Nondestructive Determination of Leaf Nitrogen Content in Corn by Hyperspectral Imaging Using Spectral and Texture Fusion

Round 1

Reviewer 1 Report

This manuscript concerned timeliness and practicality of leaf nitrogen content prediction in corn and proposed a solution based on spectral imaging and image analysis techniques. However, the contribution of the proposed solution is marginal, and the experimental results should be more convincing. The detailed comments are as follows:

1. The motivation of this study needs to be clarified. As the literature on plant nitrogen prediction reviewed before Line 87, Page 2, many techniques based on image analysis and hyperspectral imaging have been proposed. What are the advantages and disadvantages of the existing methods? What are the trends of this technical route? These problems should be discussed entirely.

2. Authors employ the existing CARS-SPA algorithm for band selection, the existing GLCM method for texture feature extraction, and also existing PLSR and SVR for feature fusion. There is no unique algorithm contribution in this study.

3. The experiment section lacks results on other similar datasets, and it is easy to suspect that the algorithm has overfitting problems. The authors need to make more attempts to make the experiments and conclusion more convincing and reliable.

4. It is recommended that some experiments related to timelines and the practicality of algorithms be added.

5. Inconsistent citation style. See Line 53 on Page 2.

6. Too many format issues, typos, and grammar mistakes, such as:

a) Key abbreviations need corresponding explanations when they appear for the first time, such as LNC in Line 40, Page 1.

b) Line 137 begins with unnecessary spacing and wrong initial capital words. The same issue should be checked throughout the manuscript. Please refer to the proper format for inserting equations.

Author Response

Response to Reviewer 1 Comments

Point 1: The motivation of this study needs to be clarified. As the literature on plant nitrogen prediction reviewed before Line 87, Page 2, many techniques based on image analysis and hyperspectral imaging have been proposed. What are the advantages and disadvantages of the existing methods? What are the trends of this technical route? These problems should be discussed entirely.

Response 1: Dear Reviewers, Thank you very much for your suggestions. We have modified the motivation of our study. (Line44-Line105)

“With the continuous development of non-destructive testing technology, hyperspectral imaging has been widely used to detect plant LNC. The absorption and reflection of LNC to the waveband affect the material composition and structural information inside the crop. Hyperspectral-based plant nitrogen detection is to obtain the spectral characteristics of plants and canopies by using hyperspectral sensors without damaging the crop tissues and structures and then to get the nitrogen content surplus or deficit in the plant quickly and accurately by analyzing the spectral information of plant leaves or canopies [5]. Zhu et al. [6] quantitatively predicted the LNCs of winter wheat by constructing several LNC spectral indices (LNCSIs), and the results confirmed the validity of LNCSIs for winter wheat LNCs prediction. Sun et al. [7] developed a regression model for rice LNC prediction by measuring its active and passive spectra. Raj et al. [8] proposed a LNC prediction model based on UAV hyperspectral images of corn canopy. Li et al. [9] constructed a winter wheat LNC estimation model based on a multi-angle composite vegetation index by acquiring spectral data of multiple leaf inclination angles. The above study demonstrates the feasibility of the inversion of plant nutrient elements based on spectral features. However, the single spectral feature has limitations for detecting the distribution pattern of plant nitrogen content. It cannot dynamically predict the internal plant nitrogen content, which has limitations for further improving the robustness of the prediction model.

Hyperspectral imaging is a combination of imaging and spectroscopic techniques. Although both spectral data and texture features can improve the accuracy of nitrogen nondestructive detection to some extent, a single consideration of image texture variables cannot predict the nitrogen content inside the plant. In contrast, a single consideration of spectral variables cannot characterize the overall spatial distribution of nitrogen [10,11]. Combining spectral and image texture information from optical hyperspectral data on plant leaf nitrogen can improve the accuracy of nitrogen prediction models and the generalization ability [12,13]. Wang et al. [14] used the CatBoost algorithm with texture fusion features to predict soil nitrogen content and achieved better prediction results. Yan et al. [15] constructed a chlorophyll content prediction model based on the fusion of spectral and texture features by comparing back propagation neural network (BPNN) and support vector regression (SVR) algorithms, and its prediction set R² was 0.9571. In summary, the fusion of spectral and texture features is beneficial to alleviate the disadvantage of the low sensitivity of spectral analysis techniques and improve the accuracy and robustness of nitrogen prediction models [16].

