153 related articles for article (PubMed ID: 37273463)
1. Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models.
Yang K; Mo J; Luo S; Peng Y; Fang S; Wu X; Zhu R; Li Y; Yuan N; Zhou C; Gong Y
Plant Phenomics; 2023; 5():0056. PubMed ID: 37273463
[TBL] [Abstract][Full Text] [Related]
2. Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages.
Dai Y; Yu S; Ma T; Ding J; Chen K; Zeng G; Xie A; He P; Peng S; Zhang M
Front Plant Sci; 2024; 15():1328834. PubMed ID: 38774220
[TBL] [Abstract][Full Text] [Related]
3. Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season.
Gong Y; Yang K; Lin Z; Fang S; Wu X; Zhu R; Peng Y
Plant Methods; 2021 Aug; 17(1):88. PubMed ID: 34376195
[TBL] [Abstract][Full Text] [Related]
4. Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system.
Lu N; Zhou J; Han Z; Li D; Cao Q; Yao X; Tian Y; Zhu Y; Cao W; Cheng T
Plant Methods; 2019; 15():17. PubMed ID: 30828356
[TBL] [Abstract][Full Text] [Related]
5. Estimation of Rice Aboveground Biomass by Combining Canopy Spectral Reflectance and Unmanned Aerial Vehicle-Based Red Green Blue Imagery Data.
Wang Z; Ma Y; Chen P; Yang Y; Fu H; Yang F; Raza MA; Guo C; Shu C; Sun Y; Yang Z; Chen Z; Ma J
Front Plant Sci; 2022; 13():903643. PubMed ID: 35712565
[TBL] [Abstract][Full Text] [Related]
6. Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras.
Cen H; Wan L; Zhu J; Li Y; Li X; Zhu Y; Weng H; Wu W; Yin W; Xu C; Bao Y; Feng L; Shou J; He Y
Plant Methods; 2019; 15():32. PubMed ID: 30972143
[TBL] [Abstract][Full Text] [Related]
7. UAV and Satellite Synergies for Mapping Grassland Aboveground Biomass in Hulunbuir Meadow Steppe.
Zhu X; Chen X; Ma L; Liu W
Plants (Basel); 2024 Mar; 13(7):. PubMed ID: 38611535
[TBL] [Abstract][Full Text] [Related]
8. Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.
Liu J; Zhu Y; Song L; Su X; Li J; Zheng J; Zhu X; Ren L; Wang W; Li X
Front Plant Sci; 2023; 14():1284235. PubMed ID: 38192693
[TBL] [Abstract][Full Text] [Related]
9. Aboveground biomass estimation of wetland vegetation at the species level using unoccupied aerial vehicle RGB imagery.
Zhou R; Yang C; Li E; Cai X; Wang X
Front Plant Sci; 2023; 14():1181887. PubMed ID: 37528979
[TBL] [Abstract][Full Text] [Related]
10. Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height.
Liu Y; Feng H; Yue J; Jin X; Li Z; Yang G
Front Plant Sci; 2022; 13():938216. PubMed ID: 36092445
[TBL] [Abstract][Full Text] [Related]
11. Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation.
Shi Y; Gao Y; Wang Y; Luo D; Chen S; Ding Z; Fan K
Front Plant Sci; 2022; 13():820585. PubMed ID: 35283919
[TBL] [Abstract][Full Text] [Related]
12. Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (
Selvaraj MG; Valderrama M; Guzman D; Valencia M; Ruiz H; Acharjee A
Plant Methods; 2020; 16():87. PubMed ID: 32549903
[TBL] [Abstract][Full Text] [Related]
13. Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index.
Zhou C; Gong Y; Fang S; Yang K; Peng Y; Wu X; Zhu R
Front Plant Sci; 2022; 13():957870. PubMed ID: 35991436
[TBL] [Abstract][Full Text] [Related]
14. Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm.
Nian Y; Su X; Yue H; Zhu Y; Li J; Wang W; Sheng Y; Ma Q; Liu J; Li X
Front Plant Sci; 2024; 15():1396183. PubMed ID: 38726299
[TBL] [Abstract][Full Text] [Related]
15. Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model.
Cheng Z; Meng J; Shang J; Liu J; Huang J; Qiao Y; Qian B; Jing Q; Dong T; Yu L
Sensors (Basel); 2020 Oct; 20(21):. PubMed ID: 33113905
[TBL] [Abstract][Full Text] [Related]
16. Improved estimation of aboveground biomass of regional coniferous forests integrating UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries.
Wang Y; Jia X; Chai G; Lei L; Zhang X
Plant Methods; 2023 Jun; 19(1):65. PubMed ID: 37391772
[TBL] [Abstract][Full Text] [Related]
17. Vegetation growth status as an early warning indicator for the spontaneous combustion disaster of coal waste dump after reclamation: An unmanned aerial vehicle remote sensing approach.
Ren H; Zhao Y; Xiao W; Zhang J; Chen C; Ding B; Yang X
J Environ Manage; 2022 Sep; 317():115502. PubMed ID: 35751291
[TBL] [Abstract][Full Text] [Related]
18. Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice.
Zheng H; Cheng T; Li D; Yao X; Tian Y; Cao W; Zhu Y
Front Plant Sci; 2018; 9():936. PubMed ID: 30034405
[TBL] [Abstract][Full Text] [Related]
19. Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data.
Zhu Y; Zhao C; Yang H; Yang G; Han L; Li Z; Feng H; Xu B; Wu J; Lei L
PeerJ; 2019; 7():e7593. PubMed ID: 31576235
[TBL] [Abstract][Full Text] [Related]
20. Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China.
Jiang F; Deng M; Tang J; Fu L; Sun H
Carbon Balance Manag; 2022 Sep; 17(1):12. PubMed ID: 36048352
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
