AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript use the YOLOV5m to detect the volunteer cotton plants of cornfields and showcase the application of a customized UAS for spot-spray applications through simulation based on the developed CV algorithm.The content is very informative, but there are still more questions that need to be addressed.

Overall, much of this manuscript I think is redundant:

(1) Introduction is written in a disorganized and illogical manner with no transitions between the content of the paragraphs. The manuscript does not provide enough background information, such as the importance of the problem of detecting volunteer cotton and the limitations of the current solutions.Also, you just introduced the techniques and algorithms used, but did not make it clear how they were applied to the problem in this paper, just made a brief introduction.

(2) Some of the manuscript illustrations are not clear, e.g., Fig. 1 and Fig. 14.

(3) The section of 2.2 image data acquisition, especially UAV image acquisition, is too much of an introduction, and I personally feel that 2.3 Manufacturer Recommended Corrections is a bit redundant; it only needs to be an introduction to the process of image acquisition or preprocessing.

(4) The complete flowchart in Figure 7 needs a bit of typographical adjustment; the order of the serpentine rows tends to be confusing and unintelligible. And there is no need to put operations like splitting data on the flowchart, just add what is important.

Comments on the Quality of English Language

The clarity and flow of language in the manuscript is good, with clear and concise sentence structure and few spelling errors.However, there are still some areas that need attention to detail, and consideration should be given to the use of transitional vocabulary to improve the flow between sentences.

Author Response

Thank you for taking your useful time in reviewing my manuscript. We are thankful for your time and useful suggestions to enhance the quality of the manuscript. Please find the attached document with our response to all of your comments.

Thank you very much!

Best,

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Authors, check the grammar errors throughout the manuscript, also suggest modified changes as per the technical comments given below to enhance the clarity coherence, and overall quality of the manuscript.

·        Title is quite lengthy, author suggested to simplify and concise as "AI-driven computer vision detection of cotton in corn fields using UAS remote sensing data and spot-Spray application".

·        Line 27; why the detection speed on the NVIDIA Jetson TX2 GPU is relevant and how it impacts the feasibility of real-time applications.

·        Line 31; what is meant by "near real-time" in terms of seconds or frames per second to provide clearer insight into the system's responsiveness?

·        Line 39-41; provide recent data to support the effectiveness of the Texas Boll Weevil Eradication Foundation (TBWEF).

·        Line 42-44; why the Lower Rio Grande Valley (LRGV) is particularly vulnerable to re-infestation?

·        Line 49-52; author suggested shortening the details of crop rotation with corn and sorghum, focusing more on the relevance of volunteer cotton.

·        Line 69; author must describe how UAS technology for precise detection and spot-spraying surpasses traditional methods.

·        Line 76-77; should add specific advantages of YOLOv5, such as detection accuracy or inference speed, to justify its selection over other algorithms.

·        Line 144; in Figure 1 should be mentioned the WGS coordinates in the frame and the boundary line for the field area must be shown with a bold line also mentioning the legend.

·        Line 132-134; the specific cotton seed varieties must be mentioned and explain if they are representative of typical volunteer cotton plants.

·        Line 165; author should justify why the choice of an altitude of 4.6 meters for UAS flights and the impact of this altitude on image quality.

·        Line 179-180; details on any specific modifications made to the customized sprayer UAS for this study should be included.

·        Line 211-212; the process of normalizing images by gain and exposure settings must be explained for improve image quality.

·        Line 223-225; why choosing YOLOv5m over other variants and suitable for this study should be explained by author for clarity.

·        Line 256; on which criteria for choosing the dataset split ratios (80%, 15%, 5%) and must added the justification of these decisions in manuscript.

·        Line 278; accuracy and potential errors in converting pixel-wise bounding box coordinates into GPS coordinates must be discussed in detail.

·        Line 318; Figure 6 quality must be enhanced as per journal policy.

·        Line 343-345; implications of [email protected] and [email protected]:0.95 values for the detection performance of the model must be discussed in manuscript.

·        Line 349; confusion matrix shows an accuracy of 78%, author must explain what is the reasons for misclassification and how they could be mitigated.

·        Line 360-363; confidence values associated with bounding boxes must be deliberated in terms of their impact on detection reliability.

·        Line 366-367; author should compare the speed of 0.4 FPS on the NVIDIA Jetson TX2 GPU with real-time requirements to assess feasibility.

·        Line 373-375; must justify the selection of ten random locations for VC plants and added explain the criteria for choosing these locations and how representative they are of actual field conditions.

·        Line 404 and 417; the Figure 14 and Figure 15 should be replaced with higher DPI quality.

·        Line 420-421; statement about the potential application of multispectral remote sensing imagery should be supported by specific examples or references to existing studies to strengthen the argument.

·        Line 443-445; in discussion section YOLOv5’s performance could be enhanced by comparing it with other state-of-the-art object detection algorithms, providing a clearer picture of its relative advantages.

·        Line 465-467; the tolerance to false positives over false negatives is an important consideration and author should include a quantitative analysis of the trade-offs between precision and recall in the context of the study’s objectives in discussion section.

·        Line 473-476; author suggested to discuss any limitations observed during the training process and potential strategies for improving generalizability.

·        Line 515-516; the statement related to reducing management costs must be supported by an estimated cost analysis based on the study's findings, providing an economic benefit.

·        Line 554-675; author should ensure that all references, if any, are correctly formatted and consistent throughout the manuscript according to journal style or follow the instructions. E.g.

 

·        In Line 589-590; reference not correct ”P. K. Yadav et al., “Plastic Contaminant Detection in Aerial Imagery of Cotton Fields Using Deep Learning, Agriculture, vol. 13, no. 1365, pp. 1–22, 2023, doi: 10.3390.” 

Author Response

Thank you very much for your time in reviewing this manuscript! We really appreciate your time in going through the manuscript at a much detail and proving many useful suggestions in enhancing the quality of the manuscript. Please find the attached document with detailed response to your feedback and comments.

Thank you very much.

Best,

Author Response File: Author Response.pdf