Low-Damage Grasp Method for Plug Seedlings Based on Machine Vision and Deep Learning

Response to Reviewer 1 Comments

1. Summary

Thank you very much for taking the time to review this manuscript and for providing your valuable comments and suggestions. We have responded in detail to your concerns and have highlighted the changes in the resubmitted manuscript for your convenience.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes

Is the research design appropriate?

Yes

Are the methods adequately described?

Can be improved

Are the results clearly presented?

Can be improved

Are the conclusions supported by the results?

Can be improved

Are all figures and tables clear and well-presented?

Yes

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: Contributions and prominent features of the article can be given item by item at the end of the Introduction.

Response 1: Thank you for your suggestion. We have added a clearly itemized list of the main contributions at the end of the Introduction (Page 3, Line 125).

Comments 2: The novelty of the proposed methodology should be emphasized.

Response 2: We agree and have revised the end of the Introduction (Page 3, Line 121) to more clearly highlight the novelty of our approach.

Comments 3: A figure caption should be provided for Figure 11.

Response 3: We have added a detailed and descriptive caption for Figure 11 (now Figure 12) to clarify the contents of the image and its relevance to the results.

Comments 4: Hyperparameters and training settings used in comparative methods should be clearly stated.

Response 4: Thank you for your suggestion. The hyperparameters and training settings used in the comparison method in the article are the same as those used in our proposed LRGN (Section 3.1), but we did not explain them, so we added relevant descriptions. (Page 12, Line 377)

Comments 5: The number of samples used in training and testing and augmentation techniques (rotation, crop, etc.) should be given in tables.

Response 5: We have added a new table in Section 3.3.1 (Page 12, Line 372) that presents the number of training and test samples in Cornell datasets, along with the augmentation strategies used.

Comments 6: The effects of external variables such as different soil conditions and light environments on the model should be discussed.

Response 6: Thank you for your valuable suggestion. We agree that environmental factors such as soil conditions and lighting can affect model performance in real-world applications. However, in our current study, all image data were collected inside a closed light-controlled box, where lighting and background conditions were kept consistent to minimize external interference. Since the plug seedlings are cultivated by the same company, the soil conditions are also consistent. As a result, the influence of varying environmental factors was minimal and not explicitly analyzed in this work.

Nevertheless, we recognize the importance of evaluating model robustness under diverse conditions. In future work, we plan to test the proposed method under varying light intensities, soil types, and background complexity to better understand its adaptability and ensure reliable deployment in actual greenhouse or field environments.

Comments 7: Performance degradation at locations close to the edge of the seedling has been stated, but this has not been reported quantitatively.

Response 7: We appreciate this insightful suggestion. In Section 3.3.2 (Page 14, Line 437), we added a quantitative analysis of grasp accuracy near edge regions.

Comments 8: Although the success rates obtained are given in the results section, the effects of these results on agricultural production are not detailed.

Response 8: Thank you for your suggestion. We have expanded the final part of the Conclusion section (Page 17, Line 487) to include the potential practical impact.

Response to Reviewer 2 Comments

1. Summary

Thank you very much for taking the time to review this manuscript and for providing your valuable comments and suggestions. We have responded in detail to your concerns and have highlighted the changes in the resubmitted manuscript for your convenience.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes

Is the research design appropriate?

Yes

Are the methods adequately described?

Yes

Are the results clearly presented?

Yes

Are the conclusions supported by the results?

Yes

Are all figures and tables clear and well-presented?

Yes

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: Line 67: Using the figure 18 (3) from [5], which illustrates what is meant by "... the coordinates of the extreme point E at the boundary...". Also explain V1 and V2. This gives the reader more clarity.

Response 1: Thank you for the suggestion. We have revised the corresponding paragraph (Page 2, Line 68) to explicitly explain the meanings of V1 and V2. Additionally, we have cited Figure 18(3) from [5] to help clarify the definition and role of the extreme point E. This revision improves the reader’s understanding of our method.

Comments 2: Line 112: End of the line: Use “protect” instead of “avoid”.

Response 2: We have revised the sentence (Page 3, Line 115) by replacing “avoid” with “protect” to make the expression more accurate and appropriate.

Comments 3: Line 135: Begin of the line: Write “seedlings” insteat of “seed-lings”; Line 171: Write “system” insteat of “sys-tem”; Line 181: Write “prediction” insteat of “pre-diction”.

Response 3: Thank you for pointing out these typographical errors. We have corrected “seed-lings” to “seedlings”, “sys-tem” to “system”, and “pre-diction” to “prediction” in the revised manuscript.