Integration of Visible and Thermal Imagery with an Artificial Neural Network Approach for Robust Forecasting of Canopy Water Content in Rice

A total of 120 rice plant samples were scanned by visible and thermal proximal sensing systems under different water stress levels to evaluate the canopy water content (CWC). The oven-drying method was employed for assessing the canopy’s water state. This CWC is of great importance for irrigation ma...

Full description

Bibliographic Details

Published in:	Remote Sensing
Main Authors:	Osama Elsherbiny, Lei Zhou, Lei Feng, Zhengjun Qiu
Format:	Article
Language:	English
Published:	MDPI AG 2021-05-01
Subjects:	canopy water content GLCM features color vegetation indices thermal indicators high-quality features ANN
Online Access:	https://www.mdpi.com/2072-4292/13/9/1785