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 *

173 related articles for article (PubMed ID: 35498682)

  • 41. Predicting plant biomass accumulation from image-derived parameters.
    Chen D; Shi R; Pape JM; Neumann K; Arend D; Graner A; Chen M; Klukas C
    Gigascience; 2018 Feb; 7(2):1-13. PubMed ID: 29346559
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.
    Woldaregay AZ; Årsand E; Walderhaug S; Albers D; Mamykina L; Botsis T; Hartvigsen G
    Artif Intell Med; 2019 Jul; 98():109-134. PubMed ID: 31383477
    [TBL] [Abstract][Full Text] [Related]  

  • 43. A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping.
    Ye D; Wu L; Li X; Atoba TO; Wu W; Weng H
    Plants (Basel); 2023 Apr; 12(8):. PubMed ID: 37111921
    [TBL] [Abstract][Full Text] [Related]  

  • 44. Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops.
    Jimenez-Sierra DA; Correa ES; Benítez-Restrepo HD; Calderon FC; Mondragon IF; Colorado JD
    Sensors (Basel); 2021 Jun; 21(13):. PubMed ID: 34202363
    [TBL] [Abstract][Full Text] [Related]  

  • 45. Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning.
    Garbouge H; Rasti P; Rousseau D
    Sensors (Basel); 2021 Dec; 21(24):. PubMed ID: 34960519
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery.
    Chandel NS; Rajwade YA; Dubey K; Chandel AK; Subeesh A; Tiwari MK
    Plants (Basel); 2022 Dec; 11(23):. PubMed ID: 36501383
    [TBL] [Abstract][Full Text] [Related]  

  • 47. 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]  

  • 48. An explainable deep machine vision framework for plant stress phenotyping.
    Ghosal S; Blystone D; Singh AK; Ganapathysubramanian B; Singh A; Sarkar S
    Proc Natl Acad Sci U S A; 2018 May; 115(18):4613-4618. PubMed ID: 29666265
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops.
    Hu P; Chapman SC; Zheng B
    Funct Plant Biol; 2021 Jul; 48(8):766-779. PubMed ID: 33663681
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Research on precise phenotype identification and growth prediction of lettuce based on deep learning.
    Yu H; Dong M; Zhao R; Zhang L; Sui Y
    Environ Res; 2024 Jul; 252(Pt 1):118845. PubMed ID: 38570128
    [TBL] [Abstract][Full Text] [Related]  

  • 51. SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques.
    Maji AK; Marwaha S; Kumar S; Arora A; Chinnusamy V; Islam S
    Front Plant Sci; 2022; 13():889853. PubMed ID: 35991448
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data.
    Han L; Yang G; Dai H; Xu B; Yang H; Feng H; Li Z; Yang X
    Plant Methods; 2019; 15():10. PubMed ID: 30740136
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data.
    Sakeef N; Scandola S; Kennedy C; Lummer C; Chang J; Uhrig RG; Lin G
    Comput Struct Biotechnol J; 2023; 21():3183-3195. PubMed ID: 37333861
    [TBL] [Abstract][Full Text] [Related]  

  • 54. A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications.
    Mathew J; Delavarpour N; Miranda C; Stenger J; Zhang Z; Aduteye J; Flores P
    Sensors (Basel); 2023 Jul; 23(14):. PubMed ID: 37514799
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Point Cloud Completion of Plant Leaves under Occlusion Conditions Based on Deep Learning.
    Chen H; Liu S; Wang C; Wang C; Gong K; Li Y; Lan Y
    Plant Phenomics; 2023; 5():0117. PubMed ID: 38239737
    [TBL] [Abstract][Full Text] [Related]  

  • 56. Estimating plant biomass in agroecosystems using a drop-plate meter.
    Robertson SM; Schmid RB; Lundgren JG
    PeerJ; 2023; 11():e15740. PubMed ID: 37547713
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production.
    Bauer A; Bostrom AG; Ball J; Applegate C; Cheng T; Laycock S; Rojas SM; Kirwan J; Zhou J
    Hortic Res; 2019; 6():70. PubMed ID: 31231528
    [TBL] [Abstract][Full Text] [Related]  

  • 58. A Monitoring System for the Segmentation and Grading of Broccoli Head Based on Deep Learning and Neural Networks.
    Zhou C; Hu J; Xu Z; Yue J; Ye H; Yang G
    Front Plant Sci; 2020; 11():402. PubMed ID: 32351523
    [TBL] [Abstract][Full Text] [Related]  

  • 59. BreedVision--a multi-sensor platform for non-destructive field-based phenotyping in plant breeding.
    Busemeyer L; Mentrup D; Möller K; Wunder E; Alheit K; Hahn V; Maurer HP; Reif JC; Würschum T; Müller J; Rahe F; Ruckelshausen A
    Sensors (Basel); 2013 Feb; 13(3):2830-47. PubMed ID: 23447014
    [TBL] [Abstract][Full Text] [Related]  

  • 60. Development of a machine vision-based weight prediction system of butterhead lettuce (
    Kim JG; Moon S; Park J; Kim T; Chung S
    Front Plant Sci; 2024; 15():1365266. PubMed ID: 38903437
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.