, the authors should conduct a literature search for findings published to date on the estimation or calibration of this parameter in leaves. They should highlight the novelty of the topic addressed based on the scientific knowledge provided. They should provide more information on the importance of estimating the SPAD value.

 

Modification and reply 3：Thank you very much for your valuable suggestions on our paper. We have revised the introduction section based on your feedback. Specifically, we have enriched the scientific content of the introduction and conducted additional literature searches to provide more information on the prediction of SPAD value. We also summarized previously published findings on the estimation or calibration of this parameter in leaves and, based on the available scientific knowledge, highlighted the innovative aspects of the topic. The revised introduction now reads as follows: Gray jujube (Ziziphus jujuba Mill.) is an important economic fruit tree unique to China, with its fruits being rich in nutrients and having broad market prospects (1). In the high-quality and high-yield cultivation management of gray jujube, the plant's Nitrogen nutrition status is a key limiting factor. Chlorophyll, as the core pigment of plant photosynthesis, has its content highly Positive correlation with the leaf nitrogen content, and is therefore regarded as a reliable indicator for assessing plant physiological health status and nitrogen nutrition levels(2, 3). The SPAD-502 chlorophyll meter, by measuring the light transmittance of leaves at specific wavelengths, can quickly and non-destructively obtain the relative chlorophyll content (SPAD value), and has become an important tool for field nutrition diagnosis (4). However, as a point-based Measurement Equipment, the SPAD meter is less efficient in characterizing large-area canopy with spatial heterogeneity, and is difficult to integrate into future high-throughput field phenotyping platforms.Therefore, the development of a new technology capable of achieving rapid and comprehensive surveillance of the nitrogen status in grey jujube is of great significance for realizing its precise fertilization and intelligent management.

Near-Infrared Spectroscopy, as an efficient and environmentally friendly means of non-destructive inspection, has its analytical foundation in the double frequency and combination band vibrations of Hydrogen-containing group such as C-H, O-H, and N-H in organic compounds (5, 6). This technology has been attested to have great potential in the quantitative inversion of plant Leaf Biochemical parameters (such as nitrogen, chlorophyll, and moisture). In recent years, numerous studies have been devoted to applying NIR spectroscopy to estimate the Chlorophyll content of various crops. For example, Zhang et al.(7)successfully used NIR spectroscopy to predict the SPAD value of rice leaves and found that after Standard Normal Variate (SNV) Pretreatment, the Model performance was significantly improved; Li et al.(8)compared multiple Modeling methods in the study of citrus leaves and confirmed the superiority of the machine learning algorithm in such nonlinear problems; Prattana Lopin et al.(9)further effectively screened the Characteristic wavelength related to chlorophyll through the competitive adaptive reweighted sampling algorithm, simplifying the model.These studies have laid a solid foundation for the application of NIR technology in plant nutrition surveillance. However, NIR spectra are susceptible to interference from environmental noise, light scattering, and sample baseline drift. Therefore, selecting appropriate spectral pre-processing methods (such as Smooth, Standard Normal Variate, Derivative processing, etc.) to extract effective information, combined with Characteristic wavelength selection algorithms (such as Competitive Adaptive Reweighted Sampling) to eliminate redundant variables, is key to constructing robust and high-precision quantitative models (10-12).

In terms of Modeling algorithms, machine learning methods capable of handling complex Nonlinear relationships have demonstrated significant advantages. Back Propagation (BP) neural network and radial basis function neural network, among others(13, 14), have been widely applied in the field of spectral analysis due to their powerful function approximation capabilities.Different training algorithms affect both the convergence speed and prediction accuracy by altering the optimization path of the model, and systematic comparison of these algorithms is crucial for constructing the optimal model(15).

Although NIR spectroscopy has made significant progress in crop nutrition surveillance, existing research has primarily focused on staple food crops such as rice and wheat, as well as some fruit trees. For the important economic tree species of gray jujube, its leaf structure and biochemical composition may possess uniqueness, making the direct application of models from other crops less universally applicable. Currently, research on the systematic assessment of SPAD value in gray jujube leaves using NIR spectroscopy remains very limited, particularly in exploring the optimal spectral pre-processing workflow, Feature band selection, and the combined effects of different neural network training strategies, where in-depth and systematic reports are still lacking.

Therefore, this study aims to fill this research gap by taking jujube leaves as the research object and systematically investigating the quantitative analysis model of SPAD value based on NIR spectroscopy. The specific objectives of this study include: (1) collecting NIR spectra and SPAD value of jujube leaves to construct a Dataset; (2) comparing the enhancement effects of various spectral pre-processing methods on Model performance; (3) applying the Competitive Adaptive Reweighted Sampling algorithm to screen Characteristic wavelength and optimize model inputs; (4) constructing BPNN and RBFNN models and evaluating the predictive efficacy of different training function; (5) determining the optimal model combination suitable for the non-destructive inspection of SPAD value in jujube leaves. This study is expected to provide a reliable technical solution and theoretical basis for the rapid and non-destructive diagnosis of the nutritional status of jujube.

