PONE-D-25-42641Identifying Graft Incompatible Rootstocks for Sweet Cherry Through Machine Learning AlgorithmsPLOS ONE

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Additional Editor Comments:

Please consider the comments from two reviewers.

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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

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: 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

Reviewer #2: Yes

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

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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: This study evaluated the graft incompatibility by identifying Graft Incompatible Rootstocks for Sweet Cherry Through Machine Learning Algorithms.It is meaningful to study the topic, and the whole story is complete.However, minor revisions are necessary to increase the readability and rigor of the article.

Comments are as follows,

1.Problems in the abstract:The author has made a lot of descriptions of the research method, but only one sentence describes the result analysis, and the result analysis part should be emphasized.

2.For Figures 3 and 4, please explain why all grafted combinations are not shown? It is recommended to supplement the phenotypic picture of all grafted assemblies completely.

3.In the discussion part, it is recommended to discuss the data of this study, and it is recommended to cite the corresponding chart when discussing the content of your own research.

Reviewer #2: This study employs machine learning (ML) techniques to evaluate graft compatibility in cherry rootstocks. Three ML algorithms—Principal Component Analysis (PCA) with clustering, Random Forest (RF) with SHAP interpretation, and Bayesian ranking—were applied to analyze phenotypic data from graft unions, identifying key compatibility traits and probabilistically ranking genotype performance. The research identified five high-compatibility candidate genotypes (e.g., 55 K 104) and validated the superiority of Gisela 6. While manual evaluation of 12-month phenotypic data can assess basic compatibility, ML provides unparalleled capabilities in quantifying multi-trait interactions, revealing hidden biological mechanisms, and enabling risk-informed breeding decisions. Although the study shows significant potential, several critical issues require resolution. Major revision is recommended to address the following points:

1.Although precedents exist for applying machine learning in plant science, the paper fails to adequately contextualize its work against these prior studies and does not clarify the unique contributions of this research. For instance, the specific novelty of using SHAP and Bayesian ranking in the context of graft compatibility is not thoroughly discussed; it is only vaguely mentioned as 'overcoming the limitations of traditional statistics.' It is recommended that the authors add comparisons with similar studies (e.g., machine learning applications in apricot grafting, Reference 45) in the Introduction or Discussion section to highlight the advantages of their approach."

2.The study focuses on short-term compatibility but does not explore long-term agronomic traits (e.g., yield, stress resilience), which limits its translational value. It is recommended that the Discussion or Conclusion section emphasize the need for future multi-environment trials to validate the robustness of machine learning predictions, thereby enhancing practical applicability.

3. In the Methods section (2.2), the analysis mentions "n=5" but does not clarify whether this refers to sub-samples per genotype-scion combination or the total sample. This ambiguity may compromise reproducibility.

4. Hyperparameters for the Random Forest and Bayesian models are not detailed. For example, the Bayesian model uses "4 chains, 1,000 tuning iterations" but omits convergence criteria (e.g., Gelman-Rubin statistic). It is recommended to add a subsection (e.g., "2.2.3 Machine Learning Parameters") listing key parameters to ensure reproducibility.

5. Figure 3 illustrates graft development but is only briefly mentioned in the Results (Section 3.1), with no linkage to quantitative data (e.g., graft bud growth rate). It is advised to directly reference Figure 3 in the Results to strengthen visual evidence.

6. K-means clustering did not validate the cluster number selection, potentially leading to subjective grouping (Figure 5). It is recommended to supplement clustering with validation metrics (e.g., elbow method) or acknowledge this limitation in the Discussion.

7. The SHAP summary plot (Figure 7) shows a negative impact of "necrotic layer formation," but the Discussion fails to explain its physiological implications. It is suggested to add a paragraph correlating SHAP outputs (Figures 7–8) with known mechanisms (e.g., carbohydrate transport barriers, Reference 48).

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Reviewer #1: No

Reviewer #2: No

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Identifying Graft Incompatible Rootstocks for Sweet Cherry Through Machine Learning Algorithms

PONE-D-25-42641R1

Dear Dr. AYDIN,

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.

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Kind regards,

Yuan Huang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewer #1:

Reviewer #2:

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 #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: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: 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 #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

**********

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: (No Response)

Reviewer #2: The authors have adequately addressed my comments, with the remaining issues pertaining to minor terminological inconsistencies. The enhancement in explicating the novelty of the machine learning framework and the improvement in reproducibility are substantial, meeting the journal's publication standards. It is recommended that the editorial office assist in refining terminology during the typesetting phase prior to acceptable for publication.

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Reviewer #1: No

Reviewer #2: No

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