Algorithms play a decisive role in plant nitrogen prediction. The partial least squares (PLS) regression algorithm is an extension of the multiple linear regression model, which can effectively reduce the covariance between data variables and thus is applied extensively. On the other hand, the SVR algorithm can find the set of variables containing minor redundant information from the spectral information to minimize the covariance among variables, which is widely used in regression problems. Tan et al. [17] proposed a binary particle swarm optimization-support vector regression (BPSO-SVR) machine learning method, which was based on a binary particle swarm optimization algorithm, to predict soil nitrogen content with good prediction results. Bai et al. [18] proposed a PLS regression-based model for predicting the leaf nitrogen content of winter wheat with a validation set prediction accuracy of R² of 0.84. Wang et al. [19] used an aerial hyperspectral spectral imaging technique, and a partial least squares regression (PLSR)-based nitrogen content prediction model for corn plants was constructed with a prediction accuracy of R² of 0.85. Therefore, constructing plant prediction models based on PLSR and SVR algorithms has certain reliability and research significance.

Few studies have been conducted to build plant inversion models based on feature fusion. In this study, we combined spectral imaging and image analysis techniques to predict LNC in corn accurately. We successively processed the spectral data using first-order derivatives, standard normal variance (SNV), Savitzky-Golay (S-G) smoothing and normalization, and extracted feature wavelengths using an improved fusion algorithm with competitive adaptive reweighting algorithm (CARS) and a successive projection algorithm (SPA). The predictive regression model of the corn LNC was developed by fusing the spectral features and image texture features from hyperspectral data. In addition, we also compared the application of PLSR and SVR modeling methods in corn LNC monitoring. Therefore, the fusion based on texture features and spectral features can break through the limitations of single-feature prediction, effectively obtain more comprehensive phenotypic characteristics of plants, improve the credibility of data, further enhance the accuracy of prediction models, and provide a new idea and method for detecting the growth pattern of plants.”

Point 2: Authors employ the existing CARS-SPA algorithm for band selection, the existing GLCM method for texture feature extraction, and also existing PLSR and SVR for feature fusion. There is no unique algorithm contribution in this study.

Response 2: Dear Reviewer, Thank you very much for raising this critical issue. Our manuscript may be mainly biased towards innovation in applications and may not be very innovative in algorithms. But we will try new algorithms and innovate in our future research.

Point 3: The experiment section lacks results on other similar datasets, and it is easy to suspect that the algorithm has overfitting problems. The authors need to make more attempts to make the experiments and conclusion more convincing and reliable.

Response 3: Dear Reviewer, Thank you very much for your suggestion. Considering the cost of the experiment and the growth cycle of the plants, we collected relatively small sample data. We added repeated trials and cross-validation in the early stage of modeling to prevent the phenomenon of model overfitting. However, we did not realize the importance of presenting the repeated trial and cross-validation in the manuscript, so it was not reflected in the paper. With your suggestion, we must present our process of conducting repeated tests and cross-validation in the paper to improve the model's credibility and conclusions. Some studies validate the feasibility of small sample datasets. For example, Cao et al. [1] predicted the nitrogen content of 38 small samples of maize plant data based on different dimensionality reduction algorithms and obtained the best prediction model with R² of 0.96. Fan et al.[2] predicted the nitrogen content of 72 maize leaf samples based on multispectral variables and obtained better prediction results. Together, the above studies can effectively demonstrate the feasibility of small sample data in predicting the nitrogen content of maize plants. Therefore, the sample size of 82 samples can fully support the study of this paper.

[1]    Cao, C.; Wang, T.; Gao, M. Hyperspectral inversion of nitrogen content in maize leaves based on different dimensionality reduction algorithms. Computers and Electronics in Agriculture, 2021, 190, 106461.

[2]    Fan, L.; Zhao, J.; Xu, X. Hyperspectral-based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables. Sensors, 2019, 19(13), 2898.

Point 4: It is recommended that some experiments related to timelines and the practicality of algorithms be added.

