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Dear authors, we are pleased to verify that you meet the reviewer's valuable feedback to improve your research.
Thank you for considering PeerJ Computer Science and submitting your work.
[# PeerJ Staff Note - this decision was reviewed and approved by Xiangjie Kong, a PeerJ Section Editor covering this Section #]
All comments were fixes
All comments were fixes
All comments were fixes
Dear authors,
You are advised to critically respond to all comments point by point when preparing a new version of the manuscript and while preparing for the rebuttal letter. Please address all the comments/suggestions provided by the reviewers.
Kind regards,
PCoelho
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
The literature review could be expanded.
No major concerns.
No major concerns.
The major challenge is the rather poor English language and the lack of academic writing skills. This will need to be addressed by a more experienced colleague.
The English use is not clear in some instances, which i have tried to suggest revision in the attached file.
The literature references is sufficient and covers the background of the research in context.
I suggest the authors adhere strictly with the journal article structure.
The predictive modelling result is relevant to the hypothesis
I suggest thorough formatting of the manuscript equations, figures and tables with adherence to the journal manuscript structure. The research is within the aims and scope of the journal.
The finds of the research is valid within the confines of where the data used for the modelling was collected. More validations will need to be done on the optimized LSTM model.
The accuracy of the predict model for irrigation volume prediction is good, but more validation of the model performance with data that were not used to train the model should be carried out.
I have highlighted some of the formatting suggestions, with others in the attached tracked manuscript:
17 scientifically-based
20 to 22 and short-term memory neural networks (BiLSTM), convolutional neural networks (CNN), and attention mechanisms.
45 define the abbreviation Artificial Intelligence (AI)
51 to 52 to manage irrigation more accurately
66 BiLSTM-CNN-Attention-based
447-451
To demonstrate the implementation of the BiLSTM-CNN- model for irrigation prediction, the predictive models were trained and tested on Windows 10, a 64-bit operating system, with NVIDIA GeForce GTX 1060 6G GPU, ATTENTION Ryzen 2700X processor, and 16G RAM installed with TensorFlow framework, Python 3.6. Other libraries such as numpy, pandas, and opencv were used for the programming
Upon thorough review of your manuscript, I would like to offer the following feedback for your consideration:
1. Clarity of Language: I have identified several instances where the clarity of English usage could be improved to enhance the overall readability and coherence of the text. Detailed suggestions for revision have been provided in the attached file. I strongly recommend incorporating these changes to ensure that the manuscript meets the linguistic standards of academic publishing.
2. Literature Review: The manuscript commendably covers the relevant literature, demonstrating a comprehensive understanding of the research context. The references are adequately comprehensive, providing a solid foundation for the study. This aspect of the manuscript is well-executed and contributes significantly to the academic rigor of your work.
3. Structural Compliance: It is imperative that the manuscript strictly adheres to the prescribed structure as outlined by the target journal. This will not only ensure compliance with editorial guidelines but also enhance the manuscript's organization and navigability for its intended readership. I urge you to review the journal's guidelines once more and adjust the structure of your manuscript accordingly.
4. Predictive Modelling Results: The results obtained from the predictive modeling are notably relevant and contribute meaningfully to addressing the research hypothesis. This section aligns well with the overall objectives of your study and adds substantial value to your research findings.
In summary, while the manuscript exhibits significant strengths, particularly in terms of literature review and the relevance of its predictive modeling results, attention to linguistic clarity and structural compliance is recommended for further improvement.
After a detailed review of your manuscript, I would like to offer some specific recommendations for enhancement:
1. Formatting of Equations, Figures, and Tables: There is a notable need for thorough formatting of the manuscript, especially concerning equations, figures, and tables. It is essential that these elements strictly adhere to the formatting guidelines outlined by the journal. Proper formatting will not only improve the manuscript’s overall presentation but also facilitate better reader comprehension and engagement.
2. Alignment with Journal's Scope: It is commendable that your research aligns well with the aims and scope of the journal. This congruence is crucial for the suitability of your manuscript for potential publication in this venue. Your adherence to the thematic and topical focus of the journal is appreciated and adds to the relevance of your work.
I encourage you to make the necessary formatting adjustments to align your manuscript with the journal's standards. Such refinements will significantly enhance the professional quality of your submission.
Upon reviewing the findings of your research, I would like to share some critical observations:
1. Contextual Validity of Findings: The research findings demonstrate validity within the specific context of the data utilized for modeling. This is a commendable achievement, as it provides a strong foundation within the defined scope. The robustness of the results within these parameters is a significant aspect of your study.
2. Need for Extended Validation of the LSTM Model: While the initial validations of the optimized LSTM model are noteworthy, there is a clear necessity for broader validation efforts. To enhance the credibility and generalizability of your model, it is imperative to conduct further validation studies. These should ideally extend beyond the initial data confines and explore diverse datasets or application contexts. Such an approach will not only reinforce the robustness of your model but also demonstrate its applicability in varying scenarios.
In light of these points, I encourage you to consider additional validation work on the LSTM model, which would undoubtedly strengthen the overall impact and reach of your research.
Your manuscript presents commendable work, particularly in the accuracy of the predictive model for irrigation volume prediction. However, I would like to suggest a few areas for improvement and clarification:
1. Model Validation: The accuracy of the predictive model is notable. However, it is essential to extend the validation of the model's performance using datasets that were not involved in the training process. This additional validation step will significantly enhance the robustness and generalizability of your model, thereby solidifying its applicability in real-world scenarios.
2. Formatting and Clarifications: The text emphasizes the importance of consistent and clear formatting for the term "scientifically-based" and the use of short-term memory neural networks (BiLSTM), convolutional neural networks (CNN), and attention mechanisms. It also defines the abbreviation for AI and suggests rephrasing the text to "to manage irrigation more accurately." It also recommends a clearer demonstration of the BiLSTM-CNN model's implementation for irrigation prediction, detailing the training and testing environment.
I believe these modifications will greatly enhance the clarity and impact of your manuscript. I look forward to your revised submission incorporating these recommendations.
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