138 related articles for article (PubMed ID: 37615731)
1. An integrative data-driven approach for monitoring corn biomass under irrigation water and nitrogen levels based on UAV-based imagery.
Feizolahpour F; Besharat S; Feizizadeh B; Rezaverdinejad V; Hessari B
Environ Monit Assess; 2023 Aug; 195(9):1081. PubMed ID: 37615731
[TBL] [Abstract][Full Text] [Related]
2. UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping.
Nguyen C; Sagan V; Bhadra S; Moose S
Sensors (Basel); 2023 Feb; 23(4):. PubMed ID: 36850425
[TBL] [Abstract][Full Text] [Related]
3. Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning.
Sharma P; Leigh L; Chang J; Maimaitijiang M; Caffé M
Sensors (Basel); 2022 Jan; 22(2):. PubMed ID: 35062559
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Growth Monitoring and Yield Estimation of Maize Plant Using Unmanned Aerial Vehicle (UAV) in a Hilly Region.
Sapkota S; Paudyal DR
Sensors (Basel); 2023 Jun; 23(12):. PubMed ID: 37420599
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform.
Hassan MA; Yang M; Rasheed A; Yang G; Reynolds M; Xia X; Xiao Y; He Z
Plant Sci; 2019 May; 282():95-103. PubMed ID: 31003615
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. High-Throughput Phenotyping of Bioethanol Potential in Cereals Using UAV-Based Multi-Spectral Imagery.
Ostos-Garrido FJ; de Castro AI; Torres-Sánchez J; Pistón F; Peña JM
Front Plant Sci; 2019; 10():948. PubMed ID: 31396251
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
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. 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]
14. 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]
15. 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]
16. Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat.
Yang M; Hassan MA; Xu K; Zheng C; Rasheed A; Zhang Y; Jin X; Xia X; Xiao Y; He Z
Front Plant Sci; 2020; 11():927. PubMed ID: 32676089
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Monitoring the damage of armyworm as a pest in summer corn by unmanned aerial vehicle imaging.
Tao W; Wang X; Xue JH; Su W; Zhang M; Yin D; Zhu D; Xie Z; Zhang Y
Pest Manag Sci; 2022 Jun; 78(6):2265-2276. PubMed ID: 35229453
[TBL] [Abstract][Full Text] [Related]
19. Estimating yield-contributing physiological parameters of cotton using UAV-based imagery.
Pokhrel A; Virk S; Snider JL; Vellidis G; Hand LC; Sintim HY; Parkash V; Chalise DP; Lee JM; Byers C
Front Plant Sci; 2023; 14():1248152. PubMed ID: 37794937
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
