-->PONE-D-26-13218-->-->CT-PestNet: A Novel Crop Pest Classification Model Based on Cross-scale Feature Distillation Enhancement and Vision Transformer with Local Token Interaction-->-->PLOS One

Dear Dr. Li,

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Reviewers' comments:

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-->Comments to the Author

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

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Reviewer #1: Partly

Reviewer #2: Yes

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

Reviewer #1: No

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: No

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: No

Reviewer #2: No

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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: Summary:

This paper presents a novel hybrid CNN-Transformer model for crop pest classification, with two interesting modules (CFDE for multi-scale feature distillation and LTIR for local token interaction). The experimental work is extensive and the results show clear improvements over several baselines. However, the manuscript has three major weaknesses that must be addressed before publication:

1. Lack of statistical analysis – no significance tests, no variance reporting.

2. Missing computational cost comparison – parameters, FLOPs, inference time.

3. Poor English – numerous grammatical errors and non-standard phrasing.

Specific recommendations for revision:

- Add a statistical comparison table with p‑values (e.g., McNemar’s test) for the main accuracy/F1 differences.

- Report results from at least 3 independent runs (mean ± std) for all models.

- Add a table comparing model size (parameters), FLOPs, and inference time per image.

- Provide the source code and a brief README for reproducibility.

- Undergo professional language editing.

- Add an ethics statement regarding the use of insect images (even if publicly sourced).

- Correct minor formatting issues (e.g., “Fig 1.(a)” → “Fig. 1(a)”, remove stray numbers on pages 17 and 9).

Overall recommendation: Minor revision – the scientific contribution is valid, but the presentation and statistical rigour need improvement.

Reviewer #2: The manuscript PONE-D-26-13218, titled “CT-PestNet: A Novel Crop Pest Classification Model Based on Cross-scale Feature Distillation Enhancement and Vision Transformer with Local Token Interaction” presents a well-structured and technically sound deep learning framework that integrates CNN and Transformer architectures for crop pest classification. The proposed CT-PestNet model, along with CFDE and LTIR modules, reflects a thoughtful attempt to address both global and local feature extraction challenges. The experimental validation on benchmark datasets (IP102 and D0) is comprehensive, and the inclusion of visualization analyses (e.g., Grad-CAM) strengthens the interpretability of the results. Overall, the study demonstrates promising performance improvements and contributes to the growing literature on AI-driven agricultural monitoring. However, the manuscript requires revision before it can be considered for publication. The following comments are provided to improve clarity, rigor, and alignment with PLOS ONE standards:

1. The abstract is informative but could be improved by including quantitative performance metrics (e.g., accuracy gains) to better highlight the contribution. Currently, claims such as “outperforms most existing models” remain too general.

2. Although limitations of CNN and Transformer models are mentioned, the precise research gap is not explicitly defined. It would be beneficial if the novelty of CFDE and LTIR is positioned more clearly against recent hybrid models.

3. The related work section primarily summarizes prior studies without critically analyzing their limitations. A comparative synthesis would strengthen the motivation for the proposed approach.

4. Design decisions such as kernel sizes (7×7, 5×5, 3×3) and token dimensions (D = 1024) are presented without justification. A short rationale or sensitivity analysis is recommended.

5. The manuscript reports accuracy and F1-score but lacks statistical significance testing or confidence intervals. It is suggested that variability across runs be reported.

6. Given the high performance on the D0 dataset (99.73% accuracy) , concerns regarding overfitting should be discussed, particularly considering dataset size.

7. The manuscript identifies class imbalance (e.g., fewer egg/larva samples) , yet no mitigation strategy (e.g., reweighting, augmentation) is implemented. This should be addressed experimentally.

8. While CFDE and LTIR contributions are evaluated, the study lacks component-level analysis (e.g., SENet contribution, kernel variation impact).

9. Some figures (e.g., Fig 6–7) are referenced without detailed interpretation in the text. Figure captions should be more self-contained and explanatory. Picture of rice stem fly in Figure 4 seems licensed as logo shows on it, please use your own pictures of these pests otherwise get permission from the original sources. Same comment must be dealt with for all other such pictures.

10. It is suggested that the authors cite recent relevant work to strengthen the manuscript’s broader context. In particular, Ghaffar et al. (2025) (https://doi.org/10.55627/pbiotech.003.04.1702) on AI-driven plant stress management and Zulkiffal et al. (2025) (https://doi.org/10.55627/pbiotech.003.04.1709) on data-driven wheat improvement for rust resistance provide useful complementary perspectives. Their inclusion would enhance the interdisciplinary relevance of the study.

11. Minor grammatical inconsistencies are present (e.g., “organically combines CNN and Transformer” ). A careful language revision is recommended for clarity and fluency.

12. The conclusion largely restates earlier content without offering forward-looking insights or practical implications. It could be strengthened by highlighting future research directions.

13. Although no human/animal subjects are involved, a brief clarification regarding data sourcing ethics (web-scraped images) would improve transparency.

14. The manuscript claims reduced computational burden but does not provide FLOPs, parameter count, or inference time comparisons, which are important for practical deployment.

15. Some references are preprints (e.g., arXiv) and may need to be supplemented with peer-reviewed sources, as PLOS ONE encourages robust and verifiable citations.

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Reviewer #1: No

Reviewer #2: No

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CT-PestNet: A Novel Crop Pest Classification Model Based on Cross-scale Feature Distillation Enhancement and Vision Transformer with Local Token Interaction

PONE-D-26-13218R1

Dear Dr. Li,

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,

Peng Geng

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 #1: (No Response)

Reviewer #2: 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 #1: (No Response)

Reviewer #2: Yes

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

Reviewer #1: (No Response)

Reviewer #2: Yes

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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 #2: 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 #2: 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 have comprehensively addressed all concerns raised in my previous review:

1. Technical completeness – Provided detailed mathematical formulations and architectural diagrams for CFDE and LTIR.

2. Experimental rigor – Added statistical significance testing (paired t-tests, p-values), standard deviations from five independent runs, and error bars in figures.

3. Computational analysis – Added Table 3 comparing parameters, FLOPs, and inference time.

4. Data availability – Corrected all data URLs and added an Ethics Statement clarifying data sources and usage terms.

5. Code sharing – Explained institutional confidentiality restrictions and offered private access for reviewers.

6. Language quality – Thoroughly revised and edited the manuscript for clarity and fluency.

7. Ablation studies – Added detailed component-level analysis including SENet contribution and kernel size variations.

8. Limitations – Acknowledged class imbalance challenges and proposed future research directions.

Final Recommendation

I recommend ACCEPTANCE of the manuscript in its current form.

The revised manuscript is technically sound, methodologically rigorous, ethically transparent, and well-written. It makes a meaningful contribution to the field of AI-driven crop pest classification and meets all PLOS ONE publication standards.

Reviewer #2: The authors have significantly improved improved the manuscript, I endorse its its publication in PLoS One.

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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: Yes: Dr. Nabil Abo Kaf

Reviewer #2: No

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