PONE-D-24-15636Federated learning based reference evapotranspiration estimation for distributed crop fieldsPLOS ONE

Dear Dr. Tausif,

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: ---The purpose section should be emphasized better.

---Give information about the agriculture of the study area.

---Write the limitations of the methods.

---Add the limitations of the study and your suggestions to the results.

---It needs a good discussion section separate from results.

---Test the reliability of prediction results.

--- Compare the measurement and prediction results by performing the Kruskal-Wallis test.

--- Draw the Taylor diagram.

--- Draw error boxplot and violin graphs.

---Add the scatter diagrams.

---Add descriptive statistics parameters.

--- Flowchart is quite pale, needs some improvement.

--- Which software did you analyze with?

--- Can you give information about analysis times? Also provide information about the computer on which you performed the analysis.

--- Include descriptive statistics of the data.

--- Add NSE to the Evaluation criteria section. Interpret the MAPE, R2 and NSE criteria according:

Uncuoglu, E., Citakoglu, H., Latifoglu, L., Bayram, S., Laman, M., Ilkentapar, M., & Oner, A. A. (2022). Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems. Applied Soft Computing, 129, 109623.

--- Check out the following articles about

Bayram, S., & Çıtakoğlu, H. (2023). Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods. Environmental Monitoring and Assessment, 195(1), 67.

Citakoglu, H., Cobaner, M., Haktanir, T., & Kisi, O. (2014). Estimation of monthly mean reference evapotranspiration in Turkey. Water Resources Management, 28, 99-113.

Cobaner, M., Citakoğlu, H., Haktanir, T., & Kisi, O. (2017). Modifying Hargreaves–Samani equation with meteorological variables for estimation of reference evapotranspiration in Turkey. Hydrology Research, 48(2), 480-497.

Reviewer #2: In the present paper, the authors propose an interesting approach for reference evapotranspiration estimation for distributed crop fields using Federated learning based techniques. The paper seem to be interesting and needs to be improved before publication. Following are the major and minor and improvements:

Major comments

I. The introduction lacks recent literature updates. It is essential to incorporate recent studies to provide a comprehensive overview of the current state of research in the field. Below are suggested paragraphs to be added to the literature review section to enhance the introduction:

"Machine learning Remote sensing field becomes cost effective tools in modeling hydrological variables e.g. River discharge, rainfall runoff estimation, Evapotranspiration, land use classification, surface temperature estimation". Following studies will enrich the introduction and literature sections:

1. https://doi.org/10.3390/rs12071107

2. https://doi.org/10.1029/2023WR035785

3. https://link.springer.com/article/10.1007/s43621-024-00263-w

4. https://doi.org/10.1201/9781003377825 (Chapter 3)

5. https://doi.org/10.1007/s42452-023-05520-7

6. https://doi.org/10.1186/s40562-023-00287-6

7. https://doi.org/10.3390/su15043572

8. https://doi.org/10.1007/978-3-031-29394-8_8

9. https://doi.org/10.3390/atmos13101609

II. Novelty of the paper is not written clearly. It should be highlighted.

III. How you divide the dataset? How much data is used for training and testing periods? I suggest authors to add table along with statistics in dataset sections.

IV. I did not find adequate information on the methodologies related to Random Forest Regressor (RFR), Support Vector Regressor (SVR), and Decision Tree Regressor (DTR). Although I found some information on the Federated Learning (FL) model, it is not sufficient for the focus of this paper, which centers around Federated Learning.

V. Compare your findings with previous stud and visual representation (Taylor diagram, Radar chart, Smith graph) of machine learning results should be added. Below are the reference studies and need to be citied:

1. https://doi.org/10.1007/s13201-022-01667-7

2. https://www.mdpi.com/2073-4441/14/10/1666

3. https://www.mdpi.com/2306-5338/10/8/169

4. https://www.mdpi.com/2073-4441/15/6/1149

5. https://doi.org/10.1002/ird.2838

Minor comments

Some grammatical errors are present and need revision.

Clear location map in Figure 2 is needed.

Improvement in the graphical representation of Figure 3.

Check the references as per journal guidelines and add DOI in references

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Reviewer #1: No

Reviewer #2: No

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Attachments

Attachment

Submitted filename: Reviewer Comments -1.docx

PONE-D-24-15636R1Federated learning based reference evapotranspiration estimation for distributed crop fieldsPLOS ONE

Dear Dr. Tausif,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

One or more of the reviewers has recommended that you cite specific previously published works. Members of the editorial team have determined that the works referenced are not directly related to the submitted manuscript. As such, please note that it is not necessary or expected to cite the works requested by the reviewer.

