Cyber-Physical Systems for Smart Farming: A Systematic Review
Round 1
Reviewer 1 Report (Previous Reviewer 3)
Comments and Suggestions for AuthorsI believe that the article has been improved sufficiently. I would still like a bit more descriptions of the methods mentioned in Section 5, but generally the text is acceptable now in my opinion.
Author Response
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Author Response.pdf
Reviewer 2 Report (Previous Reviewer 2)
Comments and Suggestions for Authors- The article comprehensively analyses integrating cyber-physical systems (CPS) into smart agriculture.
- The methodology is clearly defined and follows established systematic review frameworks. However, additional details on inclusion/exclusion criteria (e.g., criteria for selecting high-impact articles) would increase transparency. Justification for choosing bibliometric databases (Scopus, Springer) should be included.
- Although the manuscript covers a broad theoretical framework, it lacks an in-depth discussion of practical examples or implementations.
- The paper lacks a critical discussion of the limitations of CPS implementation, such as costs, technical challenges, or the digital divide among farmers.
- Including a comparative table summarizing different CPS implementations and their effectiveness would have been useful.
Author Response
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Author Response File: Author Response.pdf
Reviewer 3 Report (New Reviewer)
Comments and Suggestions for AuthorsThe study conducts an in-depth literature review and bibliometric analysis of cyber-physical systems (CPS) in smart agriculture. It explores the integration of artificial intelligence (AI), machine learning (ML), and digital twins (DT) with CPS for precision agriculture. The study examines 108 bibliographic records from Scopus and Springer to identify key trends, research issues, and uses of CPS in agriculture. The findings highlight the potential of CPS to enhance efficiency, sustainability, and food security, while addressing key implementation issues.
The study effectively delineates CPS as a transformative technology in smart agriculture. It successfully recognizes CPS as a vital component in current agricultural operations, presents a comprehensive overview of its function in automation, using data-driven solutions, and sustainability and bibliometric approaches to map research patterns, suggesting a growing interest in the field.
However, there are several things that could be done better:
A systematic literature review (SLR) and bibliometric analysis are appropriate. The review is conducted from 2008 to 2024; however, models before 2018 appear to be less important. A more concentrated focus on recent works (2018-2024) could be more effective. The databases Scopus and Springer are reputable sources; however, omitting IEEE Xplore and Web of Science may have limited coverage.
The investigation lacks a precise hypothesis, which makes evaluation difficult. Although bibliometric trends provide important information, empirical validation of the effectiveness of CPS in agriculture is lacking.
The studies reviewed do not address potential biases, such as publication bias or regional focus. Grouping CPS applications into thematic groups is useful, but lacks a mechanism for quantitative validation.
The article fails to demonstrate the advantages of CPS-based agriculture over competing techniques or IoT-only techniques. A comparative analysis section comparing CPS implementations in different agricultural contexts (e.g., orange-visible vs. open-field) would have added depth.
The review is well-structured and takes a cursory look at CPS applications in deep farming. However, the consideration of CPS limitations (such as cybersecurity threats and infrastructure costs) could be expanded. The impact of policy and legislation on CPS adoption is unclear. The topic is highly relevant given global concerns about food security, climate change, and sustainable agriculture. The emphasis on AI and ML is in line with current developments in smart agriculture.
The review highlights the lack of uniformity in CPS architectures. While acknowledging the integration of AI, there is little discussion of the claims of AI and ethical issues in CPS-driven agriculture. Future studies should focus on CPS in small-scale agriculture or emerging regions.
References are current (within five years) and relevant. There is no excessive self-citation, which emphasizes credibility. Some important references to IoT-based smart agriculture (such as an IEEE publication) are missing.
Figures, tables, clarity: While the word clouds and thematic maps provide useful information, some are overcrowded. More detailed legends could improve the clarity of the figures explaining the CPS architecture.
Some sections of the document, especially in the discussion of CPS applications, tend to repeat information. For example, the benefits of CPS in resource management are mentioned repeatedly without adding new insights.
The figures illustrating the generated CPS architecture (Figure 1) and the basic CPS schematic (Figure 2) are somewhat redundant. Both figures convey similar information, and one could be removed or combined to avoid repetition.
The collaborative network (Figure 6) is visually cluttered and difficult to interpret. The labels are small and the connections between nodes are not clearly explained in the text. A simpler version or clearer explanation of key relationships would improve readability.
Some sections (e.g., methodology) are extremely extensive, while others (e.g., limitations) are brief. The conclusion is powerful; however, the important findings could be summarized more effectively.
Lines 395-398 could be expanded with a discussion of the challenges of implementing CPS in agriculture (e.g., technical complexities, financial barriers). This would provide a more balanced view of the potential and limitations of the technology.
The cybersecurity section (lines 785-786) could be expanded to include specific examples of vulnerabilities in agricultural CPS and potential mitigation strategies.
