Spatio-Temporal Estimation of Biomass Growth in Rice Using Canopy Surface Model from Unmanned Aerial Vehicle Images
The awareness of spatial and temporal variations in site-specific crop parameters, such as aboveground biomass (total dry weight: (TDW), plant length (PL) and leaf area index (LAI), help in formulating appropriate management decisions. However, conventional monitoring methods rely on time-consuming...
Main Authors: | Clement Oppong Peprah, Megumi Yamashita, Tomoaki Yamaguchi, Ryo Sekino, Kyohei Takano, Keisuke Katsura |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-06-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/13/12/2388 |
Similar Items
-
Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice
by: Tomoaki Yamaguchi, et al.
Published: (2020-12-01) -
Spatial Estimation of Daily Growth Biomass in Paddy Rice Field Using Canopy Photosynthesis Model Based on Ground and UAV Observations
by: Megumi Yamashita, et al.
Published: (2023-12-01) -
RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System
by: Grayson R. Morgan, et al.
Published: (2021-08-01) -
Unmanned aerial vehicles
by: Vlado Jurić, et al.
Published: (2016-12-01) -
An Investigation of Winter Wheat Leaf Area Index Fitting Model Using Spectral and Canopy Height Model Data from Unmanned Aerial Vehicle Imagery
by: Xuewei Zhang, et al.
Published: (2022-10-01)