BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

146 related articles for article (PubMed ID: 33851136)

  • 1. Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature.
    Wang J; Wu B; Kohnen MV; Lin D; Yang C; Wang X; Qiang A; Liu W; Kang J; Li H; Shen J; Yao T; Su J; Li B; Gu L
    Plant Phenomics; 2021; 2021():9765952. PubMed ID: 33851136
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging.
    Zhao X; Yuan Y; Song M; Ding Y; Lin F; Liang D; Zhang D
    Sensors (Basel); 2019 Sep; 19(18):. PubMed ID: 31500150
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Assessment of Soybean Lodging Using UAV Imagery and Machine Learning.
    Sarkar S; Zhou J; Scaboo A; Zhou J; Aloysius N; Lim TT
    Plants (Basel); 2023 Aug; 12(16):. PubMed ID: 37631105
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Research on fertilization decision method for rice tillering stage based on the coupling of UAV hyperspectral remote sensing and WOFOST.
    Li S; Jin Z; Bai J; Xiang S; Xu C; Yu F
    Front Plant Sci; 2024; 15():1405239. PubMed ID: 38911973
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods.
    Zhang J; Cheng T; Guo W; Xu X; Qiao H; Xie Y; Ma X
    Plant Methods; 2021 May; 17(1):49. PubMed ID: 33941211
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery.
    Wang F; Wang F; Zhang Y; Hu J; Huang J; Xie J
    Front Plant Sci; 2019; 10():453. PubMed ID: 31024607
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform.
    Zhou C; Ye H; Hu J; Shi X; Hua S; Yue J; Xu Z; Yang G
    Sensors (Basel); 2019 Jul; 19(14):. PubMed ID: 31337086
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.
    Tian F; Vieira CC; Zhou J; Zhou J; Chen P
    Sensors (Basel); 2023 Mar; 23(6):. PubMed ID: 36991952
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Remote Estimation of Rice Yield With Unmanned Aerial Vehicle (UAV) Data and Spectral Mixture Analysis.
    Duan B; Fang S; Zhu R; Wu X; Wang S; Gong Y; Peng Y
    Front Plant Sci; 2019; 10():204. PubMed ID: 30873194
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring.
    Ge X; Wang J; Ding J; Cao X; Zhang Z; Liu J; Li X
    PeerJ; 2019; 7():e6926. PubMed ID: 31110930
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress.
    Jiang Z; Tu H; Bai B; Yang C; Zhao B; Guo Z; Liu Q; Zhao H; Yang W; Xiong L; Zhang J
    New Phytol; 2021 Oct; 232(1):440-455. PubMed ID: 34165797
    [TBL] [Abstract][Full Text] [Related]  

  • 13. High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits.
    Chen R; Lu H; Wang Y; Tian Q; Zhou C; Wang A; Feng Q; Gong S; Zhao Q; Han B
    Front Plant Sci; 2024; 15():1327507. PubMed ID: 38562563
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Inversion modeling of japonica rice canopy chlorophyll content with UAV hyperspectral remote sensing.
    Cao Y; Jiang K; Wu J; Yu F; Du W; Xu T
    PLoS One; 2020; 15(9):e0238530. PubMed ID: 32915830
    [TBL] [Abstract][Full Text] [Related]  

  • 15. High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat.
    Singh D; Wang X; Kumar U; Gao L; Noor M; Imtiaz M; Singh RP; Poland J
    Front Plant Sci; 2019; 10():394. PubMed ID: 31019521
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Haplotype analysis from unmanned aerial vehicle imagery of rice MAGIC population for the trait dissection of biomass and plant architecture.
    Ogawa D; Sakamoto T; Tsunematsu H; Kanno N; Nonoue Y; Yonemaru JI
    J Exp Bot; 2021 Mar; 72(7):2371-2382. PubMed ID: 33367626
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology.
    Zhou J; Beche E; Vieira CC; Yungbluth D; Zhou J; Scaboo A; Chen P
    Front Plant Sci; 2021; 12():768742. PubMed ID: 35087547
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Mapping soil available copper content in the mine tailings pond with combined simulated annealing deep neural network and UAV hyperspectral images.
    Zhang Y; Wei L; Lu Q; Zhong Y; Yuan Z; Wang Z; Li Z; Yang Y
    Environ Pollut; 2023 Mar; 320():120962. PubMed ID: 36621716
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Estimating the rice nitrogen nutrition index based on hyperspectral transform technology.
    Yu F; Bai J; Jin Z; Zhang H; Yang J; Xu T
    Front Plant Sci; 2023; 14():1118098. PubMed ID: 37035061
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.