Please submit your revised manuscript by Sep 16 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Hanna Landenmark

Staff Editor

PLOS ONE

on behalf of 

Nguyen-Thanh Son

Academic Editor, PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

One or more of the reviewers has recommended that you cite specific previously published works. Members of the editorial team have determined that the works referenced are not directly related to the submitted manuscript. As such, please note that it is not necessary or expected to cite the works requested by the reviewer.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: (No Response)

Reviewer #4: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: (No Response)

Reviewer #4: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: (No Response)

Reviewer #4: No

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: (No Response)

Reviewer #4: No

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: (No Response)

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors of the article have satisfactorily incorporated all my comments and I have no further comments.

Reviewer #3: 1. Time spent need to be measured in the experimental results.

2. Limitation and Discussion Sections need to be inserted.

3. All metrics need to be calculated in the experimental results.

4. The parameters used for the analysis must be provided in table

5. Comparison with similar studies on a similar dataset need to be inserted (with references).

6. The cost associated with deploying these deep learning models, including the necessary hardware and software, is not addressed.

7. The architecture of the proposed model must be provided

8. The authors need to make a clear proofread to avoid grammatical mistakes and typo errors.

9. Enhance the clarity of the Figures by improving their resolution.

10. Add future work in last section (conclusion) (if any)

11. To improve the Related Work and Introduction sections authors are recommended to review this highly related research work paper:

a) Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

b) Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

c) Optimal Gasoline Price Predictions: Leveraging the ANFIS Regression Model

d) The effect of rebalancing techniques on the classification performance in cyberbullying datasets

e) Developing an efficient method for automatic threshold detection based on hybrid feature selection approach

f) Acoustic data detection in large-scale emergency vehicle sirens and road noise dataset

g) Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment

h) Predicting female pelvic tilt and lumbar angle using machine learning in case of urinary incontinence and sexual dysfunction

Reviewer #4: The manuscript used estimated reference evapotranspiration of multiple locations with distinct weather conditions using a federated learning approach, which allows for training ML models across multiple decentralized devices or servers holding local data samples, without exchanging them. They applied three ML models, including Random Forest Regressor (RFR), Support Vector Regressor (SVR), and Decision Tree Regressor (DTR), for each location. The review comments are presented in the following:

Title:

1. I do not understand why you recommend federated learning over centralized learning for estimating reference evapotranspiration. The title is “Federated learning based reference evapotranspiration estimation for distributed crop fields”. It means that you are proposing a federated learning, which is suitable for heterogeneous datasets whose sizes may span several orders of magnitude, for estimating a variable in three location (distributed) with quite similar datasets. What is wrong with applying distributed learning for estimating reference evapotranspiration?

Abstract:

1. The abstract does not justify why there is a need to apply a federated learning for estimating reference evapotranspiration.

Introduction:

2. The gap in the literature is not established in the introduction.

3. Remove Equation 1 from the introduction and present it in the materials and methods section.

4. It is stated in lines 35-37 that “Moreover, simple ML approaches also face limitations, particularly related to data centralization and may not effectively capture the diverse climatic conditions of different regions.” For this case, why do not we add climatic conditions as an input instead of considering a federated learning for estimating reference evapotranspiration.

5. I suggest merging the section of“2 Related work” within the introduction.

6. Lines 177-184: This paragraph is repeating the last paragraph of the introduction. Avoid repeating.

Methods:

7. Lines 285-286: “…. which has proven to be a viable solution for overcoming the challenges mentioned earlier and improving ETo estimates.” This sentence requires a reference. Otherwise, you have to justify it.

8. There are a lot figures in this section, while very little description is provided in the text. Explain what every figure/table shows in details in the text.

9. Figure 14 is not a good flowchart for the FL. It does not show how you integrate the local models.

10. Line 301: Remove ” from the end of the sentence.

11. Check Equation 3 and introduce all parameters in this equation appropriately.

12. Lines 303-304: Where is the description of this part?

13. Add an appropriate reference for any equation that it is not from your work.

14. More information should be added regarding how you calibrate the hyperparameters of the ML models.

15. Which software/libraries you used to implement the ML algorithms and FL?

16. Add a reliability analysis (10.1007/s40899-023-01021-y) to check how reliable the estimations made by your ML models are.

Results:

17. How much did FL model improve estimated reference evapotranspiration in comparison to using individual ML models?

18. There are a lot figures in this section, while very little description is provided in the text. Explain what every figure/table shows in details in the text.

19. What are the advantages and disadvantages of the FL model?

Discussion

20. What is the practical contribution of your work? How does it contribute to the practical field in the realm of improving water balance analysis?

21. The discussion should merge the findings of this work with other ones available in the literature.

Conclusion:

22. The conclusion should contain general findings from the study and present some take-home messages. Improve this section.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

Reviewer #4: No

**********

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Attachments

Attachment

Submitted filename: Review of the Manuscript PONE-D-24-15636R1.docx

PONE-D-24-15636R2Federated learning based reference evapotranspiration estimation for distributed crop fieldsPLOS ONE

Dear Dr. Tausif,

Thank you for submitting your manuscript to PLOS ONE. The reviewers recommend reconsideration of your manuscript following major revision. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. 

