Sorting of Mountage Cocoons Based on MobileSAM and Target Detection

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

present paper "Sorting of Mountage Cocoons Based on MobileSAM and Tar- 2 get Detection" is attractive and applicable. the differences of proposed method has been pointed well. the methodology is good. But i'd like to get more information about these:

1. The portion of test set and validation set is little. Is not it?
2. According to table 1, test data of reelable cocoon is 7327, but in figure 17, the results don’t match the test data.
3. please improve conclusion section and add future work.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

In this paper, a cocoon detection algorithm S-YOLOv8_c based on the collaboration of MobileSAM and YOLOv8 for silkworm cocoons within the mountage was proposed. Experimental results indicated the model could accurately detect reelable cocoons, waste cocoons, and double cocoons, meeting the requirements of cocoon quality sorting. It is interesting. However, some revisions should be made.

1. Please check that all Figures are relevant / sequent to the contents of the manuscript (section 2.2).
2. Section 3.1, page 11, should be moved to Materials and Methods section.
3. Section 4 Conclusions, page 19, could you please give more details about your study and results.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This paper presents a detailed examination of machine learning applications in the sorting of Mountage Cocoons, with a particular focus on the SAM + YOLOV8 network. The document is well-written; however, several revisions are necessary:

Major:

1) You have discussed implementing machine learning for the sorting of montage cocoons; however, you have only used a normal imaging method, which covers a maximum of 50% of the surface. Please discuss how you plan to mitigate this issue. Have you ever considered implementing two-sided imaging?

2) While the proposed method shows promise, there is a noticeable absence of robustness testing under varying conditions such as different lighting and cocoon sizes. A detailed error analysis and the system's performance under these varied conditions would be highly beneficial.

Minor:

1) Line 11: This should be revised to: "Corresponding author."

2) The abstract requires rewriting to be concise and clear; transfer detailed information to the introduction.

3) Line 141: The ratio should be expressed as a percentage.

4) Figure 1 should be repositioned to follow its mention in the text at line 147.

5) Lastly, a section on future work should be included to suggest directions for further research.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank authors for answering my comments. All my questions were answered.