Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds

Round 1

Reviewer 1 Report

1. The following “the endosperm over 1,500 epochs with just 15 training images. The endosperm/seed area ratio, particularly within the 50-60% range covering over 50% of the samples, emerged as a significant metric for evaluating the viability of seed batches” is not required in abstract section.
2. Author says in the introduction “Addressing these challenges requires solutions that not only increase agricultural productivity in terms of land area but also optimize resource usage”. What they mean by challenges.
3. The reference citation style is wrong.
4. Introduction section is too small. It requires more information. For a good research paper, previous works to be clearly projected.
5. In YOLOv8 architecture there are different versions are present. Which one the authors have used in the manuscript.
6. A detailed information to be included about the More recent references are required to support the present research.

7. A major concern is language. A native language editing is required.
8. More recent references are required to support the present research. The present numbers are not sufficient.

Extensive editing of English language required

Author Response

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Reviewer 2 Report

The manuscript presents a ML approach to assessing agricultural seed vigor using a YOLOv8 model applied to X-ray images of seeds. It aims to automate the classification of seeds based on their physiological potential. While the concept of employing YOLOv8 in this context is noteworthy, several areas in the manuscript require further development and clarification:

1) No literature review: The manuscript needs a more comprehensive section on related studies. It is essential not only to discuss the results of previous research but also to identify gaps or limitations in those studies.

2) The training dataset is described as comprising only 15 images, which raises concerns about the model's robustness and generalizability. The authors should address how this limitation was mitigated or consider expanding the dataset to ensure more reliable results.

3) The presence of two Figures with same name of Figure 4 on pages 4 and 5 is likely a mistake and needs correction.

4) The quality and detail of Figure 3, illustrating the YOLOv8 architecture, are inadequate. This figure should be redrawn for clarity.

5) There is no comparison of the applied method with other seed assessment techniques. Include a comparative analysis in tabular form with other models and methods.

6) The description of Table 1 is placed in a footnote, which can lead to it being overlooked. Integrating this description into the main text

7) The discussion on data augmentation in the methodology is unclear. Provide a detailed explanation of the augmentation techniques used in training the model.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Authors have modified the manuscript and it looks very good article

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Almost all the comments have been addressed. A table should be added in order to compare the current research with previous ones.

Author Response

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Author Response File: Author Response.pdf