Response 4: Dear Reviewer, Thank you very much for raising this very important issue. We strongly agree with you. However, due to the critical growth cycle of the plants, we were unable to obtain more fresh test samples in the short term and thus were unable to add some timelines and algorithm applicability tests. However, we will continue our research and collect more data to expand the sample data set within the growth cycle of the plants. We have added this deficiency to the limitations section and we will investigate it further in the future.

 

Point 5: Inconsistent citation style. See Line 53 on Page 2.

Response 5: Dear reviewer, we apologize for this mistake. We have checked and modified this paragraph. (Line 53 on Page 2)

Point 6: Too many format issues, typos, and grammar mistakes, such as:

1. Key abbreviations need corresponding explanations when they appear for the first time, such as LNC in Line 40, Page 1.

Response 6: a) Dear reviewer, we apologize for these mistakes. We have checked and modified this manuscript. (Line 40 on Page 1、Line 70 on Page 2 and so on)

 

     2. Line 137 begins with unnecessary spacing and wrong initial capital words. The same issue should be checked throughout the manuscript. Please refer to the proper format for inserting equations.

Response 6: b) Dear reviewer, we apologize for these mistakes. We have carefully examined and revised this manuscript. Again, I apologize for the mistakes I made. (Line 265-Line 267、Line 269-Line 272 and so on)

Finally, thank you again for your careful reading of our paper. We hope to make progress under your guidance.

Reviewer 2 Report

- This work presents the application of hyperspectral imaging for leaf nitrogen content determination of corn. This study is important in the context of using modern technologies in agriculture. However, the following comments needed to be addressed for the improvement of the manuscript.

Comments-

Comment 1: In the Abstract (line 14) and 2.2.1 Hyperspectral Data (line 121) spectral band ranges were dismissed. Correction is mandatory.  

Comment 2: All the abbreviations such as LNC, N, PLS, SVR, and others are needed to be written in full form when they appear in the manuscript for the first time. It seems sometimes these are written in full form later where the short form came first. For example, LNC is used on page 1, line 40 but the full form is written on page 2, line 88. Please follow the rules for using abbreviations.  

Comment 3: Line 105- the sample number used for the experiment is 82 which is quite low for the application of multivariate data analysis. Because if the sample number is low then the model performance may get good for the used samples. Still, when new data needs to be predicted, accuracy becomes stunted due to the inadequacy of robustness. However, the leave-one-out cross-validation method was used considering the sample number which was appropriate.   

Comment 4: Line 110- 2.2. Experimental Data: was the data collected in the open field? or sample was taken to the laboratory then capture the images?

Comment 5: Line 124- Lumo Recorder software version number is required.

Comment 6: Line 138- What was the material of the white plate? Teflon!!

Comment 7: Line 145-148- This part looks like the result, therefore needed to be presented in the Result section.

Comment 8: Line 194- A tabular form (4 rows ×3 columns- feature, description, and equation) would be the best fit for easy presentation.

Comment 9: Figure 8, Figure 11, Figure 13, Figure 14: image quality needed to be improved.

Comment 10: Line 391-395-Sentences are already expressed in Materials and methods. Unessential to repeat.

Comment 11: Figure 12: present the full form of Asm, Con, Eng, Idm in the figure description.

Comment 12: Line 450-451- None of the hyperspectral imaging techniques (PLSR) and feature extraction methods (GLCM) are new. The sense of combing two techniques presented in this manuscript is legitimate.

Author Response

Response to Reviewer 2 Comments

 

Point 1: In the Abstract (line 14) and 2.2.1 Hyperspectral Data (line 121) spectral band ranges were dismissed. Correction is mandatory. 

 

Response 1: Dear reviewer, we apologize for this mistake. We have checked and modified this paragraph. (Line 14 and Line 130-Line 131)

 

Point 2: All the abbreviations such as LNC, N, PLS, SVR, and others are needed to be written in full form when they appear in the manuscript for the first time. It seems sometimes these are written in full form later where the short form came first. For example, LNC is used on page 1, line 40 but the full form is written on page 2, line 88. Please follow the rules for using abbreviations.

 

Response 2: Dear reviewer, we apologize for these mistakes. We have checked and modified this manuscript regarding the formal writing of abbreviations. We apologize again for our careless mistake (Line 40 on Page 1、Line 70 on Page 2, and so on)

 

Point 3: Line 105- the sample number used for the experiment is 82 which is quite low for the application of multivariate data analysis. Because if the sample number is low then the model performance may get good for the used samples. Still, when new data needs to be predicted, accuracy becomes stunted due to the inadequacy of robustness. However, the leave-one-out cross-validation method was used considering the sample number which was appropriate.  