Please submit your revised manuscript by Oct 31 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Ghani Rahman

Academic Editor

PLOS ONE

Additional Editor Comments:

I have completed my evaluation of your manuscript. The reviewers recommend reconsideration of your manuscript following major revision. I invite you to resubmit your manuscript after addressing the reviewer comments.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #4: (No Response)

Reviewer #5: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #4: No

Reviewer #5: No

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #4: No

Reviewer #5: N/A

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #4: No

Reviewer #5: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #4: No

Reviewer #5: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors of the article have satisfactorily incorporated all my comments and I have no further comments.

Reviewer #4: The authors' responses are not detailed and some of the major comments were not addressed properly. For instance, they state in lines 41-42: "A universal ETo model cannot be created using traditional machine learning methods because they can’t handle diverse weather conditions."This is not correct. There are many papers on application of traditional machine learning methods for this purpose. It is not the gap in the literature. In addition, the authors' response to the comment why they applied "federated learning over centralized learning for estimating reference evapotranspiration" is not acceptable. It is important to note that there are many machine learning models and artificial intelligence techniques. Nevertheless, they should be used for solving appropriate problems. In other words, the authors proposed federated learning for estimating reference evapotranspiration, while it is not justified. Instead of federated learning, distributed learning, which is a suitable tool. can be used. Since the major contribution of this manuscript is proposinjg federated learning for estimating reference evapotranspiration, I suggest rejection.

Reviewer #5: 1. The author mentions that a "feature importance-based analysis" was conducted; however, the manuscript lacks details on how the importance of the features was calculated. Please provide a clear explanation and justification for the method used to determine feature importance, including any metrics, algorithms, or criteria employed in the analysis.

2. The manuscript lacks technical details about the proposed Federated Learning-based framework for estimating ETo. Readers would benefit from more specific information about the framework's design and implementation. For example, what type of architectural design was used on the client side in FL environment for estimating ETo? How many clients participated in the training process? How many communication rounds occurred between the server and clients to exchange trained weights? Additionally, if machine learning algorithms such as Random Forest Regressor (RFR), Decision Tree Regressor (DTR), or Support Vector Regressor (SVR) were trained on the client side, it is important to explain how the trained weights were aggregated on the server side, evaluated, and then sent back to the clients. Please provide appropriate details to address these concerns, and if possible, include code to further clarify your approach.

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Reviewer #1: No

Reviewer #4: No

Reviewer #5: No

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PONE-D-24-15636R3Federated learning based reference evapotranspiration estimation for distributed crop fieldsPLOS ONE

Dear Dr. Tausif,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

Ghani Rahman

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #4: (No Response)

Reviewer #5: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #4: Partly

Reviewer #5: No

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #4: No

Reviewer #5: (No Response)

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #4: (No Response)

Reviewer #5: (No Response)

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #4: (No Response)

Reviewer #5: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors of the article have satisfactorily incorporated all my comments and I have no further comments.

Reviewer #4: The justification of using federated learning seems to be improved. But not satisfactory because the results of traditional ML models rely on their input data. I recommend to modify this sentence: "Traditional machine learning methods of ETo are very effective in many contexts but limited in the scope of their applicability to specific geographical locations". When you provide traditional ML models with a local dataset, they cannot be used globally. Therefore, this is a problem related to the input data rather than ML models.

Additionally, there is no postprocessing of the ML results. Their preformances were copared only based on a few metrics. Therefore, there is a need to conduct futher analyses on the ML results, like reliability analysis or uncertainty analysis (10.1007/s40899-023-01021-y and 10.1016/j.compag.2020.105653). This is neccessary to do because you are comparing different types of ML models. Also, this can improve the discussion section and provide a better perspective of the results.

Reviewer #5: The authors' responses to the review comments lack sufficient detail, and my major points raised in the previous review have not been adequately addressed. There is a significant absence of technical details throughout the manuscript. Although the authors claim to utilize Federated Learning approaches for estimating evapotranspiration in distributed crop fields, they fail to provide specific information on how this technology is implemented within their study. The manuscript does not explain in detail how the methodology used and the steps taken to integrate Federated Learning into their analysis. Furthermore, the responses provided mainly discuss the general principles of Federated Learning rather than its application to the authors' specific research context. This generic explanation does not demonstrate how the authors' approach is uniquely tailored to their work, making it challenging to assess the validity and relevance of their findings. To enhance the manuscript, I recommend that the authors include technical descriptions of their methodology, particularly, how Federated Learning was specifically applied to their evapotranspiration estimation. This information is crucial for readers to understand and evaluate the effectiveness of the proposed approach.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #4: No

Reviewer #5: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Federated learning based reference evapotranspiration estimation for distributed crop fields

PONE-D-24-15636R4

Dear Dr. Tausif,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Ghani Rahman

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #5: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #5: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #5: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #5: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #5: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #5: No

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