We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

Question 4：It's not clear whether the leaves were stored at a temperature (4°C or -4°C; see line 136 and line 139); or whether all leaves were actually frozen before the NIR recording, there's some confusion. It would be more accurate to analyze both SPAD and NIR on fresh, unmanipulated leaves. Please verify.

-Review lines 254 through 257, it does not match the results shown in Figure 1.

 

Modification and reply 4：Thank you very much for your valuable suggestions on our manuscript. We have clearly stated the number of Leaf sample used for data collection and subsequent statistical analysis (a total of 188 leaves), along with the storage temperature and duration (after collection, the samples were stored in a 4°C car refrigerator and retrieved for further experiments on the same day upon returning to the laboratory). We sincerely appreciate your recommendation that "conducting simultaneous SPAD and NIR analyses on fresh, untreated leaves would yield more accurate results." However, due to limitations in the experimental setup and equipment availability, we are currently unable to perform such simultaneous measurements on fresh leaves. In subsequent studies, we will seek to mitigate the potential errors arising from this limitation.We have revised the description pertaining to Figure 1. The updated content is as follows: Figure 1b displays the Frequency distribution histogram of SPAD value in grey jujube leaf samples. The SPAD value serves as an important indicator of leaf Relative chlorophyll content and even Nitrogen nutrition status. In terms of distribution pattern, the SPAD value approximately follows a Normal distribution, suggesting that chlorophyll content in most leaves is moderate and the sample population exhibits good uniformity. The SPAD value is predominantly concentrated between 25 and 45, with the peak count (i.e., Sample number) occurring between 35 and 40, indicating that this SPAD value range represents the most typical chlorophyll content level in this study. Notably, leaves with SPAD value below 25 or above 45 are relatively scarce, which may be due to the fact that all samples were obtained from healthy grey jujube plants, with few individuals showing extreme deficiency or nutrient surplus.The distribution results confirm that the grey jujube leaf samples used in the experiment exhibited both natural variation in Chlorophyll content and a relatively stable overall condition, rendering them highly suitable for developing a predictive model for Chlorophyll content. We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

Question 5：It is difficult to visualize the results in Figures 4 and 5; it is recommended to enlarge the axis values.

For a better understanding, indicate the meaning of BP and RBF in the Figure5.

In Figure 6, it is more appropriate to use two decimal places in numerical values (for all indicators: R2, RMSE, RPD). Also, in the text, unify the decimal places according to the results in the figures.

 

Modification and reply 5：Thank you very much for your valuable feedback on our paper. We have enlarged the axis values in Figures 4 and 5 (increasing the font size from 22 to 30), which has improved the readability of the results in these figures. Additionally, an annotation has been added to Figure 5(BP denotes the model developed using the BP neural network method, while RBF represents the model constructed with the RBF neural network method.).We have also revised Figure 6, in which the values are now displayed with two decimal places. For consistency, the entire text has been updated to uniformly use two decimal places.We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

 

Question 6：

The conclusions are somewhat detailed (sometimes they seem more like results). But overall, both the discussion and the conclusions are appropriate for the authors' stated objectives and the topic addressed.

-References

Check the references, there are some errors in the journals, some italics are missing.

 

Modification and reply 6：Thank you very much for your valuable feedback on our paper. We have revised the conclusion section in accordance with your suggestions to present the research objectives and subject matter more concisely and logically. Additionally, we have corrected the references by rectifying errors in some journal titles and adding missing italics. The revised conclusion section is as follows: This study successfully validated the feasibility of using Near-Infrared Spectroscopy for rapid and non-destructive inspection of SPAD value in gray jujube Leaf. Compared with traditional destructive chemical Measurement methods, this method achieves non-destructive and rapid analysis; compared with the single-point Measurement of SPAD meters, spectroscopy technology more easily enables high-throughput Assessment at the canopy scale.

 

Through systematic comparison, the combination of Smooth with First Derivative and Competitive Adaptive Reweighted Sampling characteristic wavelength selection was determined to be the optimal scheme. On this foundation, the established BP neural network model based on the Lbfgs algorithm achieved the best prediction performance (Validation set Coefficient of determination (R²) > 0.84, RPD > 2.5), confirming the model's excellent accuracy and robustness.

 

In summary, the technical system constructed in this research provides an advanced and reliable solution for the rapid field diagnosis of gray jujube nitrogen nutrition and precision fertilization management.The successful application of this method will directly serve production practice, providing strong technical support for achieving the precision agriculture goals of improving fertilizer utilization efficiency, reducing production costs, decreasing non-point source pollution, and guaranteeing high-quality and high-yield jujube fruits.

We sincerely apologize for any inconvenience our shortcomings may have caused during your review process. We will be sure to incorporate your guidance into our future work.

 

Finally, please allow me to express our deepest gratitude on behalf of our team. Your valuable guidance has not only enriched our work but also enabled every member to grow significantly.  

We will always carry your teachings with us in our future endeavors. Wishing you all the best!

Jun kai , Zeng

                                                     October 12, 2025

 

 

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors, 

Thank you for revised version of manuscript and answers on my questions and suggestions, it is fine for me. 

Wish you all the best in the futre work,