A dual-branch model combining convolution and vision transformer for crop disease classification

Qingduan Meng; Jiadong Guo; Hui Zhang; Yaoqi Zhou; Xiaoling Zhang

doi:10.1371/journal.pone.0321753

Peer Review History

Original SubmissionOctober 15, 2024
15 Oct 2024 Author Response https://doi.org/10.1371/journal.pone.0321753.r001
8 Dec 2024 Decision Letter - Venkata Krishna Parimala, Editor PONE-D-24-46420A dual-branch model combining convolution and Vision Transformer for crop disease classificationPLOS ONE Dear Dr. Zhang, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Also address the following comments while revising your paper: Briefly mention how the model outperforms state-of-the-art approaches, emphasizing its advantages in accuracy and computational efficiency. Clarify the technical importance of integrating the ALP-FFN module and self-attention mechanism for improved performance. Provide context for the reported accuracy and efficiency, explaining how these advancements address specific challenges in crop disease identification or precision agriculture. Please submit your revised manuscript by Jan 22 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. 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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: 1- The most important pre-processing techniques for agricultural images are not shown. 2- Add additional research to review the previous research review for the research field 3- Please explain figure 7 better. 4- Discuss the results listed in the tables and the criteria that were adopted to establish a clear vision. 5- Clarification of proposed future work from this manuscript Reviewer #2: 1. Your figures appears as an appendix, fix them within the papers even if it takes a whole page. 2. Your tables and format is not well arranged, make your tables in a page, not in separate pages. 3. Follow the actual format. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy . Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0321753.r002
Revision 1
26 Jan 2025 Author Response Dear editor and reviewers: Thank you for offering us an opportunity to improve the quality of our submitted manuscript “A dual-branch model combining convolution and Vision Transformer for crop disease classification”. We appreciate very much the editor's and reviewers’ constructive and insightful comments. In this revision, we have addressed all of these comments. We hope the revised manuscript has now met the publication standard of your journal. Revised portion are marked in red in the paper. The main corrections in the paper and the responses to the editor's and reviewers’ comments are as following: Editor Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Also address the following comments while revising your paper: 1. Comment: Briefly mention how the model outperforms state-of-the-art approaches, emphasizing its advantages in accuracy and computational efficiency. Reply: Thank you for your advice. We have highlighted the advantages of model performance as well as computational efficiency in the abstract, results discussion section, and conclusion. 2. Comment: Clarify the technical importance of integrating the ALP-FFN module and self-attention mechanism for improved performance. Reply: We have supplemented the description of the advantages of linear self-attention and ALP‑FFN modules in section 2.3.2 and section 2.3.3, and conducted additional ablation experiments to verify the effectiveness of the two modules. As a result, the original section 3.3 is now divided into two parts: section 3.3.1 “effectiveness of the dual-branch architecture” and section 3.3.2 “effectiveness of the ALP-FFN module and the SSA module respectively”. Furthermore, we have removed the description of the ALP-FFN module’s parameter reduction from both the introduction and section 2.3.3, since newly added ablation experiments indicate that the deep convolution operation introduced by ALP-FFN slightly increases the number of parameters. We apologize for this oversight. 3. Comment: Provide context for the reported accuracy and efficiency, explaining how these advancements address specific challenges in crop disease identification or precision agriculture. Reply: Thank you for your suggestion. In the introduction, we have addressed the current challenges in disease classification, including the complexity of crop disease characteristics, the difficulty of integrating CNNs with ViTs, and the unsuitability of ViTs for resource-constrained devices. We also explained in the methods and results sections why our model can effectively tackle these challenges. Reviewer 1 1. Comment: The most important pre-processing techniques for agricultural images are not shown. Reply: We have changed the title of section 2.1 from “Datasets” to “Datasets and preprocessing”, and expanded this section to include explanations of image preprocessing techniques, encompassing dataset partitioning, image cropping, image normalization. In addition, we have described the data augmentation techniques used in the training process in section 3.1. 2. Comment: Add additional research to review the previous research review for the research field. Reply: We have reviewed applied research on combining CNNs with transformers for crop disease identification and reviewed their shortcomings. Also, we added references to the following literature: Reference 24: Xie W, Zhao M, Liu Y, Yang D, Huang K, Fan C, et al. Recent advances in Transformer technology for agriculture: A comprehensive survey. Eng Appl Artif Intell. 2024;138: 109412. doi:10.1016/j.engappai.2024.109412. Reference 26: Yu S, Xie L, Huang Q. Inception convolutional vision transformers for plant disease identi-fication. Internet Things. 2023;21: 100650. doi:10.1016/j.iot.2022.100650. Reference 27: Wang Y, Chen Y, Wang D. Convolution Network Enlightened Transformer for Regional Crop Disease Classification. Electronics. 2022;11: 3174. doi:10.3390/electronics11193174. 3. Comment: Please explain figure 7 better. Reply: We apologize for any confusion caused by Figure 7. We have explained the calculation process of the ALP-FFN module in more detail and added the calculation formulas. In addition, we noticed that figure 7 is poorly drawn, so we have redrawn figure 7 to make it more understandable. 4. Comment: Discuss the results listed in the tables and the criteria that were adopted to establish a clear vision. Reply: We have improved the discussion of the experimental results in sections 3.2.1 and 3.2.2, and added new discussion in comparison with previous work. In addition, the explanation of the performance metrics has been given in section 3.2. 5. Comment: Clarification of proposed future work from this manuscript. Reply: In the conclusion section, we have outlined our future work plan as follows: (1) Collecting datasets from real field environments. (2) Expanding the model to include more crop and disease types. Reviewer 2 1. Comment: Your figures appears as an appendix, fix them within the papers even if it takes a whole page. Reply: Thank you for your advice. However, the journal requires that figures cannot be included in the main manuscript file. Each figure must be prepared and submitted as a separate document. 2. Comment: Your tables and format is not well arranged, make your tables in a page, not in separate pages. Reply: We have redesigned and adjusted the format and layout of all the tables to ensure that each table is fully displayed on its own page. 3. Comment: Follow the actual format. Reply: We have carefully revised the manuscript format to meet the journal requirements. Thank you again for your positive and constructive comments and suggestions on our manuscript. We hope you will find our revised manuscript acceptable for publication. Attachments Attachment Submitted filename: Response to Reviewers .docx https://doi.org/10.1371/journal.pone.0321753.r003
11 Mar 2025 Decision Letter - Venkata Krishna Parimala, Editor A dual-branch model combining convolution and Vision Transformer for crop disease classification PONE-D-24-46420R1 Dear Dr. Zhang, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: (No Response) Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: (No Response) Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: (No Response) Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: (No Response) Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: (No Response) Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: (No Response) Reviewer #2: 1.Expand the Comparison: Include comparisons with more recent models, especially those that also combine CNNs and Transformers, to provide a more comprehensive evaluation of the proposed model's performance. 2. Computational Efficiency Analysis: Provide a detailed analysis of the model's computational efficiency, including inference time and energy consumption, to demonstrate its suitability for deployment on edge devices. 3. Ethical Considerations: Add a brief discussion on ethical considerations related to the use of AI in agriculture, including potential biases in the dataset and the impact of misclassification on farmers. 4. Hyperparameter Tuning: Include a discussion on hyperparameter sensitivity and tuning to provide insights into the robustness of the proposed model. 5. Broader Impact Discussion: Expand the discussion on the potential impact of the proposed model on precision agriculture, including how it could be integrated into existing agricultural practices and its potential to reduce crop losses due to diseases ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy . Reviewer #1: No Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0321753.r004
Formally Accepted
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