A Comprehensive Review of Digital Twins Technology in Agriculture
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors
Although there are some problems listed as follow, this manuscript can be published in this journal.
1 Line 59-66, the content should be deleted including ‘The structure of this paper is as follows.
- Section 1: Overview of this article.
- Section 2: Introduces the core concepts and architecture of DT.
- Section 3: Discusses the current research and key technologies in agricultural DT.
- Section 4: Lists the technical challenges faced in the field.
- Section 5: Provides insights into the future direction of DT in agriculture.
- Section 6: Summarizes the paper’.
2 Line 67, Figure 1 and the content ‘The outline of this review is shown in Figure 1’ should be deleted.
3 Figure 5 should be deleted.
4 Line 355, Figure 6 and the content ‘The outline of this section is shown in Figure 6’ should be deleted.
5 Line 359, ‘seen’ should be revised ‘been’.
6 Line 446- 447, the citation of references [119] and [103] is not understood. Zhang et al. [119] introduced the concept of DT and proposed a Digital Twin-Driven Time-Varying Proportional-Integral Adaptive control (DT-TVPIA) method [103].
7 Line 526, Figure 7 and the content ‘The outline of this section is shown in Figure 7’ should be deleted.
Comments on the Quality of English Language
The writting need to be refined for some figures replicated with the contents.
Author Response
Comments 1: Line 59-66, the content should be deleted including ‘The structure of this paper is as follows.
Section 1: Overview of this article.
Section 2: Introduces the core concepts and architecture of DT.
Section 3: Discusses the current research and key technologies in agricultural DT.
Section 4: Lists the technical challenges faced in the field.
Section 5: Provides insights into the future direction of DT in agriculture.
Section 6: Summarizes the paper’.
Response 1: Thank you for this suggestion. As recommended, we have deleted the paper structure outline originally presented in Lines 59-66. The revised manuscript now directly proceeds to the core content without redundancy.
Comments 2: Line 67, Figure 1 and the content ‘The outline of this review is shown in Figure 1’ should be deleted.
Response 2: As recommended, we have deleted the Figure 1 in Lines 67.
Comments 3: Figure 5 should be deleted.
Response 3: As recommended, we have deleted the Figure 5.
Comments 4: Line 355, Figure 6 and the content ‘The outline of this section is shown in Figure 6’ should be deleted.
Response 4: As recommended, we have deleted the Figure 6 and the content ‘The outline of this section is shown in Figure 6’ in Lines 355.
Comments 5: Line 359, ‘seen’ should be revised ‘been’.
Response 5: As pointed out, the word "seen" in Line 359 has been revised to "been" in the updated manuscript. The corrected sentence now reads:
"CPM has been significant advancements with the integration of DT technology in recent years."
We appreciate this correction, which improves the grammatical accuracy of our manuscript.
Comments 6: Line 446- 447, the citation of references [119] and [103] is not understood. Zhang et al. [119] introduced the concept of DT and proposed a Digital Twin-Driven Time-Varying Proportional-Integral Adaptive control (DT-TVPIA) method [103].
Response 6: As pointed out, we have revised the citation in Lines 446-447 to clarify the references. The updated sentence now correctly attributes both the concept of DT and the proposed DT-TVPIA method to Zhang et al. [124], ensuring proper citation consistency. The revised text reads:
"Zhang et al. [124] introduced the concept of DT and proposed a Digital Twin-Driven Time-Varying Proportional-Integral Adaptive control (DT-TVPIA) method."
Comments 7: Line 526, Figure 7 and the content ‘The outline of this section is shown in Figure 7’ should be deleted.
Response 7: As recommended, we have deleted the Figure 7 and the content ‘The outline of this section is shown in Figure 7’ in Lines 526.
Comments 8: Comments on the Quality of English Language：The writting need to be refined for some figures replicated with the contents.
Response 8: We have carefully corrected these errors and conducted a thorough proofreading of the manuscript.
Reviewer 2 Report
Comments and Suggestions for Authors
The paper, although extensive, is very summative described. This gives the impression of a paper without much argumentation, discussing in a very general way the vastness of DT technology. I suggest modifying the concept and design of the paper and limiting it to the subject of agriculture as stated in the title. It is also imperative to come up with concrete examples of research in which a more systematic analysis of the advantages and disadvantages of each component of DT is made. I suggest avoiding the use of AI in the design of the paper and studying the cited paperss in more depth.
Author Response
Comments 1: I suggest modifying the concept and design of the paper and limiting it to the subject of agriculture as stated in the title. It is also imperative to come up with concrete examples of research in which a more systematic analysis of the advantages and disadvantages of each component of DT is made. I suggest avoiding the use of AI in the design of the paper and studying the cited paperss in more depth.
