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AgriEngineering
Volume 3
Issue 3
10.3390/agriengineering3030044
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Article
Peer-Review Record

Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield

AgriEngineering 2021, 3(3), 681-702; https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3030044
by Jason Barnetson 1,2,*, Stuart Phinn 1 and Peter Scarth 1
Reviewer 1: Anonymous
Reviewer 2: Anonymous
AgriEngineering 2021, 3(3), 681-702; https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3030044
Submission received: 8 August 2021 / Revised: 3 September 2021 / Accepted: 6 September 2021 / Published: 10 September 2021
(This article belongs to the Special Issue Novel Approaches for Unmanned Aerial Vehicle)

Round 1

Reviewer 1 Report

The aim of this study was to develop, employ and test the accuracy of RPAS technology and to test the ability to utilize these pasture yield estimates at each growth stage, to both train and evaluate a satellite-derived deep-learning ANN model of pasture yield.
Overall, this is a clear, concise, and well-written manuscript.
I have just some comments: 
- Very long title 
- Table 1: add the geographic localization of the sites. 
- Explain your choice concerning the number and size of plots and quadrats used for this study.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

Determining antecedent pasture state for climate resilient grazing in the pastures of Queensland: An integrated remotely piloted aircraft system and satellite based deep-learning method for estimating time-integrated measures of pasture yield

This study presents an innovative methodology for the estimation of pasture yield. The authors used imagery collected from remote piloted aircraft systems and satellites and then applied deep-learning algorithms for predicting the total standing dry matter of pasture in grassland and woodland fields. The results indicated that the accuracy of the pasture yield estimates ranged from 0.8 to 1.8 t ha-1. Finally, the authors concluded that their study can help overcome limitations related with the use of optical based remote sensing systems on estimation of pasture yields.

Comments

Discussion and conclusion sections

The authors must support their statements by arguing based on results from relevant studies.

Overall Comment

I recommend this research article to be accepted after minor revision. The authors present an innovative methodology on the combined use of aerial and satellite data with deep learning algorithms for estimating pasture yield. Their work will help modern farming to cope with challenges related with sustainable production and minimize environmental consequences.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

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