Detalhe da pesquisa
1.
Analysis and Evaluation of the Image Preprocessing Process of a Six-Band Multispectral Camera Mounted on an Unmanned Aerial Vehicle for Winter Wheat Monitoring.
Sensors (Basel)
; 19(3)2019 Feb 12.
Artigo
em Inglês
| MEDLINE | ID: mdl-30759869
2.
Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China).
Sensors (Basel)
; 17(4)2017 Mar 24.
Artigo
em Inglês
| MEDLINE | ID: mdl-28338637
3.
SPSI: A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery.
Plant Phenomics
; 5: 0087, 2023.
Artigo
em Inglês
| MEDLINE | ID: mdl-37681001
4.
Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR.
Plant Methods
; 19(1): 134, 2023 Nov 26.
Artigo
em Inglês
| MEDLINE | ID: mdl-38007501
5.
Fusarium head blight monitoring in wheat ears using machine learning and multimodal data from asymptomatic to symptomatic periods.
Front Plant Sci
; 13: 1102341, 2022.
Artigo
em Inglês
| MEDLINE | ID: mdl-36726669
6.
Estimation of area- and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis.
Plant Methods
; 14: 76, 2018.
Artigo
em Inglês
| MEDLINE | ID: mdl-30181765
7.
Assessing the Impact of Spatial Resolution on the Estimation of Leaf Nitrogen Concentration Over the Full Season of Paddy Rice Using Near-Surface Imaging Spectroscopy Data.
Front Plant Sci
; 9: 964, 2018.
Artigo
em Inglês
| MEDLINE | ID: mdl-30026750
8.
Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice.
Front Plant Sci
; 9: 936, 2018.
Artigo
em Inglês
| MEDLINE | ID: mdl-30034405