Development of Prediction Models for Estimating Key Rice Growth Variables Using Visible and NIR Images from Unmanned Aerial Systems

Development of Prediction Models for Estimating Key Rice Growth Variables Using Visible and NIR Images from Unmanned Aerial Systems

The rapid and accurate acquisition of rice growth variables using unmanned aerial system (UAS) is useful for assessing rice growth and variable fertilization in precision agriculture. In this study, rice plant height (PH), leaf area index (LAI), aboveground biomass (AGB), and nitrogen nutrient index...

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Bibliographic Details
Main Authors: Zhengchao Qiu, Fei Ma, Zhenwang Li, Xuebin Xu, Changwen Du
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Remote Sensing
Subjects:
rice growth variables
unmanned aerial system
multispectral imagery
digital imagery
random forest model
Online Access:https://www.mdpi.com/2072-4292/14/6/1384

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