 

Response 3: Dear reviewers, we strongly agree with your suggestions. However, we considered that the factor of sample data would lead to model overfitting in the early stage of modeling, and we performed ten replicate trials and leave-one-out cross-validation in the process of experimentation. Still, we superficially felt that it was not the research focus of the article and did not reflect the appearance of the paper. With your proposal, it is essential to remember that we have done cross-validation and have revised it throughout the manuscript.

 

Point 4: Line 110- 2.2. Experimental Data: was the data collected in the open field? or sample was taken to the laboratory then capture the images?

 

Response 4: Dear Reviewer, Thank you very much for raising this critical issue. Regarding the data collection location, we have already mentioned in Experimental Area 2.1, " Corn leaf samples were collected outdoors and immediately sent inside the laboratory for hyperspectral data collection." (Line 115-Line 116)

 

Point 5: Line 124- Lumo Recorder software version number is required.

 

Response 5: Dear reviewer, we apologize for this mistake. We have checked and modified this paragraph. (Line 133)

 

Point 6: Line 138- What was the material of the white plate? Teflon!!

 

Response 6: Dear Reviewers, Thank you very much for your questions. We use a white board that is wooden. (Line147)

 

Point 7: Line 145-148- This part looks like the result, therefore needed to be presented in the Result section.

 

Response 7: Dear Reviewer, Thank you very much for this important issue. We have completed the modification and put this part of the data and tables in the results section（Line 307-Line 311）

 

Point 8: Line 194- A tabular form (4 rows ×3 columns- feature, description, and equation) would be the best fit for easy presentation.

 

Response 8: Dear Reviewers, Thank you very much for your suggestions. We feel that your comments can significantly improve the presentation of the manuscript. We have completed the revision.（Line 200）

Table 1. Summary of texture features

Features	Description	Equation
Entropy	A measure of the image’s randomness reflects the texture's complexity or non-uniformity.
Energy	Reflecting the uniformity of the image grayscale distribution and the degree of texture coarseness.
Contrast	Reflecting the clarity of the image and the depth of the texture grooves.
Inverse Different Moment	Reflecting the degree of local variation of image texture.

 

Point 9: Figure 8, Figure 11, Figure 13, Figure 14: image quality needed to be improved.

 

Response 9: Dear reviewer, we apologize for these mistakes. We have improved the image quality of Figure 8, Figure 11, Figure 13, and Figure 14 (Line 328 ,Line 375,line 422,Line 432)

(a)

(b)

Figure 8. (a) Original spectral curves; (b) First derivative curves

(a)	(b)
(c)	(d)

Figure 11. Accuracy of the measured and predicted values of the validation set: (a) Full spectrum -SVR model, (b) Characteristic wavelength-SVR model, (c) Full spectrum-PLSR model, (d) Characteristic wavelength-PLSR model

(a)

(b)

Figure 13. Prediction of corn leaf nitrogen content by models SVR (a) and PLSR (b) based on image texture features

(a)

(b)

Figure 14. Leaf nitrogen content of corn predicted by the models SVR(a) and PLSR(b) based on fusion features

 

Point 10: Line 391-395-Sentences are already expressed in Materials and methods. Unessential to repeat.

 

Response 10: Dear reviewer, we apologize for this mistake. We have removed the unessential content (Line 405-Line 406)

 

Point 11: Figure 12: present the full form of Asm, Con, Eng, Idm in the figure description.

 

Response 11: Dear reviewer, we apologize for these mistakes. We have checked and modified this paragraph. (Figure12)

Figure 12. Correlation coefficient between corn leaf texture attributes and Nitrogen values

 

Point 12: Line 450-451- None of the hyperspectral imaging techniques (PLSR) and feature extraction methods (GLCM) are new. The sense of combing two techniques presented in this manuscript is legitimate.

