These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

129 related articles for article (PubMed ID: 37978327)

  • 1. UAV-based individual Chinese cabbage weight prediction using multi-temporal data.
    Aguilar-Ariza A; Ishii M; Miyazaki T; Saito A; Khaing HP; Phoo HW; Kondo T; Fujiwara T; Guo W; Kamiya T
    Sci Rep; 2023 Nov; 13(1):20122. PubMed ID: 37978327
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest.
    Johansen K; Morton MJL; Malbeteau Y; Aragon B; Al-Mashharawi S; Ziliani MG; Angel Y; Fiene G; Negrão S; Mousa MAA; Tester MA; McCabe MF
    Front Artif Intell; 2020; 3():28. PubMed ID: 33733147
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering.
    Camenzind MP; Yu K
    Front Plant Sci; 2023; 14():1214931. PubMed ID: 38235203
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery.
    Tatsumi K; Igarashi N; Mengxue X
    Plant Methods; 2021 Jul; 17(1):77. PubMed ID: 34266447
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multispectral Drone Imagery and SRGAN for Rapid Phenotypic Mapping of Individual Chinese Cabbage Plants.
    Zhang J; Wang X; Liu J; Zhang D; Lu Y; Zhou Y; Sun L; Hou S; Fan X; Shen S; Zhao J
    Plant Phenomics; 2022; 2022():0007. PubMed ID: 37266137
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Ramie Yield Estimation Based on UAV RGB Images.
    Fu H; Wang C; Cui G; She W; Zhao L
    Sensors (Basel); 2021 Jan; 21(2):. PubMed ID: 33477949
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning.
    Bellis ES; Hashem AA; Causey JL; Runkle BRK; Moreno-García B; Burns BW; Green VS; Burcham TN; Reba ML; Huang X
    Front Plant Sci; 2022; 13():716506. PubMed ID: 35401643
    [TBL] [Abstract][Full Text] [Related]  

  • 8. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat.
    Fei S; Hassan MA; Xiao Y; Su X; Chen Z; Cheng Q; Duan F; Chen R; Ma Y
    Precis Agric; 2023; 24(1):187-212. PubMed ID: 35967193
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image.
    Ma Y; Ma L; Zhang Q; Huang C; Yi X; Chen X; Hou T; Lv X; Zhang Z
    Front Plant Sci; 2022; 13():925986. PubMed ID: 35783985
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Prediction of Buckwheat Maturity in UAV-RGB Images Based on Recursive Feature Elimination Cross-Validation: A Case Study in Jinzhong, Northern China.
    Wu J; Zheng D; Wu Z; Song H; Zhang X
    Plants (Basel); 2022 Nov; 11(23):. PubMed ID: 36501299
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data.
    Fei S; Hassan MA; Ma Y; Shu M; Cheng Q; Li Z; Chen Z; Xiao Y
    Front Plant Sci; 2021; 12():730181. PubMed ID: 34987529
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Droplet distribution in cotton canopy using single-rotor and four-rotor unmanned aerial vehicles.
    Meng Y; Ma Y; Wang Z; Hu H
    PeerJ; 2022; 10():e13572. PubMed ID: 35722263
    [TBL] [Abstract][Full Text] [Related]  

  • 13. High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation.
    Volpato L; Pinto F; González-Pérez L; Thompson IG; Borém A; Reynolds M; Gérard B; Molero G; Rodrigues FA
    Front Plant Sci; 2021; 12():591587. PubMed ID: 33664755
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. UAV-Borne Dual-Band Sensor Method for Monitoring Physiological Crop Status.
    Yao L; Wang Q; Yang J; Zhang Y; Zhu Y; Cao W; Ni J
    Sensors (Basel); 2019 Feb; 19(4):. PubMed ID: 30781552
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Estimation of spatial and temporal variability of pasture growth and digestibility in grazing rotations coupling unmanned aerial vehicle (UAV) with crop simulation models.
    Insua JR; Utsumi SA; Basso B
    PLoS One; 2019; 14(3):e0212773. PubMed ID: 30865650
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. Development of Cloud-Based UAV Monitoring and Management System.
    Itkin M; Kim M; Park Y
    Sensors (Basel); 2016 Nov; 16(11):. PubMed ID: 27854267
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.