Response 1:
We sincerely appreciate the reviewer's constructive suggestions for improving our manuscript. We have implemented revisions to address these concerns:
As suggested, we have substantially reduced generic DT discussions and refocused the paper exclusively on agricultural applications, aligning with the title's scope.
In Subsection 3.3, we have added a new comparative table (Table 1) analyzing agricultural DT case studies across multiple dimensions: Application Scenario, Core Technologies, Data Sources, Key Functionality, Validated Outcomes, and Limitations.
The existing DT systems are also listed in subsections 3.3.1-3.3.6 to further summarize the current application of digital twin technology in agriculture. In the literature search and literature collation, AI helps us to better review. And AI is used to polish the writing, but there are still many writing problems, which have been corrected in the revised version.
These modifications have strengthened the paper's academic rigor while maintaining its accessibility. We are grateful for the reviewer's insightful comments, which have significantly enhanced the quality of our review.
Reviewer 3 Report
Comments and Suggestions for Authors
The manuscript presents an overview of digital twins in agriculture applications. The manuscript is very interesting and it is well conceived. Some points will be highlighted aiming to contribute with the improvement of the paper.
1. The conception and organziation of the manuscript is appropriate. I just believe that the introduction is very short, however it does not compromise the manuscript as the other sections are well detailed. In the end of the introduction section, in general the description of the next sections is described with more details.
2. Figures 1, 5, 6 and 7 are very useful as they help to better understand what will be described in the sequence of the chapters.
3. I missed some experimental evaluation that could illustrate the adoption of DT in the agriculture. I also missed some real data that could emphasize the relevance of the research. Anyway, as the manuscript is devoted to a state of the art review, this is not a big concern.
4. The manuscript should be revised to correct several minor flaws and mistakes on the English language.
I believe that the conclusions section is very naive compared to the entire manuscript, so I think it can be improved to better reflect all what was presented in the manuscript.
Comments on the Quality of English Language
The manuscript should be revised to correct several minor flaws and mistakes on the English language.
Author Response
Comments 1: I just believe that the introduction is very short, however it does not compromise the manuscript as the other sections are well detailed. In the end of the introduction section, in general the description of the next sections is described with more details.
Response 1: We sincerely thank the reviewer for their valuable suggestion regarding the introduction section. In the revised manuscript, we have expanded the introduction to provide a more thorough background on digital twins. To enhance the paper’s clarity and impact, we have also added a dedicated paragraph at the end of the introduction explicitly outlining this review’s unique contributions to the literature. We greatly appreciate the reviewer’s insightful feedback, which has helped improve the overall quality of our work.
Comments 2: I believe that the conclusions section is very naive compared to the entire manuscript, so I think it can be improved to better reflect all what was presented in the manuscript.
Response 2: In response to this valuable suggestion, we have thoroughly revised the conclusions to provide a more comprehensive and insightful summary that better reflects the depth and breadth of the manuscript.
We have revised the Conclusions with:
The integration of DT technology into agriculture represents a pattern shift toward data-driven, precise, and intelligent farming. This review has systematically examined the development, key technologies, applications, and challenges of agricultural DTs, providing a comprehensive overview of its potential to enhance agricultural productivity and sustainability.
One of the most significant contributions of DTs in agriculture lies in their ability to enable real-time monitoring, predictive analytics, and decision support. By leveraging multi-source data ranging from IoT sensors and remote sensing imagery to climatic and soil condition databases, DTs enable farmers to implement precision agriculture strategies effectively. Applications in crop production management, livestock monitoring, disaster prediction, and agricultural machinery optimization demonstrate multiple usages of DTs in modern agriculture. Furthermore, the development of advanced 3D modeling and simulation techniques has enhanced the accuracy of virtual agricultural environments, allowing for more precise forecasting and scenario analysis.
Despite these advances, several technical and operational challenges still exist. Data acquisition remains a fundamental obstacle, while issues related to data heterogeneity, sensor calibration, and collection inefficiencies limit the reliability of DT systems. In addition, agricultural data integration poses significant difficulties due to fragmented data sources, inconsistencies in data formats, and interoperability constraints between different platforms. The standardization and accuracy of 3D crop model construction require further refinement, as discrepancies in the data representation can lead to suboptimal simulation results. Moreover, the integration of physical and virtual models in agriculture remains an intricate challenge, demanding seamless synchronization between real-world agricultural processes and their digital counterparts. Finally, the implementation of a full life cycle DT for crops and related services are necessary robust frameworks for long-term data storage, processing, and analysis, ensuring that DT systems can support continuous decision-making and adaptive learning.
To address these challenges, future research should focus on improving data standardization, advancing AI-driven data fusion techniques, and enhancing the scalability of DT applications in agriculture. The integration of DTs with FMs may further revolutionize the sector, enabling more autonomous, transparent, and resilient agricultural ecosystems.
