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Algorithms
Volume 14
Issue 10
10.3390/a14100290
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Article
Peer-Review Record

Fine-Grained Pests Recognition Based on Truncated Probability Fusion Network via Internet of Things in Forestry and Agricultural Scenes

Algorithms 2021, 14(10), 290; https://doi.org/10.3390/a14100290
by Kai Ma 1, Ming-Jun Nie 2, Sen Lin 3,*, Jianlei Kong 4,*, Cheng-Cai Yang 4 and Jinhao Liu 1
Reviewer 1: Anonymous
Algorithms 2021, 14(10), 290; https://doi.org/10.3390/a14100290
Submission received: 4 September 2021 / Revised: 26 September 2021 / Accepted: 28 September 2021 / Published: 30 September 2021
(This article belongs to the Special Issue Algorithms for Machine Learning and Pattern Recognition Tasks)

Round 1

Reviewer 1 Report

Dear authors,

Thanks for your paper. Paper is clear from scientific point of view and methods of analysis and results  are clearly described. What is not clear for me is practical impacts of such technology. There are clearly described different repositories of pests. Different methods, how were this images collected were described. But what is missing, how trained algorithm can be applied in practical cases. Usually existing methods for collection of data are more suitable for monitoring of damages by pests, then identification of single pests Is intention, that there will be repository, where people will be able to send images of pests to identify this pests (I think, that usability is limited) or what is your idea. Please can you extend this in Discussion.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

The authors have done extensive work in trying to automatically identify pests with computer algorithms. While the modeling technique presented seems consistent, the paper falls short by greatly broadening the pest targets by not being able to give a detailed host description. Thus, we recommend an approach focusing on only one agricultural crop and its main pests for a modeling focused on results that can be useful for at least one agricultural crop. Another limitation was to use the discussion based only on the accuracy of the results, since we believe that the logic of the model used for classification is more important than the accuracy, considering that sources of error can occur from the phase of sample collection, image processing, among other sources. Thus, we encourage the authors to reformulate the paper with a focus on key pests from at least one host with more details about the crop evaluated.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Dear authors,

thanks, your paper looks OK for me

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