The description of the SLR methodology (lines 222-228) is somewhat repetitive and could be shortened. The key steps (e.g., search process, exclusion criteria) are important, but the detailed explanation of each step could be streamlined. The discussion (lines 748-753) on the thematic evolution (Figure 7) is too detailed and could be summarized more concisely. The key trends (e.g., the rise of CPS and AI) are important, but the extensive description of each period could be reduced.
Some sections (e.g., methodology) are extremely extensive, while others (e.g., limitations) are brief. The conclusion is powerful; however, the important findings could be summarized more effectively.
Lines 395-398 could be expanded with a discussion of the challenges of implementing CPS in agriculture (e.g., technical complexities, financial barriers). This would provide a more balanced view of the potential and limitations of the technology.
The section on cybersecurity (lines 785-786) could be expanded to include specific examples of vulnerabilities in agricultural CPS and potential mitigation strategies. The description of the SLR methodology (lines 222-228) is somewhat repetitive and could be shortened. The key steps (e.g., search process, exclusion criteria) are important, but the detailed explanation of each step could be streamlined.
The discussion (lines 748-753) on the thematic evolution (Figure 7) is too detailed and could be summarized more concisely. The key trends (e.g., the rise of CPS and AI) are important, but the extensive description of each period could be reduced.
Overall, the article provides a comprehensive and well-structured overview of CPS in smart agriculture. However, it could be improved by addressing the weaknesses identified above, especially in terms of critical analysis, cybersecurity, and policy implications. Figures and tables could also be refined to improve clarity and readability. With these improvements, the article would make a greater contribution to the field of smart agriculture.
The English could be improved to more clearly express the research.
Author Response
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Author Response File: Author Response.pdf
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis manuscript gives a comprehensive overview for agricultural cyber-physical systems. The authors focus on the development of technologies such as artificial intelligence, machine learning, and digital twins in smart agriculture. The manuscript uses a rigorous bibliometric approach for literature search. Overall, the manuscript is informative, complete, and can bring some contributions to the field of smart agriculture. Here are a few of my recommendations.
1. The authors selected two databases, Scopus and Springer. To my knowledge, they have some duplication of data. How did the manuscript address this issue?
2. The first section is a bit too much background on the developmental aspects of agriculture. Condensing them a bit would provide readers with quicker access to the main thrust of the manuscript.
3. The bibliometric methods and word cloud statistics used in the manuscript I think are excellent. Can the authors discuss further what problems were encountered in adopting these methods and how they were resolved?
4. The Fig. 6 contains much information, and I would suggest additional remarks. For example, the correlation between "agricultural robots" and "robots" and the relationship between "anomaly detection" and "cyber security", could be briefly stated.
5. The statement in section 6 is too concise and does not go far enough, even a bit like it was written with an AI tool. The authors should have given clear recommendations, such as where to do research that would help fast-track the field.
Author Response
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Author Response File: Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for Authors1. This paper represents the comprehensive systematic review of CPS for smart agriculture. While CPS has been an extensive topic of research studies in different contexts, targeted usage in agriculture, let alone CPS integrated with AI, ML, and DT, is much rarer.
2. However, the paper has not deeply discussed practical examples or field implementation of CPS in agriculture. The issues of economic viability and scalability are understudied.
3. Although submitted to a sustainability journal, the paper does very little to link CPS programs to broader sustainability goals, such as greenhouse gas reduction or water conservation. More discussion on this should be added.
4. Although the methodology represents a systematic review, justification for including exclusively peer-reviewed papers is not comprehensively established and might be susceptible to selection bias. Perhaps more justification for the choices should be provided.
5. The paper is generally precise but verbose in some sections. Simplifying the definitions and reducing redundancy can be done to improve readability.
Author Response
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Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsThe article is devoted to systemizing of cyber-physical systems applications for smart farming, which is a very relevant topic nowadays due to recent developments of technologies like IoT and its usage in agroculture. However, there are many issues which should be fixed:
1) The article is called Systematic Review and contains 18 pages, which seems very little to me. Moreover, the first 10 pages are useless and do not have any information about cyber-physical systems.
2) There are 123 references, but the last one discussed in the text is [47], the others are just listed in the tables without any mentions at all, which is unacceptable especially for review.
3) No methods from Table 3 are discussed. In Conclusion authors state that "This study presents a comprehensive systematic review and Bibliometric analysis of the literature, focusing on the integration of AI, ML, and DT technologies in the development of CPS for smart agriculture." There is nothing comprehensive about this text in my opinion.
I do not think that the article in its current form can be accepted for publication. At first it must be extensively expanded, all the methods considered must be discussed and explained as usually is done in reviews, and all the issues above must be answered.
Comments on the Quality of English LanguageEnglish is rather poor in grammatical sense and must be fixed throughout the whole text.
Author Response
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Author Response File: Author Response.pdf