Comments 3: Comments on the Quality of English Language：The manuscript should be revised to correct several minor flaws and mistakes on the English language.
Response 3: We have carefully corrected these flaws and mistakes and conducted a thorough proofreading of the manuscript.
Reviewer 4 Report
Comments and Suggestions for Authors
The paper provides a structured literature review of the digital twin (DT) concept in different aspect of agriculture and related technology. It focuses on the essence of DT, technologies that enable development of DT models and systems and current challenges in further application of DT in agriculture. In general, the paper is well written and gives a solid overview of various aspects of interest. However, there are certain aspects that should be improved. In particular, the text is very generic and the overall impressions is that there is a lack of added value or novelty in the presented study. Therefore, I would suggest the authors to further improve the manuscript content by incorporating some of the following suggestions:
1. please try to improve the introductory part or part 2 by providing a kind of critical discussion and argument supported distinction between the concepts of digital twins and digital shadows. Then highlight what approach regarding this distinction will be adopted in the paper and later on in the text try to emphasize this aspect when possible (when describing different examples in the subsequent sections)
2. please put more details into limitations of digital twins, as tools for various aspects of agricultural operations, as well as limitations in their development (this later case has already been covered to some extent by the existing sections named “challenges”). Also discuss the costs of introducing digital twins and try to make or reference some analyses that would show when digital twins are reasonable to consider (in the sense of the tradeoff between price and benefits, or complexity and development or deployment time).
3. In order for the paper to be recommended for possible publication by the journal it has to have clear contribution to the current state of knowledge, and in that sense the review should be to some extent unique and interesting for potential readers or practitioners. Thus, please take into consideration these requirements and try to improve the current version of the text.
Author Response
Comments 1: Please try to improve the introductory part or part 2 by providing a kind of critical discussion and argument supported distinction between the concepts of digital twins and digital shadows. Then highlight what approach regarding this distinction will be adopted in the paper and later on in the text try to emphasize this aspect when possible (when describing different examples in the subsequent sections).
Response 1: Thank you for this suggestion. We expanded the Introduction with:
According to the prevailing taxonomy in digital twin research [12,13], integration levels have advanced sequentially from digital models (static) through digital shadows (dynamic) to full digital twins (interactive), with each stage defined by data coupling intensity and control capabilities. As shown in Figure 1. A Digital Model (DM) refers to a static, descriptive representation of a physical system, often used for visualization or structural understanding without automatic data updates from the real world. A Digital Shadow (DS) introduces a unidirectional data flow from the physical asset to the digital replica, enabling real-time monitoring and partial synchronization, but lacking full interactivity or bi-directional feedback. In contrast, a DT entails a dynamic, bidirectional communication loop where real-time data continuously updates the virtual model, and insights or simulations from the model can influence the physical system, enabling advanced prediction, control, and optimization processes. As emphasized by Kritzinger et al. [12], a digital shadow can be viewed as a necessary intermediate step toward a true digital twin, particularly in domains like agriculture where infrastructure constraints often limit full bi-directional integration. In this review, we adopt a pragmatic stance by referring to all stages—whether digital shadow or full digital twin—under the term "DT".
12. Kritzinger, W.; Karner, M.; Traar, G.; Henjes, J.; Sihn, W. Digital Twin in manufacturing: A categorical literature review and classification Ifac-PapersOnline 2018, 51, 1016–1022.
13. Sepasgozar, S.M. Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment. Buildings 2021,11,151.
Comments 2: Please put more details into limitations of digital twins, as tools for various aspects of agricultural operations, as well as limitations in their development (this later case has already been covered to some extent by the existing sections named “challenges”). Also discuss the costs of introducing digital twins and try to make or reference some analyses that would show when digital twins are reasonable to consider (in the sense of the tradeoff between price and benefits, or complexity and development or deployment time).
Response 2: We sincerely appreciate the reviewer’s insightful suggestion regarding the limitations and cost-benefit considerations of digital twins in agriculture. As recommended, we have added a dedicated subsection (Section 4.6) titled "Limitations" to comprehensively address these aspects. We expanded the section with:
There are still some limitations to the full deployment of DTs in agriculture. One of the primary challenges lies in the high costs associated with the development, implementation, and maintenance of agricultural DT systems. Establishing a fully functional DT requires significant investment in hardware (e.g., IoT sensors, remote sensing devices, and high-performance computing infrastructure), software (e.g., AI algorithms, predictive modeling, and cloud computing services), and expertise (e.g., data scientists, agronomists, and system engineers) [144]. These costs can be prohibitive, especially for small-scale farmers and agricultural enterprises with limited financial resources [60].
