Performance of Machine Learning Models in Predicting Common Bean (Phaseolus vulgaris L.) Crop Nitrogen Using NIR Spectroscopy

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript (unfortunately) does not do justice in addressing the main objective "to evaluate and compare the performance of 75 four machine learning algorithms (Random Forest - RF, K-nearest neighbors - KNN, 76 Artificial Neural Network - RNA and M5Rules - M5) in the prediction of leaf nitrogen of 77 common bean crop by means of hyperspectral data in the NIR range (700 to 1300 nm)". The "evaluate" and "compare" components are superficially presented in section 3 (Results). Additionally, greater detail regarding the algorithms/models should be presented in section 2 (Materials and Methods) and used to contextualize the evaluation and comparison of the model results. Alternatively, the aim and objectives should be revised to reflect an application of machine learning algorithms for N modelling in bean using spectroscopy.

Please see specific details attached.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Author Response

Dear Reviewer,

We would like to express our sincere gratitude for the time you dedicated to reviewing our article and for the valuable feedback you provided. Your thorough work and insightful comments were instrumental in improving the quality and clarity of the manuscript and ensuring its relevance to the subject matter.

We are at your disposal to clarify any questions you may have regarding the article. Your contribution is essential to us, and we are committed to incorporating any additional recommendations you may have to further strengthen the paper.

All suggested revisions and changes have been duly addressed and highlighted point by point in the attached file for your review.

We reiterate our gratitude for your dedication to reviewing our work. We are open to any additional suggestions you may have to further improve the manuscript.

 

Best regards.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Please see in the attached file.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Author Response

Dear Reviewer,

We would like to express our sincere gratitude for the time you dedicated to reviewing our article and for the valuable feedback you provided. Your thorough work and insightful comments were instrumental in improving the quality and clarity of the manuscript and ensuring its relevance to the subject matter.

We are at your disposal to clarify any questions you may have regarding the article. Your contribution is essential to us, and we are committed to incorporating any additional recommendations you may have to further strengthen the paper.

All suggested revisions and changes have been duly addressed and highlighted point by point in the attached file for your review.

We reiterate our gratitude for your dedication to reviewing our work. We are open to any additional suggestions you may have to further improve the manuscript.

Best regards.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript entitled “Performance of Machine Learning Models in Predicting Common Bean (Phaseolus vulgaris L.) Crop Nitrogen Using NIR Spectroscopy” aimed to evaluate the efficiency of four algorithms in predicting soil nitrogen. The study presents important and current methodologies on the implementation of machine learning in precision agriculture.

In the abstract, I suggest presenting the models before their abbreviations.

Lines 50-58: I believe it would be worthwhile to elaborate more on this topic, enriching the paragraph.

Lines 75-82: Align the objectives in the abstract with the objectives presented here.

Lines 83-87: You could start with the key topics.

Figure 1: Improve the image quality.

Lines 126-140: Include the experimental scheme in Figure 1. This would make it easier to visualize.

Line 137: Review the chemical formulas.

Figure 2: If possible, include a standard deviation bar or explain the average daily variation.

Line 184: Insert a reference immediately after mentioning KNN.

Line 246: “T1 (0 kg ha-1 N), T2 (50 kg ha-1 N), T3 (100 kg ha-1 N), and T4 (150 kg ha-1 N)” should be presented in the Materials and Methods section.

Where is the discussion section? Is it combined with the results?

Please pay attention to textual revision, as the use of connectors will give the text more fluency. Review the numbering of figures and graphs, and generally improve the quality of the images.

 

Comments on the Quality of English Language

The English used in the text is sufficient for comprehension, but it can be improved to make the reading more fluid. I suggest the authors reread the entire manuscript and adjust the connections between paragraphs and sections.

Author Response

Dear Reviewer,

We would like to express our sincere gratitude for the time you dedicated to reviewing our article and for the valuable feedback you provided. Your thorough work and insightful comments were instrumental in improving the quality and clarity of the manuscript and ensuring its relevance to the subject matter.

We are at your disposal to clarify any questions you may have regarding the article. Your contribution is essential to us, and we are committed to incorporating any additional recommendations you may have to further strengthen the paper.

All suggested revisions and changes have been duly addressed and highlighted point by point in the attached file for your review.

We reiterate our gratitude for your dedication to reviewing our work. We are open to any additional suggestions you may have to further improve the manuscript.

Best regards.

Author Response File: Author Response.pdf