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 *

124 related articles for article (PubMed ID: 37131210)

  • 21. Establishment of integrated protocols for automated high throughput kinetic chlorophyll fluorescence analyses.
    Tschiersch H; Junker A; Meyer RC; Altmann T
    Plant Methods; 2017; 13():54. PubMed ID: 28690669
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Low-cost 3D systems: suitable tools for plant phenotyping.
    Paulus S; Behmann J; Mahlein AK; Plümer L; Kuhlmann H
    Sensors (Basel); 2014 Feb; 14(2):3001-18. PubMed ID: 24534920
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping.
    Mahlein AK
    Plant Dis; 2016 Feb; 100(2):241-251. PubMed ID: 30694129
    [TBL] [Abstract][Full Text] [Related]  

  • 24.
    Merchuk-Ovnat L; Ovnat Z; Amir-Segev O; Kutsher Y; Saranga Y; Reuveni M
    Plant Methods; 2019; 15():90. PubMed ID: 31404403
    [TBL] [Abstract][Full Text] [Related]  

  • 25. PI-Plat: a high-resolution image-based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits.
    Sandhu J; Zhu F; Paul P; Gao T; Dhatt BK; Ge Y; Staswick P; Yu H; Walia H
    Plant Methods; 2019; 15():162. PubMed ID: 31889986
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Automated acquisition of top-view dairy cow depth image data using an RGB-D sensor camera.
    Kadlec R; Indest S; Castro K; Waqar S; Campos LM; Amorim ST; Bi Y; Hanigan MD; Morota G
    Transl Anim Sci; 2022 Oct; 6(4):txac163. PubMed ID: 36601061
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera.
    Song P; Li Z; Yang M; Shao Y; Pu Z; Yang W; Zhai R
    Front Plant Sci; 2023; 14():1097725. PubMed ID: 36778701
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Erratum: High-Throughput Identification of Resistance to Pseudomonas syringae pv. Tomato in Tomato using Seedling Flood Assay.
    J Vis Exp; 2023 Oct; (200):. PubMed ID: 37851522
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat.
    Ren A; Jiang D; Kang M; Wu J; Xiao F; Hou P; Fu X
    Plant Methods; 2022 Jun; 18(1):77. PubMed ID: 35672714
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Non-destructive Plant Biomass Monitoring With High Spatio-Temporal Resolution
    Buxbaum N; Lieth JH; Earles M
    Front Plant Sci; 2022; 13():758818. PubMed ID: 35498682
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Computer vision and machine learning enabled soybean root phenotyping pipeline.
    Falk KG; Jubery TZ; Mirnezami SV; Parmley KA; Sarkar S; Singh A; Ganapathysubramanian B; Singh AK
    Plant Methods; 2020; 16():5. PubMed ID: 31993072
    [TBL] [Abstract][Full Text] [Related]  

  • 32. PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments.
    Valle B; Simonneau T; Boulord R; Sourd F; Frisson T; Ryckewaert M; Hamard P; Brichet N; Dauzat M; Christophe A
    Plant Methods; 2017; 13():98. PubMed ID: 29151844
    [TBL] [Abstract][Full Text] [Related]  

  • 33. RGB image-based method for phenotyping rust disease progress in pea leaves using R.
    Osuna-Caballero S; Olivoto T; Jiménez-Vaquero MA; Rubiales D; Rispail N
    Plant Methods; 2023 Aug; 19(1):86. PubMed ID: 37605206
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Assessing the Performance of RGB-D Sensors for 3D Fruit Crop Canopy Characterization under Different Operating and Lighting Conditions.
    Gené-Mola J; Llorens J; Rosell-Polo JR; Gregorio E; Arnó J; Solanelles F; Martínez-Casasnovas JA; Escolà A
    Sensors (Basel); 2020 Dec; 20(24):. PubMed ID: 33321817
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (
    Quirós Vargas JJ; Zhang C; Smitchger JA; McGee RJ; Sankaran S
    Sensors (Basel); 2019 Apr; 19(9):. PubMed ID: 31052251
    [TBL] [Abstract][Full Text] [Related]  

  • 36. A spatio temporal spectral framework for plant stress phenotyping.
    Khanna R; Schmid L; Walter A; Nieto J; Siegwart R; Liebisch F
    Plant Methods; 2019; 15():13. PubMed ID: 30774703
    [TBL] [Abstract][Full Text] [Related]  

  • 37. AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping.
    Pound MP; Fozard S; Torres Torres M; Forde BG; French AP
    Plant Methods; 2017; 13():12. PubMed ID: 28286542
    [TBL] [Abstract][Full Text] [Related]  

  • 38. GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton.
    Jiang Y; Li C; Robertson JS; Sun S; Xu R; Paterson AH
    Sci Rep; 2018 Jan; 8(1):1213. PubMed ID: 29352136
    [TBL] [Abstract][Full Text] [Related]  

  • 39. [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]  

  • 40. Estimating Biomass and Canopy Height With LiDAR for Field Crop Breeding.
    Walter JDC; Edwards J; McDonald G; Kuchel H
    Front Plant Sci; 2019; 10():1145. PubMed ID: 31611889
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.