Laser Weeding Technology in Cropping Systems: A Comprehensive Review

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This article reviews laser weeding technology as an alternative to traditional weed control methods. The potential environmental benefits of laser weeding are presented by comparing it with traditional methods. The review addresses standards and limitations in waiting time, automatic navigation, energy efficiency, affordability, and safety. The article suggests areas for improvement to increase the effectiveness, accessibility, and affordability of laser weeding.

 

1.      In Figure 1, a general crop field image is given as the input image. What kind of result is expected to be produced by deep learning or machine learning here? It would be more understandable if the cases where the input is a weed or a crop are presented separately.

2.      Accuracy and speed values ​​should be added to Table 1.

3.      A table should be created comparing all reviewed studies.

4.      In the Weed Detection section, the advantages and disadvantages of deep learning and traditional machine learning should be discussed.

5.      Performance metrics of studies that weed control with deep learning and traditional machine learning should be given in a table and discussed.

6.      The Deep Learning Methods section should be expanded. YOLO studies for weed detection conducted in recent years should be added.

7.      Information can be given about the cameras used for weed control and their technical specifications (frame count, resolution, etc.).

8.      The number of current references in this field in recent years should be increased.

 

9.      A few photos of platforms where laser weeding technology is used can be added to attract the attention of readers.

Comments on the Quality of English Language

This article reviews laser weeding technology as an alternative to traditional weed control methods. The potential environmental benefits of laser weeding are presented by comparing it with traditional methods. The review addresses standards and limitations in waiting time, automatic navigation, energy efficiency, affordability, and safety. The article suggests areas for improvement to increase the effectiveness, accessibility, and affordability of laser weeding.

 

1.      In Figure 1, a general crop field image is given as the input image. What kind of result is expected to be produced by deep learning or machine learning here? It would be more understandable if the cases where the input is a weed or a crop are presented separately.

2.      Accuracy and speed values ​​should be added to Table 1.

3.      A table should be created comparing all reviewed studies.

4.      In the Weed Detection section, the advantages and disadvantages of deep learning and traditional machine learning should be discussed.

5.      Performance metrics of studies that weed control with deep learning and traditional machine learning should be given in a table and discussed.

6.      The Deep Learning Methods section should be expanded. YOLO studies for weed detection conducted in recent years should be added.

7.      Information can be given about the cameras used for weed control and their technical specifications (frame count, resolution, etc.).

8.      The number of current references in this field in recent years should be increased.

 

9.      A few photos of platforms where laser weeding technology is used can be added to attract the attention of readers.

Author Response

Note: Correct line numbers are mentioned here in these paragraphs.

This article reviews laser weeding technology as an alternative to traditional weed control methods. The potential environmental benefits of laser weeding are presented by comparing it with traditional methods. The review addresses standards and limitations in waiting time, automatic navigation, energy efficiency, affordability, and safety. The article suggests areas for improvement to increase the effectiveness, accessibility, and affordability of laser weeding.

Author Response:  

Dear Reviewer,

Thank you for your valuable feedback on our manuscript. We have revised the article to better highlight the environmental benefits of laser weeding compared to traditional methods. Additionally, we have addressed the points regarding waiting time, automatic navigation, energy efficiency, affordability, and safety, providing more insights into their current limitations and potential improvements. We hope these changes meet your expectations

1. In Figure 1, a general crop field image is given as the input image. What kind of result is expected to be produced by deep learning or machine learning here? It would be more understandable if the cases where the input is a weed or a crop are presented separately.

Response: Thank you for bringing this to our attention. We have revised Figure 1 to include additional features that clearly demonstrate the output of the deep learning process.  Separate cases for weed and crop identification have been presented to enhance the clarity of how the model distinguishes between them. A short illustration is also added, lines #198-203.

2. Accuracy and speed values ​​should be added to Table 1.

Response: Thank you for your suggestion. We have updated Table 1 to include the accuracy and speed values, providing a more comprehensive overview of the results.

3. A table should be created comparing all reviewed studies.

Thank you for your valuable feedback. In response, a comprehensive table #2 comparing all the reviewed studies has been added to the paper. The table #2 includes key details such as study objectives, weed species, laser types, power settings, and main findings. This will provide a clear and organized comparison to enhance the understanding of the research landscape covered in the review.

4. In the Weed Detection section, the advantages and disadvantages of deep learning and traditional machine learning should be discussed.

Thank you for the insightful suggestion. The advantages and disadvantages of both deep learning and traditional machine learning methods in weed detection have been added to the Weed Detection section. This discussion highlights the accuracy and scalability of deep learning, while addressing the simplicity and lower computational requirements of traditional methods, providing a balanced view of both approaches in lines #204-226.

5. Performance metrics of studies that weed control with deep learning and traditional machine learning should be given in a table and discussed.

