BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

197 related articles for article (PubMed ID: 29209408)

  • 1. Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization.
    Xiong X; Duan L; Liu L; Tu H; Yang P; Wu D; Chen G; Xiong L; Yang W; Liu Q
    Plant Methods; 2017; 13():104. PubMed ID: 29209408
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Field rice panicle detection and counting based on deep learning.
    Wang X; Yang W; Lv Q; Huang C; Liang X; Chen G; Xiong L; Duan L
    Front Plant Sci; 2022; 13():966495. PubMed ID: 36035660
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Sorghum Panicle Detection and Counting Using Unmanned Aerial System Images and Deep Learning.
    Lin Z; Guo W
    Front Plant Sci; 2020; 11():534853. PubMed ID: 32983210
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning.
    Yang Z; Gao S; Xiao F; Li G; Ding Y; Guo Q; Paul MJ; Liu Z
    Plant Methods; 2020; 16():117. PubMed ID: 32863854
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.
    Teng Z; Chen J; Wang J; Wu S; Chen R; Lin Y; Shen L; Jackson R; Zhou J; Yang C
    Plant Phenomics; 2023; 5():0105. PubMed ID: 37850120
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Unsupervised Bayesian learning for rice panicle segmentation with UAV images.
    Hayat MA; Wu J; Cao Y
    Plant Methods; 2020; 16():18. PubMed ID: 32123536
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Mask R-CNN-based feature extraction and three-dimensional recognition of rice panicle CT images.
    Kong H; Chen P
    Plant Direct; 2021 May; 5(5):e00323. PubMed ID: 33981945
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model.
    Sun B; Zhou W; Zhu S; Huang S; Yu X; Wu Z; Lei X; Yin D; Xia H; Chen Y; Deng F; Tao Y; Cheng H; Jin X; Ren W
    Front Plant Sci; 2022; 13():1021398. PubMed ID: 36420030
    [TBL] [Abstract][Full Text] [Related]  

  • 10. High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning.
    Lu Y; Wang J; Fu L; Yu L; Liu Q
    Front Plant Sci; 2023; 14():1219584. PubMed ID: 37790779
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Automated Counting Grains on the Rice Panicle Based on Deep Learning Method.
    Deng R; Tao M; Huang X; Bangura K; Jiang Q; Jiang Y; Qi L
    Sensors (Basel); 2021 Jan; 21(1):. PubMed ID: 33406615
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Panicle-3D: Efficient Phenotyping Tool for Precise Semantic Segmentation of Rice Panicle Point Cloud.
    Gong L; Du X; Zhu K; Lin K; Lou Q; Yuan Z; Huang G; Liu C
    Plant Phenomics; 2021; 2021():9838929. PubMed ID: 35024618
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Automatic estimation of heading date of paddy rice using deep learning.
    Desai SV; Balasubramanian VN; Fukatsu T; Ninomiya S; Guo W
    Plant Methods; 2019; 15():76. PubMed ID: 31338116
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Enhancing Green Fraction Estimation in Rice and Wheat Crops: A Self-Supervised Deep Learning Semantic Segmentation Approach.
    Gao Y; Li Y; Jiang R; Zhan X; Lu H; Guo W; Yang W; Ding Y; Liu S
    Plant Phenomics; 2023; 5():0064. PubMed ID: 37469555
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Comprehensive dataset of annotated rice panicle image from Bangladesh.
    Rashid MRA; Hossain MS; Fahim MD; Islam MS; Tahzib-E-Alindo ; Prito RH; Sheikh MSA; Ali MS; Hasan M; Islam M
    Data Brief; 2023 Dec; 51():109772. PubMed ID: 38020434
    [TBL] [Abstract][Full Text] [Related]  

  • 16. P-TRAP: a Panicle TRAit Phenotyping tool.
    A L-Tam F; Adam H; Anjos Ad; Lorieux M; Larmande P; Ghesquière A; Jouannic S; Shahbazkia HR
    BMC Plant Biol; 2013 Aug; 13():122. PubMed ID: 23987653
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Panicle Ratio Network: streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in the field.
    Guo Z; Yang C; Yang W; Chen G; Jiang Z; Wang B; Zhang J
    J Exp Bot; 2022 Nov; 73(19):6575-6588. PubMed ID: 35776094
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Combining Image Analysis, Genome Wide Association Studies and Different Field Trials to Reveal Stable Genetic Regions Related to Panicle Architecture and the Number of Spikelets per Panicle in Rice.
    Rebolledo MC; Peña AL; Duitama J; Cruz DF; Dingkuhn M; Grenier C; Tohme J
    Front Plant Sci; 2016; 7():1384. PubMed ID: 27703460
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Study on TCM Tongue Image Segmentation Model Based on Convolutional Neural Network Fused with Superpixel.
    Zhang H; Jiang R; Yang T; Gao J; Wang Y; Zhang J
    Evid Based Complement Alternat Med; 2022; 2022():3943920. PubMed ID: 35300068
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Heterosis analysis and underlying molecular regulatory mechanism in a wide-compatible neo-tetraploid rice line with long panicles.
    Ghaleb MAA; Li C; Shahid MQ; Yu H; Liang J; Chen R; Wu J; Liu X
    BMC Plant Biol; 2020 Feb; 20(1):83. PubMed ID: 32085735
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.