In addition to financial constraints, the trade-off between the complexity of DT implementation and its potential benefits must be carefully evaluated. A crucial consideration is determining when it is economically and practically viable to adopt DT technology in agriculture. While the deployment of sensor networks, IoT devices, and data integration platforms can range from \$400 to \$600 per hectare, the potential gains—such as a 15–30\% reduction in resource waste or a 10\% increase in crop yield—may take several growing seasons to materialize [145,146]. For instance, studies have shown that integrating DT technologies in precision farming reduces input costs and improves productivity, with a Return On Investment (ROI) typically realized within 3 to 5 years [34]. However, such economic models often assume stable market prices and uninterrupted technology performance—factors that may not hold in developing regions [147,148]. Cost-benefit analyses suggest that DT adoption is most justified in high-value agricultural production systems where the potential gains from yield optimization, resource efficiency, and risk mitigation outweigh the costs [149,150]. Conversely, in low-margin, traditional farming operations, where profit margins are narrow, the ROI for DT implementation may be insufficient to justify the initial and ongoing expenditures [148]. In regions with limited digital infrastructure and weak data governance frameworks, the cost of deploying DTs often exceeds their perceived benefits, making alternative smart farming solutions, such as simpler IoT-based monitoring systems, more practical [119].
Given these constraints, future research and industry efforts should focus on developing cost-effective, modular DT solutions that can be scaled according to the financial and technological capacities of different agricultural stakeholders [151,152].
34. Purcell, W.; Neubauer, T.; Mallinger, K. Digital Twins in agriculture: Challenges and opportunities for environmental sustainability.
60. Verdouw, C.; Tekinerdogan, B.; Beulens, A.; Wolfert, S. Digital twins in smart farming. Agricultural Systems 2021, 189, 103046.
119. Rajeswari, D.; Parthiban, A.V.; Ponnusamy, S. Digital Twin-Based Crop Yield Prediction in Agriculture. In Harnessing AI and Digital Twin Technologies in Businesses; IGI Global, 2024; pp. 99–110.
144. Tao, F.; Qi, Q. Make more digital twins. Nature 2019, 573, 490–491.
145. Bali, M.K.; Singh, M. Farming in the Digital Age: AI-Infused Digital Twins for Agriculture. In Proceedings of the 2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL), 2024, pp. 14–21. https://doi.org/10.1109/ICSADL61749.2024.00009. 1101
146. Klerkx, L.; Jakku, E.; Labarthe, P. A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS-Wageningen journal of life sciences 2019, 90, 100315.
147. Wolf, S.A.; Buttel, F.H. The political economy of precision farming. American journal of agricultural economics 1996, 78, 1269–1274.
148. McClenaghan, A.; Gopsill, J.; Ballantyne, R.; Hicks, B. Cost Benefit Analysis for Digital Twin Model Selection at the Time of Investment. Procedia CIRP 2023, 120, 1197–1202.
149. Kshetri, N. The Economics of Digital Twins. Computer 2021, 54, 86–90.
150. Cesco, S.; Sambo, P.; Borin, M.; Basso, B.; Orzes, G.; Mazzetto, F. Smart agriculture and digital twins: Applications and challenges in a vision of sustainability. European Journal of Agronomy 2023, 146, 126809.
151. Rolandi, S.; Brunori, G.; Bacco, M.; Scotti, I. The digitalization of agriculture and rural areas: Towards a taxonomy of the impacts. Sustainability 2021, 13, 5172.
152. Abbasi, R.; Martinez, P.; Ahmad, R. The digitization of agricultural industry–a systematic literature review on agriculture 4.0. Smart Agricultural Technology 2022, 2, 100042.
We are grateful for this suggestion which has enhanced the manuscript’s balance between technical depth and real-world applicability.
Comments 3: In order for the paper to be recommended for possible publication by the journal it has to have clear contribution to the current state of knowledge.
Response 3: In response to this important suggestion, we have strengthened the contribution statement in our introduction section (Section 1). We expanded the section with:
This review makes the following key contributions to the field of DT in agriculture:
- We systematically examine the current state of digital twin technologies in agriculture, highlighting both the technologies—such as IoT, cloud computing, and edge computing—and their concrete applications of different agricultural issues, including crop management, disaster warning, and resource optimization.
- We identify and categorize the primary technical and operational challenges impeding widespread DT adoption in agriculture. These include difficulties in data collection from heterogeneous sources, lack of standardization, interoperability issues, and high implementation costs, especially in low-resource settings.
- We propose future research directions with a specific emphasis on the integration of DTs and Foundation Models (FMs). Such convergence is anticipated to enhance the intelligence, autonomy, and scalability of agricultural DT systems.
Round 2
Reviewer 4 Report
Comments and Suggestions for Authors
The authors have made a detailed extension of manuscript parts that address the raised questions in the previous round of review. In that sense the paper is more complete and suitable for possible publication. Since the topic is also of interest to wider community, this review paper could be recommended for possible publication.
