146 related articles for article (PubMed ID: 38192698)
1. Prediction of vertical distribution of SPAD values within maize canopy based on unmanned aerial vehicles multispectral imagery.
Chen B; Huang G; Lu X; Gu S; Wen W; Wang G; Chang W; Guo X; Zhao C
Front Plant Sci; 2023; 14():1253536. PubMed ID: 38192698
[TBL] [Abstract][Full Text] [Related]
2. Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery.
Gao W; Zeng W; Li S; Zhang L; Wang W; Song J; Wu H
PLoS One; 2024; 19(6):e0300056. PubMed ID: 38905187
[TBL] [Abstract][Full Text] [Related]
3. Inversion of Winter Wheat Growth Parameters and Yield Under Different Water Treatments Based on UAV Multispectral Remote Sensing.
Han X; Wei Z; Chen H; Zhang B; Li Y; Du T
Front Plant Sci; 2021; 12():609876. PubMed ID: 34093601
[TBL] [Abstract][Full Text] [Related]
4. Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage.
Zhang S; Zhao G; Lang K; Su B; Chen X; Xi X; Zhang H
Sensors (Basel); 2019 Mar; 19(7):. PubMed ID: 30934683
[TBL] [Abstract][Full Text] [Related]
5. Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.
Su X; Nian Y; Shaghaleh H; Hamad A; Yue H; Zhu Y; Li J; Wang W; Wang H; Ma Q; Liu J; Li X; Alhaj Hamoud Y
Front Plant Sci; 2024; 15():1404238. PubMed ID: 38799101
[TBL] [Abstract][Full Text] [Related]
6. [Winter wheat GPC estimation based on leaf and canopy chlorophyll parameters].
Song XY; Wang JH; Yang GJ; Cui B; Chang H
Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Jul; 34(7):1917-21. PubMed ID: 25269308
[TBL] [Abstract][Full Text] [Related]
7. Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy.
Chakhvashvili E; Siegmann B; Muller O; Verrelst J; Bendig J; Kraska T; Rascher U
Remote Sens (Basel); 2022 Mar; 14(5):1247. PubMed ID: 36082321
[TBL] [Abstract][Full Text] [Related]
8. Prediction of Chlorophyll Content in Multi-Temporal Winter Wheat Based on Multispectral and Machine Learning.
Wang W; Cheng Y; Ren Y; Zhang Z; Geng H
Front Plant Sci; 2022; 13():896408. PubMed ID: 35712585
[TBL] [Abstract][Full Text] [Related]
9. Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method.
Lu J; Qiu H; Zhang Q; Lan Y; Wang P; Wu Y; Mo J; Chen W; Niu H; Wu Z
Front Plant Sci; 2022; 13():1009630. PubMed ID: 36247579
[TBL] [Abstract][Full Text] [Related]
10. [Research on maize multispectral image accurate segmentation and chlorophyll index estimation].
Wu Q; Sun H; Li MZ; Song YY; Zhang YE
Guang Pu Xue Yu Guang Pu Fen Xi; 2015 Jan; 35(1):178-83. PubMed ID: 25993844
[TBL] [Abstract][Full Text] [Related]
11. Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery.
Lu N; Wang W; Zhang Q; Li D; Yao X; Tian Y; Zhu Y; Cao W; Baret F; Liu S; Cheng T
Front Plant Sci; 2019; 10():1601. PubMed ID: 31921250
[TBL] [Abstract][Full Text] [Related]
12. Off-Nadir Hyperspectral Sensing for Estimation of Vertical Profile of Leaf Chlorophyll Content within Wheat Canopies.
Kong W; Huang W; Casa R; Zhou X; Ye H; Dong Y
Sensors (Basel); 2017 Nov; 17(12):. PubMed ID: 29168757
[TBL] [Abstract][Full Text] [Related]
13. Performance comparison of RGB and multispectral vegetation indices based on machine learning for estimating
Yuan Y; Wang X; Shi M; Wang P
Front Plant Sci; 2022; 13():928953. PubMed ID: 35937316
[TBL] [Abstract][Full Text] [Related]
14. Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods.
Guo Y; Yin G; Sun H; Wang H; Chen S; Senthilnath J; Wang J; Fu Y
Sensors (Basel); 2020 Sep; 20(18):. PubMed ID: 32916808
[TBL] [Abstract][Full Text] [Related]
15. A robust spectral angle index for remotely assessing soybean canopy chlorophyll content in different growing stages.
Yue J; Feng H; Tian Q; Zhou C
Plant Methods; 2020; 16():104. PubMed ID: 32765637
[TBL] [Abstract][Full Text] [Related]
16. Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques.
Buchaillot ML; Gracia-Romero A; Vergara-Diaz O; Zaman-Allah MA; Tarekegne A; Cairns JE; Prasanna BM; Araus JL; Kefauver SC
Sensors (Basel); 2019 Apr; 19(8):. PubMed ID: 30995754
[TBL] [Abstract][Full Text] [Related]
17. Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features.
Sun X; Yang Z; Su P; Wei K; Wang Z; Yang C; Wang C; Qin M; Xiao L; Yang W; Zhang M; Song X; Feng M
Front Plant Sci; 2023; 14():1158837. PubMed ID: 37063231
[TBL] [Abstract][Full Text] [Related]
18. Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning.
Dhakal R; Maimaitijiang M; Chang J; Caffe M
Sensors (Basel); 2023 Dec; 23(24):. PubMed ID: 38139554
[TBL] [Abstract][Full Text] [Related]
19. Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters.
Wijesingha J; Dayananda S; Wachendorf M; Astor T
Sensors (Basel); 2021 Apr; 21(8):. PubMed ID: 33924176
[TBL] [Abstract][Full Text] [Related]
20. Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices.
Zhang Y; Han W; Niu X; Li G
Sensors (Basel); 2019 Nov; 19(23):. PubMed ID: 31795309
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]