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Remote Sensing
Volume 14
Issue 10
10.3390/rs14102313
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Correction to Remote Sens. 2021, 13(14), 2822.
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Correction

Correction: Lin, Z.; Guo, W. Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models. Remote Sens. 2021, 13, 2822

by
Zhe Lin
1 and
Wenxuan Guo
1,2,*
1
Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA
2
Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Lubbock, TX 79403, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(10), 2313; https://doi.org/10.3390/rs14102313
Submission received: 13 September 2021 / Accepted: 21 April 2022 / Published: 11 May 2022
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Versions Notes
In the original publication [1], there are two duplicated paragraphs in Section 3.2 on pages 8, 10, and 11, as published. The duplicated paragraphs under Table 2 should be removed and the position of Figures 3 and 4 should be updated accordingly.
The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original publication has also been updated.

Reference

  1. Lin, Z.; Guo, W. Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models. Remote Sens. 2021, 13, 2822. [Google Scholar] [CrossRef]
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MDPI and ACS Style

Lin, Z.; Guo, W. Correction: Lin, Z.; Guo, W. Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models. Remote Sens. 2021, 13, 2822. Remote Sens. 2022, 14, 2313. https://doi.org/10.3390/rs14102313

AMA Style

Lin Z, Guo W. Correction: Lin, Z.; Guo, W. Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models. Remote Sens. 2021, 13, 2822. Remote Sensing. 2022; 14(10):2313. https://doi.org/10.3390/rs14102313

Chicago/Turabian Style

Lin, Zhe, and Wenxuan Guo. 2022. "Correction: Lin, Z.; Guo, W. Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models. Remote Sens. 2021, 13, 2822" Remote Sensing 14, no. 10: 2313. https://doi.org/10.3390/rs14102313

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