Corrigendum: Retrieving rice (Oryza sativa L.) net photosynthetic rate from UAV multispectral images based on machine learning methods

Front Plant Sci. 2023 Jun 13:14:1229908. doi: 10.3389/fpls.2023.1229908. eCollection 2023.

Tianao Wu^{1

2

3}, Wei Zhang¹, Shuyu Wu^{1

2

3}, Minghan Cheng⁴, Lushang Qi¹, Guangcheng Shao¹, Xiyun Jiao^{1

2

3}

¹ College of Agricultural Science and Engineering, Hohai University, Nanjing, China.
² State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China.
³ Cooperative Innovation Center for Water Safety and Hydro Science, Hohai University, Nanjing, China.
⁴ Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou, China.

Abstract

[This corrects the article DOI: 10.3389/fpls.2022.1088499.].

Keywords: UAV multispectral remote sensing; machine learning; net photosynthetic rate; rice canopy; textural index; vegetation index.