Thank you for the kind suggestion. A table #3 summarizing the performance metrics of studies that used both deep learning and traditional machine learning for weed control has been added to the paper. The table includes key metrics such as accuracy, precision, recall, and processing time, along with a discussion of the relative strengths and weaknesses of each approach. This comparison provides a clearer understanding of how these techniques perform in different contexts.

6. The Deep Learning Methods section should be expanded. YOLO studies for weed detection conducted in recent years should be added.

Thank you for bringing this to our attention. The deep Learning Methods section has been expanded, and recent studies utilizing YOLO for weed detection have been added. These include studies that highlight the advancements in real-time, accurate weed detection using YOLO in various field conditions, lines #331-342.

7. Information can be given about the cameras used for weed control and their technical specifications (frame count, resolution, etc.).

Thank you for your suggestion. Information about the cameras used for weed control, including their technical specifications such as frame rate, resolution, and other key parameters, has been added in various places and also in lines #346-359.

8. The number of current references in this field in recent years should be increased.

Thank you for the suggestion. We have increased the number of current references from recent years, incorporating more studies on deep learning, YOLO, and weed control technologies. These new references provide a broader and more up-to-date perspective on the advancements in this field, ensuring that the review reflects the latest research trends.

9. A few photos of platforms where laser weeding technology is used can be added to attract the attention of readers

Thank you for the suggestion. I have added photos of laser weeding platforms to the manuscript to enhance its visual appeal under Figure No. 2.

Once again, I appreciate your insightful feedback and the time you have taken to review my manuscript. I have carefully considered your suggestions and incorporated the necessary changes to improve the quality and clarity of the paper. I believe these revisions have strengthened the manuscript, and I look forward to your further thoughts.

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

You have presented an interesting article on the application of modern technologies in agriculture. 

The use of solutions based on laser weed control is very valuable from the point of view of protecting the natural environment, promoting green governance practices, etc. Please respond to the following comments.

1. do such modern solutions in weed control of fields have a chance to be subsidized by the state budget in your country? Are there any programs developed for farmers to support the use of modern technologies?

2. have you performed analyses on the change in the financial condition of farms after the purchase of such solutions in the fight against weeds?

3. How to increase the feasible working time of such laser robots in the field?

4. In what crops can the use of laser weed control be most problematic?

5. Please add additional 5 articles from the last 5 years on similar topics.

 

 

Author Response

Dear Authors,

You have presented an interesting article on the application of modern technologies in agriculture. 

The use of solutions based on laser weed control is very valuable from the point of view of protecting the natural environment, promoting green governance practices, etc. Please respond to the following comments.

Author Response:  

Dear Reviewer,

Thank you for your positive feedback on our article. We appreciate your recognition of the environmental value of laser weed control and its role in promoting green governance practices. We have carefully addressed your comments to further strengthen the manuscript.

1. do such modern solutions in weed control of fields have a chance to be subsidized by the state budget in your country? Are there any programs developed for farmers to support the use of modern technologies?

Thank you for your question. In my country, there is growing recognition of the importance of modern agricultural technologies, including precision farming tools like laser weeding systems. While direct subsidies for such technologies are not widely available yet, there are ongoing government initiatives aimed at promoting sustainable agriculture, which could potentially support the adoption of modern solutions. Additionally, various agricultural support programs and research grants are in development to help farmers invest in technologies that improve environmental sustainability and efficiency. With the increasing focus on reducing chemical herbicide use, I believe there is potential for these technologies to receive more government support in the near future.

2. have you performed analyses on the change in the financial condition of farms after the purchase of such solutions in the fight against weeds?

Thank you for your insightful question. While my current paper focuses on the technical aspects and potential environmental benefits of laser weeding technology, I have not performed a detailed financial analysis on the economic impact of such technologies on farms after adoption. However, studies in this area suggest that while the initial investment in laser weeding systems can be high, long-term savings from reduced herbicide use, labor costs, and increased crop yields may lead to a positive return on investment. Future research could focus on performing a comprehensive cost-benefit analysis to better understand the financial implications for farmers.

3. How to increase the feasible working time of such laser robots in the field?

Thank you for raising this important point. To increase the feasible working time of laser robots in the field, several strategies can be considered. Enhancing battery life or incorporating solar panels can extend operational hours, optimizing energy efficiency and using lightweight materials. Another potential solution is implementing autonomous recharging stations in the field. Advances in AI and machine learning could also help optimize route planning, reducing time and energy spent on non-essential tasks, lines #539-546.

4. In what crops can the use of laser weed control be most problematic?

Thank you for the question. The use of laser weed control can be most problematic in crops that have dense foliage or closely spaced plants, such as wheat, leafy greens or crops like lettuce and spinach. A comprehensive explanation is added in the manuscript, lines #571-577 .

5. Please add additional 5 articles from the last 5 years on similar topics.

Thank you for your suggestion. I have added five recent articles from the last five years that focus on similar topics to provide a more comprehensive and up-to-date perspective on the subject, ref # [35, 36, 38, 39, 82].

Once again, I appreciate your insightful feedback and the time you have taken to review my manuscript. I have carefully considered your suggestions and incorporated the necessary changes to improve the quality and clarity of the paper. I believe these revisions have strengthened the manuscript, and I look forward to your further thoughts.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have deep expertise in the entire area and disciplines referring to the approach of laser weeding.

 

The article gives a comprehensive and up-to-date overview on the approach of weeding by laser technologies. The authors report along the main components of weeding systems such as plant recognition by machine learning etc.

 

 

Introduction

This more a collection of disadvantages of applied methods (obviously to justify laser weeding) than an independent survey of possible treatments reflecting advantages and disadvantages.

 

Energy demand of laser weeding considering energy supply on mobile weeding platforms is discussed.

 

What is missing is a coordination of the weeding systems in the different cropping systems e.g. row crops, permanent crops, horticulture with special respect to weeding under control crop conditions e.g. glass houses.

 

Targeting of the laser beam is a crucial condition for the application and is described in the article under the topic target area. The remarks are necessary but not sufficient. The vision system is not only to detect plants and discriminate weeds but to identify the target area of the weed plant which need to be attacked (meristem). And the challenge is to develop a targeting system, which complies with precision, fast acting under field conditions, which is not addressed neither under key components nor under target area.

 

Summary and conclusion

Conclusion are drawn with smart reflections of the entire aspects but the summary is incomplete because the reader does not learn about which problems are solved or to which degree and what is the stage of the technology including applicability of the existing systems.

 

 

Author Response

The authors have deep expertise in the entire area and disciplines referring to the approach of laser weeding.

The article gives a comprehensive and up-to-date overview on the approach of weeding by laser technologies. The authors report along the main components of weeding systems such as plant recognition by machine learning etc.

Author Response:  

Dear Reviewer,

Thank you for your kind feedback and recognition of our expertise in the field of laser weeding. We are pleased that you found our review comprehensive and up-to-date, and we have ensured that the discussion on key components, such as plant recognition by machine learning, is thorough and accurate.

Introduction

This more a collection of disadvantages of applied methods (obviously to justify laser weeding) than an independent survey of possible treatments reflecting advantages and disadvantages.

Thank you for your observation. We understand your concern that the current discussion might seem more focused on highlighting the disadvantages of traditional methods to justify laser weeding. In the revised version, we ensured a more balanced comparison by including a comprehensive overview of both the advantages and disadvantages of various weed control methods, including mechanical, chemical, and biological treatments. This provides a clearer, more objective perspective, lines # 74-84.

Energy demand of laser weeding considering energy supply on mobile weeding platforms is discussed.

Thank you for your comment. We have addressed the energy demand of laser weeding on mobile platforms in the revised manuscript, discussing current energy supply options like batteries and solar panels, as well as potential ways to optimize energy use.

What is missing is a coordination of the weeding systems in the different cropping systems e.g. row crops, permanent crops, horticulture with special respect to weeding under control crop conditions e.g. glass houses.

We appreciate your valuable input. We acknowledge that a discussion on coordinating weeding systems across various cropping systems is important. We included a section that explored how laser weeding technology can be adapted for different cropping systems, such as row crops, permanent crops, and horticulture, with specific attention to controlled environments like greenhouses, lines # 393-399.

Targeting of the laser beam is a crucial condition for the application and is described in the article under the topic target area. The remarks are necessary but not sufficient. The vision system is not only to detect plants and discriminate weeds but to identify the target area of the weed plant which need to be attacked (meristem). And the challenge is to develop a targeting system, which complies with precision, fast acting under field conditions, which is not addressed neither under key components nor under target area.

Thank you for your feedback. I have addressed the need for a more precise and fast-acting targeting system, particularly with regard to identifying the weed’s meristem, in the revised manuscript, lines # 476-482.

Summary and conclusion

Conclusion are drawn with smart reflections of the entire aspects but the summary is incomplete because the reader does not learn about which problems are solved or to which degree and what is the stage of the technology including applicability of the existing systems.

Thank you for your valuable feedback. I understand the importance of providing a more complete summary that highlights the problems addressed, the extent to which they are solved, and the current stage of laser weeding technology. I have revised the summary and conclusion to ensure that it clearly reflects these aspects, including the applicability of existing systems and their limitations, lines # 640-678.

We believe these revisions have significantly improved the manuscript, addressing all the points raised. Thank you again for your insightful comments and suggestions.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have made all the necessary changes in the revised version of the manuscript.

Comments on the Quality of English Language

The authors have made all the necessary changes in the revised version of the manuscript.