Recognition of Abnormal Individuals Based on Lightweight Deep Learning Using Aerial Images in Complex Forest Landscapes: A Case Study of Pine Wood Nematode

Round 1

Reviewer 1 Report

1.The description of CBAM is not sufficient. It is recommended to display the structural diagram of CBAM as a whole instead of splitting it into channel attention and spatial attention.

2.The manuscript uses Pine Wood Nematode as an example to demonstrate the effectiveness of its method for identifying abnormal individuals in a complex forest background. What about other objects? It is recommended that the author add some experiments to verify his method for abnormal individuals in other forests such as Dead Tree Clusters.

3.The author claims that the proposed D-SCNet is a lightweight method, but there is no data presentation to prove this. It is recommended to supplement the parameters and Flops of D-SCNet and other models

4.The manuscript does not provide a comprehensive introduction to related work.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

This work proposed a lightweight deep learning model D-SCNet to detect abnormal individuals in complex forests, which has excellent performance both on accuracy and speed. This work plays a significant role in the timely detection of subtle pressure changes and early management to maintain the stability of forest ecology. The manuscript is well structured and in good writing. The methods, results, discussion, and conclusion ‎are acceptable. While there are some problems need to be addressed. The specific remarks are as follows:

 

1.        In the introduction, the importance of identifying abnormal individuals needs to be strengthened; for example, early identification can control the expansion of pests in time, and so on.

2.        There is no need to dwell on the characteristics of UAV imaging systems and sensors, which are irrelevant to the research of this paper.

3.        Would it be more appropriate to change the caption of Figure 2 to "Photographs of the process of pine trees infected with Pine Wood Nematode"? Cause your photos are not the pest.

4.        Line360-361. The results are only shown here, and the reliability verification of the results is not mentioned. For the time being, it is not appropriate to say that the proposed method has a good identification effect. I suggest deleting this sentence.

5.        What are the size and representativeness of the four selected test areas? Can it be marked in Figure 9 for the convenience of readers?

6.        Line 375-386 is the method for accuracy evaluation; it should be placed in the Method. And there is no need to explain all the equations in the text. According to equations 3 and 4, MA = 1-Recall, how to interpret this indicator. Why was the commonly used evaluation index F1-score not used?

7.        Images of ground truth should be added to Figure 11 for convenient comparison.

8.        In Table 6, the models are incorrect; they should be D-SCNet, No-c, No-s, and No-cbam.

9.        L458, 5. Discussion? The discussion should be a separate level 1 chapter.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

accept