This article is a correction to:
Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery
Jikai Liu1,2†
Yongji Zhu1†Lijuan Song3,4Xiangxiang Su1Jun Li1Jing Zheng5Xueqing Zhu1Lantian Ren2,6Wenhui Wang5*Xinwei Li1,2*- 1College of Resource and Environment, Anhui Science and Technology University, Chuzhou, Anhui, China
- 2Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center, Anhui Science and Technology University, Chuzhou, Anhui, China
- 3Institute of Agricultural Remote Sensing and Information, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, China
- 4School of Management, Heilongjiang University of Science and Technology, Harbin, Heilongjiang, China
- 5College of Life Science, Langfang Normal University, Langfang, Hebei, China
- 6College of Agriculture, Anhui Science and Technology University, Chuzhou, Anhui, China
A Corrigendum on
Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery
by Liu J, Zhu Y, Song L, Su X, Li J, Zheng J, Zhu X, Ren L, Wang W and Li X (2023) Front. Plant Sci. 14:1284235. doi: 10.3389/fpls.2023.1284235
In the published article, there was an error in the UAVs data acquisition time of three rice phenological phase. 2 Materials and methods, 2.3.1 UAVs data acquisition and preprocessing, Paragraph 1 previously stated: “The DJI Phantom 4 Multispectral RTK (P4M) UAVs (DJI, Shenzhen, Guangdong, China) was used to acquire multispectral images at four growth stages, including the late tillering stage (LT: 25/07/2020), booting stage (B: 23/08/2023), heading to flowering stage (HtF: 31/08/2023), and early filling stage (EF: 09/09/2023) (Table 1).”
The corrected sentence appears below:
“The DJI Phantom 4 Multispectral RTK (P4M) UAVs (DJI, Shenzhen, Guangdong, China) was used to acquire multispectral images at four growth stages, including the late tillering stage (LT: 25/07/2020), booting stage (B: 23/08/2020), heading to flowering stage (HtF: 31/08/2020), and early filling stage (EF: 09/09/2020) (Table 1).”
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Keywords: unmanned aerial vehicles (UAVs), aboveground biomass (AGB), multispectral imagery, texture features (TFs), grey level co-occurrence matrix (GLCM), rice
Citation: Liu J, Zhu Y, Song L, Su X, Li J, Zheng J, Zhu X, Ren L, Wang W and Li X (2024) Corrigendum: Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery. Front. Plant Sci. 15:1378628. doi: 10.3389/fpls.2024.1378628
Received: 30 January 2024; Accepted: 15 February 2024;
Published: 28 February 2024.
Edited and Reviewed by:
Zhenjiang Zhou, Zhejiang University, ChinaCopyright © 2024 Liu, Zhu, Song, Su, Li, Zheng, Zhu, Ren, Wang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xinwei Li, lixw@ahstu.edu.cn; Wenhui Wang, 1172139@lfnu.edu.cn
†These authors have contributed equally to this work