PONE-D-24-43399Lightweight wavelet-CNN tea leaf disease detectionPLOS ONE

Dear Dr. Xu,

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,

Shahid Rahman, PhD

Academic Editor

PLOS ONE

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Additional Editor Comments (if provided):

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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: Yes

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

Reviewer #1: Yes

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

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Reviewer #1: Yes

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Reviewer #1: 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: 1. The manuscript is technically sound and well-structured. The integration of 2D DWT, MobileNetV3, and CBAM is innovative and improves tea leaf disease detection. The methodology is rigorous, with appropriate dataset augmentation, architecture optimizations, and comparative studies. The results are robust, supported by strong statistical evidence.

2. Statistical analysis is appropriately performed using standard metrics (accuracy, precision, recall, F1-score). Ablation studies and comparative analyses validate improvements. The dataset splitting (6:2:2) and use of focal loss effectively address class imbalance.

3. Data is publicly accessible at https://github.com/jingyang-create/tea-sickness, meeting PLOS ONE’s data availability standards. The repository includes necessary materials for replication.

4. The manuscript is clear but requires minor proofreading for grammatical and typographical errors.

5. Recommendations for Improvement

1. Abstract Refinement:

• The content is detailed but could be improved by clearly separating key points to make it more accessible to a broader audience.

• Consider highlighting the practical applications at the beginning for better emphasis.

2. Introduction Depth:

• The literature review could be broadened to provide a clearer comparison of the advancements made by WaveLiteNet in relation to other lightweight models, such as SE-DenseNet-FL and TealeafNet.

3. Clarity in Methods:

• The section on the VisuShrink thresholding method is too technical and does not offer a sufficient explanation for readers who are not familiar with wavelet transformations.

• Include a simple diagram to illustrate the DWT process for better understanding.

4. Dataset Limitations:

• Figures 3, 4, and 5 are unclear and need better resolution for better understanding.

• It is not clear if data augmentation was only applied to the training set. If applied to the whole dataset, data leakage might occur. The authors should clarify this and explain how they addressed it.

5. Evaluation Metrics:

• Although comprehensive metrics are utilized, incorporating precision-recall curves or ROC curves could provide additional support for the claims related to the model's robustness.

6. Comparative Analysis:

• Expand the comparative analysis to cover real-world deployment scenarios or latency on mobile devices.

• While the authors propose that WaveLiteNet is suitable for deployment, they do not provide supporting evidence, such as information on processing speed, memory usage, or performance on low-resource devices.

7. Ablation Study Depth:

• The ablation study on CBAM could explore in greater detail why CBAM outperforms SENet or ECA in this particular use case.

8. Ethical Considerations:

• Although not a primary focus, mentioning ethical considerations regarding data usage, such as ensuring farmer consent for data collection, would improve transparency.

9. Writing Style:

• Certain sections, such as the Results and Analysis, are too verbose and could be condensed to improve readability without sacrificing important content.

10. Supplementary Data:

• Including a table or appendix that details the specific hardware performance, such as inference times on different GPUs, would be useful for readers interested in the feasibility of deployment.

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Reviewer #1: No

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PONE-D-24-43399R1Lightweight wavelet-CNN tea leaf disease detectionPLOS ONE

Dear Dr. Xu,

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.

Please submit your revised manuscript by Apr 14 2025 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,

Shahid Rahman, PhD

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:

1. Sections, Sub Sections are not in good format. Such Material and Method (1.,1.1, 1.1.1) etc.

2. Some Figures are not transparent such 3, 5 etc.

3. Follow the overall standard format of the Research article according to the PLOS ONE Criteria.

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[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.

Lightweight wavelet-CNN tea leaf disease detection

PONE-D-24-43399R2

Dear Dr. Xu,

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,

Shahid Rahman, PhD

Academic Editor

PLOS ONE

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