Detection of Mulberry Leaf Diseases in Natural Environments Based on Improved YOLOv8
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsDear Authors,
After a careful review of the manuscript titled “Detection of Mulberry Leaf Diseases in Natural Environments Based on Improved YOLOv8”, I have made the following suggestions and comments for improving the manuscript.
Strengths:
1. The introduction of the Multi-Dimension Feature Attention (MDFA) module is a notable improvement that integrates important features at multiple dimensions, enhancing the overall detection accuracy. However,
2. The proposed YOLOv8-RFMD model demonstrates a significant increase in precision and efficiency for detecting small lesions in mulberry leaves, which is critical for early disease detection and management.
3. The model achieves impressive results with a mAP50 of 94.3% and a mAP50:95 of 67.8%, indicating its high accuracy and reliability compared to the original YOLOv8 model.
4. The work is highly relevant to real-world applications, providing a theoretical reference for automated spraying operations, which can significantly benefit mulberry farming by preventing yield loss due to diseases.
5. The manuscript is well structured and described. However, some modifications are needed to improve the manuscript
Comments:
1. The authors mentioned the representation of weights for the degree of attention in Figure 6 in the manuscript. If possible, please include a legend with the weightage values for better understanding.
2. Please ensure that the evaluation metrics are cited.
3. Please remove the citation from the section sub-heading 2.3.4 and include it in the corresponding section text.
4. Some English writing typos should be corrected.
Suggestions for improvements:
1. It would be beneficial to include a comparative analysis with other state-of-the-art models to highlight the superiority of the proposed YOLOv8-RFMD model. This comparison could be presented in terms of accuracy, efficiency, and computational requirements.
2. An ablation study that demonstrates the impact of each improvement (MDFA, RFMD Module, NWD loss function) on the overall performance of the model would provide deeper insights into the contributions of each component.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsDear Authors,
the article is of scientific interest, but in this form of presentation it requires serious improvement. First of all, the article is of practical interest for the diagnosis of mulberry diseases, but the authors do not disclose this in the manuscript. This definitely needs to be improved. I consider the improvement methodology you propose to be truly original and relevant at the present time. But the gap in this area is clearly addressed in the methodological part of the manuscript. You have unnecessarily overloaded it with a technical part that requires reduction. It is necessary to provide a geographic location of the plants used, preferably in the form of a table. The conclusions are not sufficiently conclusive and require further development in accordance with the newly formulated hypothesis in the Introduction.
Comments for author File: Comments.pdf
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsDear Authors,
After a careful review of the revised manuscript titled “Detection of Mulberry Leaf Diseases in Natural Environments Based on Improved YOLOv8”, I have made this comment.
1. The authors responded and modified the contents as per the suggestions given.
2. Authors are already given the color representations for understanding. But the weightage values give a clear idea to the readers. If possible, please include a legend with the weightage values for better understanding.
Reviewer 2 Report
Comments and Suggestions for AuthorsDear Authors,
I am pleased with the correction of the manuscript. You have supplemented the manuscript with data and made corrections. In the study, the proposed a target model for detecting mulberry leaf diseases in nature. Which is of great practical importance. The hypothesis does not raise questions and deserves a positive assessment. The review of the study results was selected accordingly, as were the statistical methods used to analyze it. The article takes into account comments on the methodology. The analysis and conclusion for each chapter are sufficient and unobjectionable. References to literature sources have been corrected. The results of previous studies by other authors are taken into account. I recommend for the journal Forests.