 

Response 12: Dear Reviewers, Thank you very much for your comments. We have added the justification for using the GLCM and PLSR algorithms.（Line 457-Line 463）

“In this paper, we propose a new hyperspectral imaging technique for predicting the nitrogen content of maize leaves. Before building the prediction model, we introduce the GLCM method for extracting texture features, a matrix function of the distance and angle of different pixel points, using the joint probability distribution of the simultaneous occurrence of different grayscale pixel points to reflect texture information. It has good results in identifying texture features of corn leaves, improving the prediction accuracy of the spectral image model, and reducing the complexity of the model. In this paper, a fusion of texture features and spectral features is used for modeling. Comparing the eight models, the PLSR model based on fused features has the best prediction of the nitrogen content of corn leaves (R_P²=0.987, RMSEP=0.047). The PLSR model is an extension of the multiple linear regression model, which can effectively overcome the covariance problem of hyperspectral data and retain the original spectral information. It improves the accuracy and generalization of nitrogen con

 

Finally, thank you again for your careful reading of our paper. We hope to make progress under your guidance.

Reviewer 3 Report

This paper improves the accuracy of nondestructive determination of leaf nitrogen content in corn by using advanced data processing technique – data fusion method -  for hyperspectral imaging. The predicted result is better than single spectral feature and single image texture feature models, which the R2 of the PLSR model is up to 0.987. This study gives a promising method for predicting plant nutrient elements, which has a great significance for increasing human life quality. The paper should be published after small drawbacks to be listed below are modified:

1.       Line 7, “³ Xiong'an Innovation Research Institute, Chinese Academy of Sciences, Xiong'an New Area 071899, China”. No author belongs to this unit.

2.       Line 40, define “LNC” as it is used first time in the text.

3.       Line 70, define “BPNN”

4.       Line 80, define “BPSO-SVR”

5.       Please give more detail explanation for figure 9 and figure 10.

6.       Line 436-7, double check the values of “0.18”and “0.182” calculated from table 4, right or not?

7.       In references, some formats need to be corrected:

Refs. 3, 4, 10, 14, 27, 28, 31, and 34.

1. What is the main question addressed by the research?

- The research related to improve the accuracy and robustness of hyperspectral imaging for nondestructively measuring leaf nitrogen content by using spectral and texture fusion techniques.

2. Do you consider the topic original or relevant in the field? Does it address a specific gap in the field?

-  Yes, and it address a specific gap in the field. 

3. What does it add to the subject area compared with other published  material?

- compared with other published material, it add the content that using the fusion techniques in determination of leaf nitrogen content in corn.

4. What specific improvements should the authors consider regarding the methodology? What further controls should be considered?

- I have no idea about both questions, I have not many experience in imaging processing.

5. Are the conclusions consistent with the evidence and arguments presented and do they address the main question posed?

- Yes, I think they are and do.

6. Are the references appropriate?

- Yes

7. Please include any additional comments on the tables and figures.

- I have no comments for the tables. The figure 1, figure 3 and figure 12 are not necessary; figure 7, figure 9, and figure 10 could be improved and giving more explanation.

Author Response

Response to Reviewer 3 Comments

 

Point 1: Line 7, “3 Xiong'an Innovation Research Institute, Chinese Academy of Sciences, Xiong'an New Area 071899, China”. No author belongs to this unit.

 

Response 1: Dear reviewer, we apologize for this mistake. We have added the authors of this unit.(Line 4)

Author: Zhuo Wang ^2,3,*

³   Xiong'an Innovation Research Institute, Chinese Academy of Sciences, Xiong'an New Area 071899, China

 

Point 2: Line 40, define “LNC” as it is used first time in the text.

 

Response 2: Dear reviewer, Thanks for your suggestion. We have modified this paragraph. (Line 40)

 

 

Point 3: Line 70, define “BPNN”.

 

Response 3: Dear reviewer, Thanks for your suggestion. We have modified this paragraph. (Line 72)

 

 

Point 4: Line 80, define “BPSO-SVR”.

 

Response 4: Dear reviewer, Thanks for your suggestion. We have modified this paragraph. (Line 83)

 

 

Point 5: Please give more detail explanation for figure 9 and figure 10.

