Detection Method for Rice Seedling Planting Conditions Based on Image Processing and an Improved YOLOv8n Model
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsOverall, the paper presents a comprehensive approach to addressing the challenges associated with assessing rice seedling planting conditions, utilizing image processing and deep learning techniques. Here's a critical review of the paper:
1. **Clarity and Structure**:
The paper is well-structured, with clear sections detailing the problem statement, methodology, experiments, and results. The introduction effectively sets the context, and the methodology section provides a detailed explanation of the proposed approach. However, some parts, especially in the methodology and results sections, could be further simplified for easier comprehension, particularly for readers less familiar with image processing and deep learning concepts.
2. **Literature Review**:
The literature review effectively contextualizes the research problem within existing literature, discussing relevant studies on seedling detection using image processing and deep learning techniques. However, the review could benefit from a more critical analysis of the limitations of previous approaches and how the proposed method addresses these limitations.
3. **Methodology**:
The methodology is thorough, detailing the image acquisition process, preprocessing steps, and the implementation of the YOLOv8n model. The integration of image processing techniques for centroid detection and the enhancements made to the YOLOv8n model are well-explained. However, some technical details could be elaborated further to facilitate reproducibility and understanding.
4. **Experimental Evaluation**:
The experimental evaluation provides comprehensive results, including metrics such as precision, recall, and mean average precision (mAP). The comparison with other models adds value, demonstrating the superiority of the proposed approach. However, the paper could benefit from more detailed discussions on the limitations of the experiments, such as dataset biases or potential overfitting issues.
5. **Conclusions and Implications**:
The conclusions effectively summarize the key findings and contributions of the research. The implications for real-world applications, particularly in rice field management, are well-articulated. However, the paper could further discuss potential avenues for future research, such as exploring the scalability of the proposed approach to larger datasets or adapting the methodology to other crops or agricultural contexts.
6. **Language and Presentation**:
The language used in the paper is generally clear and professional. However, there are instances of long, convoluted sentences that could be simplified for better readability. Additionally, some figures and equations could be better integrated into the text for improved clarity.
Here are some suggestions for improvements:
1. **Clarity and Structure**:
- Break down complex sentences into simpler ones to improve readability.
- Use subheadings within sections to further delineate the content and make it easier for readers to navigate.
2. **Literature Review**:
- Provide a more critical analysis of previous studies, highlighting their strengths and weaknesses in addressing the research problem.
- Discuss how the proposed approach builds upon and improves upon existing methodologies.
3. **Methodology**:
- Include more detailed explanations and rationale behind the selection of specific image processing techniques and deep learning models.
- Provide code snippets or algorithmic descriptions to aid readers in understanding the implementation details.
4. **Experimental Evaluation**:
- Discuss potential biases or limitations of the experimental setup, such as dataset imbalance or selection bias.
- Include qualitative analysis, such as visualizations or case studies, to complement the quantitative results and provide a more comprehensive evaluation.
5. **Conclusions and Implications**:
- Offer insights into future research directions, such as exploring the transferability of the proposed approach to different agricultural contexts or investigating the scalability of the methodology to larger datasets.
- Discuss practical implications and potential applications of the research beyond rice seedling planting, such as in other crops or in broader agricultural automation systems.
6. **Language and Presentation**:
- Use clear and concise language, avoiding unnecessary technical jargon or overly complex terminology.
- Ensure that figures and equations are properly integrated into the text and adequately explained for readers unfamiliar with the subject matter.
In summary, the paper presents a promising approach to addressing the challenges of rice seedling planting condition assessment using image processing and deep learning techniques. With some refinement in presentation and critical analysis, the paper could enhance its contribution to the field of agricultural automation and precision farming.
Comments on the Quality of English LanguageOverall, the English quality of the text is quite good. However, there are some areas where improvements could be made for better clarity and readability:
1. **Sentence Structure**:
- Some sentences are long and convoluted, making them difficult to follow. Breaking them down into shorter, more concise sentences would improve readability.
- Example: "The proposed approach combines image processing and deep learning to develop an algorithm for detecting the planting status of rice seedlings by transplanting machines."
2. **Technical Language**:
- Ensure that technical terms and concepts are explained clearly, especially for readers who may not be familiar with image processing or deep learning.
- Example: "The SimAM attention mechanism introduces a three-dimensional module architecture for space and channel..."
3. **Transition Words**:
- Incorporate more transition words to improve the flow between sentences and paragraphs.
- Example: "Additionally", "Furthermore", "Moreover", "In summary", etc.
4. **Conciseness**:
- Some sections could be made more concise by removing redundant phrases or unnecessary details.
- Example: "For the detection of seedling qualification and floating status, this paper improves upon the YOLOv8n model by using ASPP to replace SPPF for extracting multi-scale feature information of qualified and floating seedlings."
5. **Consistency**:
- Ensure consistency in terminology and formatting throughout the paper.
- Example: Ensure that abbreviations are defined upon first use and consistently used thereafter.
6. **Clarity**:
- Clarify any ambiguous phrases or terms to avoid confusion.
- Example: "However, the paper could benefit from more detailed discussions on the limitations of the experiments..."
7. **Proofreading**:
- Conduct thorough proofreading to correct any grammatical errors or typos.
- Example: "The implications for real-world applications, particularly in rice field management, are well-articulated."
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsPaper is generally well written and structured. Obtained results and ablation experiments are confirming validity of proposed approach. Overall, good work.
However, there are some issues that should be resolved:
- in order to improve clarity of presented methodology, some kind of block diagram with visual description of all steps (preprocessing, segmentation, detection, ANN,...) should be introduced;
- equation (2) - all variables should be explained;
- figure 3.: part of the image related to 3-D weights and part of the image "Extending" are the same - is it correct?
- fonts of the equations should be changed according to the rest of the text;
- there is some strange formatting in the references section ([J], some missing info,...).
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsAll comments from the first round of review were addressed properly. No further suggestions at this point. There are still some issues with font size in some equations.
Author Response
Dear reviewer: Sorry, I didn't change the font size in the equation before. I have now corrected sixteen equations and symbols in the article whose fonts did not match. Thanks.
