Smart Weather Data Management Based on Artificial Intelligence and Big Data Analytics for Precision Agriculture

Round 1

Reviewer 1 Report

 

This paper proposed “Smart weather data management based on artificial intelligence and big data analytics for precision agriculture”. The approach discussed in this manuscript is interesting. To enhance the quality of the research, I recommend following corrections.

1-     The introduction section needs more investigation of some recent and relevant work that has been done in the past. I suggest a few papers for your reference.  (Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving, ITITS-2022, “A Step toward Next-Generation Advancements in the Internet of Things Technologies”))

 

2-     What is the impact of covd19 on your experiment result? Evaluate before covid19 dataset, like 2014 to 2019 and 2019 to 2022 and add comparison results in an experiment section.

3-     Add more comparison with your proposed method.

4-     Table 6  “only ta” stand for ?

5-     Add more detail about the dataset and specific mention of data that you have been in your experiment. You should use your dataset and check your algorithm performance. Add a 5-year dataset from 2015 to 2020 and evaluate your algorithm performance.

6-     Explain your proposed and based algorithm separately with example that is easily understand for reader. Write a pseudocode of your proposed method and based method and clearly define your contribution, that will show your work credibility.

7-     In the manuscript, there are many grammatical errors and typos. Carefully revised all manuscripts and corrected them.

 

Author Response

Please see the attachment

 

Author Response File: Author Response.pdf

Reviewer 2 Report

In this article, authors present a smart weather data management system evaluated using data from a meteorological station installed in our study area covering the period from 2013 to 2020 at a half-hourly scale. The proposed system consists of state-of-the-art statistical methods, machine learning, and deep learning models to derive actionable insights from raw data. The authors presented a work with a clear methodology of system development and implementation. In general the work is well structured. However, I believe that the some important details in the study need to be given before the manuscript can be published. I have listed my comments and suggestions below.

 

Comments and Suggestions:

 

1.      We know that the ANN designers can choose an approach to normalize their data. But, there is no explanation of the normalization of the data used about ANN model used. I think it would be good if you write a small paragraph to the article about this subject.

 

2.      It is understood that different types of sensors were used in the study. I believe that technical data should be included in the article about these sensors. Because the reader must understand the whole system and have knowledge about the data traffic. If you write a paragraph about this topic, I believe that the quality of the article will increase even more.

 

3.      Precision farming, or smart farming, is the practice of using data to optimize agricultural production, despite variable circumstances. It is a way of farming where every specific crop gets the right treatment, at the right time and in the right place. As a result, this study will contribute to the right time expression in precision agriculture. I think the work is very important. Thank you for contributing to the scientific literature on the subject.

Author Response

Please see the attachment.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Carefully rechecked the manuscript and removed grammatical errors and typos.