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CORRECTION article

Front. For. Glob. Change, 08 September 2023
Sec. Forest Growth
Volume 6 - 2023 | https://doi.org/10.3389/ffgc.2023.1285232

Corrigendum: Classification of tree symbiotic fungi based on hyperspectral imagery and hybrid convolutional neural networks

This article is a correction to:

  1. Classification of tree symbiotic fungi based on hyperspectral imagery and hybrid convolutional neural networks

    1. Read original article
\r\nZhuo Liu,Zhuo Liu1,2Mahmoud Al-SarayrehMahmoud Al-Sarayreh3Yanjie Li Yanjie Li1*Zhilin Yuan Zhilin Yuan1*
  • 1Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, China
  • 2College of Forestry, Nanjing Forestry University, Nanjing, China
  • 3Department of Computer Engineering, German Jordanian University, Amman, Jordan

A corrigendum on
Classification of tree symbiotic fungi based on hyperspectral imagery and hybrid convolutional neural networks

by Liu, Z., Al-Sarayreh, M., Li, Y., and Yuan, Z. (2023). Front. For. Glob. Change 6:1179910. doi: 10.3389/ffgc.2023.1179910

In the published article, there was an error in affiliation [2]. Instead of “2 Department of Computer Engineering, German Jordanian University, Amman, Jordan”, it should be “3 Department of Computer Engineering, German Jordanian University, Amman, Jordan”. There was an error regarding the affiliation(s) for [Mahmoud Al-Sarayreh], it should be “3 Department of Computer Engineering, German Jordanian University, Amman, Jordan”.

In the published article, there was an error regarding the affiliation(s) for [Zhuo Liu]. As well as having affiliation(s) [1], they should also have [2 College of Forestry, Nanjing Forestry University, Nanjing, China].

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: dark septate endophytes (DSEs), 2D-CNN, 3D-CNN, deep learning, spectral pre-processing, hyperspectral imaging (HSI)

Citation: Liu Z, Al-Sarayreh M, Li Y and Yuan Z (2023) Corrigendum: Classification of tree symbiotic fungi based on hyperspectral imagery and hybrid convolutional neural networks. Front. For. Glob. Change 6:1285232. doi: 10.3389/ffgc.2023.1285232

Received: 29 August 2023; Accepted: 30 August 2023;
Published: 08 September 2023.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2023 Liu, Al-Sarayreh, Li and Yuan. 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: Yanjie Li, aj7105@gmail.com; Zhilin Yuan, yuanzl@caf.ac.cn

Disclaimer: 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.