PONE-D-24-05247Economic Factors and Rice Production: A Machine Learning Analysis of Correlations and Predictive ModelsPLOS ONE

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Academic Editor

PLOS ONE

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Additional Editor Comments:

1. Why author took the data set up to 2020; is there any scientific reason- suggestion to add data set upto 2023.

2. Eq 2-5: author must add reference for cite the formulae.

3. Author must add parameter table and pseudo code all proposed algorithm

4. Author must add scatter plot, time series plot, histogram plot for better accuracy of result.

5. Author must add some more discussion in terms of comparison with other study and advantages of proposed model, future scope of the research.

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Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: N/A

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Title: Consider a more specific title that reflects the main findings of your study. For example, "Impact of Economic Indicators on Rice Production: A Machine Learning Approach in Sri Lanka."

Abstract:

Consider including a sentence on the practical implications of the study's findings.

Introduction:

Clearly state the research questions or hypotheses.

Methodology:

Describe the preprocessing steps in more detail, including how missing or inconsistent values were handled.

Results and Discussion:

Discuss the implications of the results in the context of existing literature and theory.

Address any limitations of the study and suggest avenues for future research.

Conclusion:

Summarize the key findings of the study and their implications for agricultural policy and practice.

Highlight the contribution of the study to the existing literature and any practical applications of the findings.

General:

Ensure consistency in terminology and formatting throughout the manuscript.

Reviewer #2: 1. The novelty and contribution of the paper should be clearly stated.

2. What is the contribution of this finding in addressing the challenges that were identified in the introduction?

3. What authors were motivated to conduct this type of study, and why did they select the Sri Lanka as a case study?

4. Plenty of work has already proved the potential of various ML models across the world. What is different being using the applied models in this study?

5. What were the used input parameters?

6. Provide the range of statistical evaluating standards and support them by citing suitable references. The authors can cite https://doi.org/10.1007/978-3-031-12641-3_31; Doi:10.4704/nq.2022.20.14. NQ880113; https://doi.org/10.1063/5.0161098; https://doi.org/10.1063/5.0132387; DOI: 10.1088/1755-1315/1084/1/012054; DOI: 10.1088/1755-1315/1032/1/012016

7. Likewise, what is the transferability of such results to other locations in terms of impact or usefulness?

8. Include all the figures and tables at their respective place in the text.

9. Authors need to consult with a native English speaker for language improvement.

10. Reveal more practical debate in the discussion section and explain the usefulness and practical implications of this work.

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Reviewer #1: No

Reviewer #2: No

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Impact of Economic Indicators on Rice Production: A Machine Learning Approach in Sri Lanka.

PONE-D-24-05247R1

Dear Dr. %Namal Rathnayake%,

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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Kind regards,

Dr. Sandeep Samantaray

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: