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Additional Editor Comments:

The MS need to be revised in view of the comments given by both the reviewer as major revision .

The methods of AI ML learning needs explanation as per the efficiency for detection of waterbodies, other breeding sites , the distance of drone from the waterbodies how larval risk was calculated, how correctly the mapping was done, what larvicide and how it was put

Reviewers' comments:

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: No

Reviewer #2: N/A

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1:  Overall, this is an interesting premise. This manuscript presents field evaluation of a drone- and AI-assisted approach to larval source management (LSM) in Ghana. The study compares this tech-enhanced method to conventional manual LSM across eight sub-districts and assesses differences in operational efficiency, resource use, and malaria trends. However, I am thoroughly disappointed in the detail and framing of everything presented here. This paper still needs extensive improvements on transparency of methods and some improved data analysis. The authors sorely need to address their overreach of conclusions and do a better job of narratively incorporating existing literature on their topic in the discussion. Some clarifications are needed from the authors before acceptance of the manuscript:

1. As stated by authors, the detection of waterbodies was still conducted manually using drone images, with only minimal support from image recognition AI. Then I did not find any novelty in the manuscript. Until we are not detecting the breeding sites with automated AI enabled drone technology, it is not of much use. Further, drone images did not identify the sites positive for larvae. Even limited success in the same might have resulted in a lot of output.

2. The AI model section lacks methodological details, particularly regarding model validation and feature importance. How the CatBoost model was trained and validated (e.g., dataset split, hyperparameter tuning). Also provide limitations of the model’s precision and implications for potential overtreatment.

3. Use of 100 m height for drone application for time efficiency is not justified. As better resolution of images at 50 m height might have provided good results in larval site identification. Please clarify in detail.

4. There are no details of larvicide used in the manuscript. Kindly provide the same.

5. How the larval risk criteria were defined? Kindly provide.

6. Kindly provide details of how many habitats came under high risk larval sites. It will justify the claims of reduction in larvicide usage. Further, show the difference in area of breeding habitat sprayed both under conventional and targeted larviciding.

7. Please state why no ethical permission was obtained for drone based surveillance of breeding habitats. Whether the grid used in drone mappring (300x300m) area did not involve any human or cattle habitation. Further, state about ethical clearance for stakeholder and field worker Qualitative insights.

8. Why traps were placed at one site only in each conventional and interventional subdistricts. Traps placement in all the study location might provide better assessment of entomological outputs.

9. No statistical tests are presented to compare outcomes between groups, limiting confidence in the reported differences. The study would benefit from formal statistical testingto compare larvicide use, person-days, and mosquito abundance between groups. Present p-values or confidence intervals where appropriate to support claims of efficiency.

10. Line 459-484 seems not the part of results. It may be moved to discussion.

11. There is some inconsistency in results. Kindly present in clear and concise way. Specially line 533-594.

12. In discussion authors suggest mapping by drone and spraying manually. Both mapping and spraying by AI enabled drone technology could result in better output.

13. In methodology, there is no details of Feedback from field staff.

14. Kindly provide quantitative results for Qualitative insights from Local Stakeholders. How many field and stakeholder feedbacks were obtained. How many of those reported what.

15. In introduction, details of malaria cases of 2023 may be provided.

16. Legends for figure may be defined in better way.

Reviewer #2:  In recent years LSM has gained much attention since this method seems to be more promising. AI-based drone application is one such innovation in vector control. Some suggestions:

1. Here the larvicide was Vectolex WDG which is Bacillius spharicus 2362 (strain ABTS - 1743) was used. I do not fine its efficacy. Because its efficacy depends on exposure to sunlight, pH of water and in fragile ecosystem. Please give details.

2. Economic validation.

3. Community acceptance.

4. Impact on other insects.

5. Larval data missing.

7. Most mosquitoes were Culex. Impact of any diseases transmitted by Culex spp.

8. Limitations such as inability of breeding detection during dry season and also breeding habitats covered under canopy.

9. Whether other vector control agents such as larvivorous fish can be used?

**********

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Reviewer #1: No

Reviewer #2: Yes:  Prof Susanta Kumar Ghosh

**********

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Field evaluation of drone and AI assisted larval source management in Ghana

PLOS ONE

Dear Dr. Kim,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jan 05 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Johanna Pruller

Senior Editor

PLOS One 

on behalf of

Himmat Singh

Academic Editor

PLOS One

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Additional Editor Comments:

Although the authors have tried to answer most of the comments by reviewer 1, a few things regarding LSM should be further expanded upon, such as economic value and community acceptance as that adds dimensions to vector control.

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Field evaluation of drone and AI assisted larval source management in Ghana

PONE-D-25-27483R2

Dear Dr. Kim,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Himmat Singh

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments: