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Thanks to the authors for their efforts to improve the work. This version successfully satisfied the reviewers. It can be accepted currently. Congrats!
[# PeerJ Staff Note - this decision was reviewed and approved by Jyotismita Chaki, a 'PeerJ Computer Science' Section Editor covering this Section #]
My previous feedback has been addressed, and everything looks good now.
My previous feedback has been addressed, and everything looks good now.
My previous feedback has been addressed, and everything looks good now.
N/A
My comments have been addressed. It is acceptable in the present form.
My comments have been addressed. It is acceptable in the present form.
My comments have been addressed. It is acceptable in the present form.
Thanks to the authors for their efforts to improve the work. However, the issues are only partially addressed. Please revise the article continually according to the comments.
The authors should thoroughly address the comments and make necessary improvements.
The authors should thoroughly address the comments and make necessary improvements.
The authors should thoroughly address the comments and make necessary improvements.
My prior comments that the authors still need to work further upon are listed below. I think the authors should also make some improvements in their updates.
1) The paper should place the proposed method in the context of state-of-the-art techniques, clearly delineating its advantages and limitations.
2) The methodology section lacks sufficient detail about the key steps involved in the proposed approach. Each step should be described in detail, with clear explanations and justifications.
3) The recursive sub-tensor compressive sensing algorithm is not explained clearly. The paper should provide a step-by-step description of the algorithm, including the mathematical formulation and theoretical underpinnings.
4) The criterion for determining the optimal band using spatial spectral decorrelation needs further clarification. The paper should explain how this criterion is computed and why it is effective.
5) The robustness of the proposed method should be tested under different conditions, such as varying sampling rates and different types of HSIs.
Please revise the article according to the comments of the reviewers. Then it will be evaluated again.
1. The paper is well structured and has a clear introduction, methods, results, and discussion sections. However, one thing is missing - the paper should discuss the potential limitations of the proposed method and suggest areas for future research.
2. It is better to have more recent references to highlight the current state of research in this field. Most of the current references are before 2020.
1. While the experimental procedure is clear to me, I think there could be more description of the data source. How are those leaves being picked? Any preprocessing step is being done?
2. The discussion about reproducibility seems missing.
3. The evaluation could be further proved by introducing additional performance metrics like structural similarity index etc.
1. The paper does not discuss the computational complexity and runtime of the RSTHCS method compared to other approaches. This can be further discussed.
2. The method is currently evaluated only on tea leaves and soybean leaves. We can expand to a wider range of plant species and leaf types to prove the evaluation in a larger scope.
N/A
The manuscript entitled “A Novel Recursive Sub-Tensor Hyperspectral Compressive Sensing of Plant Leaves Based on Multiple Arbitrary-Shape Regions of Interest” has been investigated in detail. The paper proposes a novel method for compressing hyperspectral images (HSIs) of plant leaves by focusing on the relevant leaf regions and discarding background information, which aims to improve storage efficiency and reconstruction quality. While the approach is interesting and has potential benefits, the paper suffers from several significant issues that need to be addressed for it to meet the standards of scientific rigor and clarity.
1) The introduction does not clearly define the problem of HSI compression and the specific challenges that the proposed method addresses. A detailed problem statement is necessary to contextualize the research.
2) The motivation for choosing recursive sub-tensor compressive sensing over existing methods is not well articulated. The paper should provide a stronger rationale for the proposed approach.
3) The literature review is inadequate, failing to cover a comprehensive range of existing methods in hyperspectral image compression. A thorough review of related works is needed to highlight the novelty and significance of the proposed method.
4) The paper should place the proposed method in the context of state-of-the-art techniques, clearly delineating its advantages and limitations.
5) The methodology section lacks sufficient detail about the key steps involved in the proposed approach. Each step should be described in detail, with clear explanations and justifications.
6) The recursive sub-tensor compressive sensing algorithm is not explained clearly. The paper should provide a step-by-step description of the algorithm, including the mathematical formulation and theoretical underpinnings.
7) The criterion for determining the optimal band using spatial spectral decorrelation needs further clarification. The paper should explain how this criterion is computed and why it is effective.
8) The choice of performance metrics (e.g., PSNR, SAM, spectral indices) should be justified. The paper should explain why these metrics are appropriate for evaluating the proposed method.
9) The robustness of the proposed method should be tested under different conditions, such as varying sampling rates and different types of HSIs.
10) “Discussion” section should be edited in a more highlighting, argumentative way. The author should analysis the reason why the tested results is achieved.
11) The authors should clearly emphasize the contribution of the study. Please note that the up-to-date of references will contribute to the up-to-date of your manuscript. The studies named- “Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments; Agricultural crop classification with R-CNN and machine learning methods” - can be used to explain the methodology in the study or to indicate the contribution in the “Introduction” section.
This study may be proposed for publication if it is addressed in the specified problems.
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