Toward Automatic Monitoring for Anomaly Detection in Open-Pit Phosphate Mines Using Artificial Vision: A Case Study of the Screening Unit
Round 1
Reviewer 1 Report
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Comments for author File: 
Comments.pdf
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Author Response
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Author Response File: Author Response.pdf
Reviewer 2 Report
Line 262-263: It is a deep learning method rather than a deep method that is misrepresented.
Moderate editing of English language required.
Since there are many architectures for CNN models, in the article you should give the architecture of the CNN model for this paper.
The discussion of the current state of research could be a bit more insightful.
In summary, I was happy to review your manuscript: “Towards Automatic Monitoring for Anomaly Detection in pen-Pit Phosphate Mine Using Artificial Vision: A Case Study of the Screening Unit”. The overall structure of this article is clear, detailed and rich in charts and graphs, but there are still some subtle problems that I hope to improve. Overall Recommendation is accept after minor revision (corrections to minor methodological errors and text editing).
Moderate editing of English language required
Author Response
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Author Response File: Author Response.pdf
Reviewer 3 Report
Abstract is not informative enough about the work
The abstract should to be re-written including, what is investigated, what was done, main results of research and the main conclusion useful for interested readers.
Author Response
Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.
We appreciate your recommendation regarding the abstract. Consequently, the abstract has been updated to provide more details about the objectives, conducted investigations, and findings.
"Abstract: Phosphorus is a limited resource which is not replaceable worldwide. Its significant role as a fertilizer underlines the necessity for prudent and strategic management. Adequate monitoring of the phosphate extraction process will mitigate anything that can influence the quantity or quality of the product. The phosphate extraction process's most important phase is the screening unit, which is used to separate phosphate minerals from unwanted materials. Nevertheless, it encounters several anomalies and malfunctions that influence the performance of the whole chain. This unit requires continuous automated control to avoid any blockages or risks caused by malfunctions. Using artificial intelligence and image processing techniques, the main goal of the investigations described in this paper is to evaluate the performances of machine learning and deep learning models in detecting the screening unit malfunction in the open pit phosphate mine of Benguerir-Morocco. The findings highlight that the CNN and HOG-based models are the most suitable and accurate for the given case study."
Author Response File: Author Response.pdf
