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Based on the reviewer's responses, the author(s) have addressed all the major and minor comments. Now the paper can be accepted and ready for publication.
[# PeerJ Staff Note - this decision was reviewed and approved by Jyotismita Chaki, a PeerJ Section Editor covering this Section #]
paper is revised as per comments
good
good
paper is revised as per comments
Clear and unambigous
Original and fits the scope.
Well stated conclusions.
Accepted without any additional comments.
Based on the reviewer's comments, I would like to request the authors to substantially revise the paper based on the reviewers' comments with any new set of additional experiments, if required, and submit the revised paper to PeerJ Computer Science.
[# PeerJ Staff Note: The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at copyediting@peerj.com for pricing (be sure to provide your manuscript number and title) #]
Paper required proof reading.
It is mentioned that high accuracy with lower parameters is acheived from the proposed model. How the system balance this tradeoff?
This paper proposed MobileNet-RepMLP model with computational efficiency. Also, RepMLP and ECA modules are also introduced,will they increase the computation? -Justify.
when taken from image dataset, size can be equal. But, real time images has varied size. How this model resize?
1. Fig 4 clarity should be improved.
2. Experimental parameters can be tabulated.
3. Abstract length can be reduced.
4. It is mentioned that high accuracy with lower parameters is acheived from the proposed model. How the system balance this tradeoff?
5. This paper proposed MobileNet-RepMLP model with computational efficiency. Also, RepMLP and ECA modules are also introduced,will they increase the computation? -Justify.
6. Paper required proof reading.
7. when taken from image dataset, size can be equal. But, real time images has varied size. How this model resize?
The study presents an interesting work on disease identification using an improved lightweight MobileNetV2 model. The authors have evaluated the model on publically available CIFAR-10 and PlantVillage crop disease datasets. However, there are few areas in the manuscript that require improvement. I suggest the following revisions before publication:
1. The paper is well written with professional English throughout
2. The manuscript is not formatted properly according to the journal format. The citation and reference style should be re-checked.
3. Figures and tables are well structured
4. Results and discussion requires improvement.
1. The paper fits into the scope of the journal as it proposes a new light weight model architecture.
2. Material and methods section is well written and research question is well defined.
1. Underlying data is clear and public benchmark dataset is utilized in the experiments.
2. Conclusion needs to be modified.
The study presents an interesting work on disease identification using an improved lightweight MobileNetV2 model. The authors have evaluated the model on publically available CIFAR-10 and PlantVillage crop disease datasets. However, there are few areas in the manuscript that require improvement. I suggest the following comments to be addressed before publication:
1. The manuscript is not well formatted according to the journal format. Authors may re-check the format in the manuscript template given in the journal website.
2. Why the did the authors choose MobileNetV2 model even though other MobileNet models like MobileNetV3 is available and proven to be more accurate?
3. The model was trained with SGD and learning rate 0.1. The authors have done the hyperparameter tuning? Authors may add a table on the hyperparameter tuning and corresponding performance improvement. Also, discuss it in the results section.
4. The authors should include the loss function graph with epoch during the training to check the convergence, indicating that the DL models converge without over fitting or under fitting.
5. Results and discussion should be modified by comparing the model performance with similar light weight models developed by other studies.
6. The references given in the conclusion section should be removed and that can be better discussed in the last section of the results and discussion.
All images need to be upscaled. Especially the images that contain small fonts. Use the proper color contrast selection, making it easier to understand and enjoyable to learn.
In Figure 9, the performance of each model cannot be distinguished properly because they overlap too much. It would be better for the reader if presented with another graphical model or limited to only the best three models displayed.
no comment
no comment
Overall, the article is appropriate and suitable for publication.
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