 

Response 5: Dear reviewer, Thanks for your suggestion. We have added more explanations about Figure 9 and Figure 10 (Line 336-Line 353、Line 356-Line 367)

“CARS sets the number of Monte Carlo sampling runs to 50, and tenfold cross-validation is used to evaluate the effect of each subset, and the spectral feature variables are selected for the first-order derivative preprocessed data, as shown in Figure 9. From Figure 9a, it can be seen that the number of variables decreases rapidly in 25 sampling runs due to the exponential decay function EDP, and then gradually slows down and stabilizes, which indicates that the CARS algorithm has "coarse selection" and "selection" in the feature variable selection. This process indicates that the CARS algorithm has two processes: "coarse selection" and "selection" in feature selection. As can be seen in  Figure 9b, in the initial stage, the ten-fold cross-validation RMSECV values of the individual PLS models became progressively smaller as the number of iterations increased due to the exclusion of a large number of variables that were not relevant to the prediction of nitrogen content in corn leaves. When the RMSECV reached the minimum value, the corresponding number of sampling was 24. With a further increase in the number of sampling, the RMSECV also gradually increased, indicating that some crucial variables in the spectrum were eliminated. Therefore, it can be seen in Figure 9c that when the variables obtained in 24 iterations were identified as the characteristic variables for predicting the nitrogen content of corn leaves, a total of 24 variables.”

“Based on the CARS screening wavelengths, other feature variable screening was performed using the SPA algorithm. The SPA algorithm screened the 14 reflected wavelengths with the lowest covariance of leaf nitrogen content as feature wavelengths. The screened wavelengths were correlated with maize LNC as shown in Figure 9. The results showed that the wavelengths at 505.64 nm, 572.1 nm, and 732.01 nm showed a weak correlation with LNC, while the other wavelengths showed a strong correlation. Moreover, most of the screened feature wavelengths were distributed in the nitrogen-sensitive band range, further indicating the accuracy of the CARS-SPA algorithm based on the extraction of sensitive bands of the spectrum, which showed strong feasibility for predicting maize LNC using spectral information and could well invert the intrinsic relationship between spectral information and LNC. Hence, selecting feature wavelengths is significant for predicting corn LNC.”

 

Point 6: Line 436-7, double check the values of “0.18”and “0.182” calculated from table 4, right or not?

 

Response 6: Dear reviewer, we apologize for this mistake. We have checked and modified this paragraph. (Line 443- Line 444)

 

Point 7: n references, some formats need to be corrected:

Refs. 3, 4, 10, 14, 27, 28, 31, and 34.

 

Response 7: Dear reviewer, we apologize for these mistakes. We have checked and modified the format of the references. (Line 491-Line 492、Line 493-Line 494、Line 505-Line 506、Line 413-Line 414、Line 539-Line 542、Line546-Line 547、Line 552-Line 553)

3. Zhao, M.; Sun, X.; Wang, D. A review of rice hyperspectral remote sensing monitoring research. Acta Agricultural University Jiangxi. 2019, 41(01), 1–12.
4. Song, L.; Ye, J.; Zheng, Y. Research Progress on Nondestructive Rapid Nutrition Diagnosis of Crop Nitrogen. China Rice. 2017, 23(06), 19–22
5. Shun, J.; Jin, M.; Mao, P. Detection of nitrogen content in lettuce leaves based on spectroscopy and texture using hyperspectral imaging technology. Transactions of the Chinese Society of Agricultural Engineering. 2014, 30(10), 167–173
6. Wang, C.; Yang, C.; Cui, L. Prediction of Soil Total Nitrogen Based on CatBoost Algorithm and Fusion of Image Spectral Features. Transactions of the Chinese Society for Agricultural Machinery. 2021, S1 (52), 316–322
7. Araújo, M C U.; Saldanha, T C B.; Galvão, R K H. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems. 2001,57(2), 65–73
8. Kamruzzaman, M.; ElMasry, G.; Sun, D-W. Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Analytica Chimica Acta. 2021, 714, 57–67
9. Li, X. The correlation of total nitrogen content with leaf spectral reflectance and SPAD values in different corn varieties. Soils and Fertilizers Sciences in China. 2015, 03, 34-39+119
10. Wang, C.; Yang, W.; Cui, L. Prediction of Soil Total Nitrogen Based on CatBoost Algorithm and Fusion of Image Spectral Features. Transactions of the Chinese Society for Agricultural Machinery. 2021, S1 (52), 316–322

 

Finally, thank you again for your careful reading of our paper. We hope to make progress under your guidance.

Round 2

Reviewer 1 Report

All my previous concerns have been addressed.