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 *

203 related articles for article (PubMed ID: 34464811)

  • 1. Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea.
    Jeong S; Ko J; Yeom JM
    Sci Total Environ; 2022 Jan; 802():149726. PubMed ID: 34464811
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Monitoring paddy productivity in North Korea employing geostationary satellite images integrated with GRAMI-rice model.
    Yeom JM; Jeong S; Jeong G; Ng CT; Deo RC; Ko J
    Sci Rep; 2018 Oct; 8(1):16121. PubMed ID: 30382152
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting rice productivity for ground data-sparse regions: A transferable framework and its application to North Korea.
    Shi Y; Li L; Wu B; Zhang Y; Wang B; Niu W; He L; Jin N; Pan S; Tian H; Yu Q
    Sci Total Environ; 2024 Jun; 946():174227. PubMed ID: 38936710
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level.
    Jiang H; Hu H; Zhong R; Xu J; Xu J; Huang J; Wang S; Ying Y; Lin T
    Glob Chang Biol; 2020 Mar; 26(3):1754-1766. PubMed ID: 31789455
    [TBL] [Abstract][Full Text] [Related]  

  • 5. County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model.
    Sun J; Di L; Sun Z; Shen Y; Lai Z
    Sensors (Basel); 2019 Oct; 19(20):. PubMed ID: 31600963
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Geographical variations in gross primary production and evapotranspiration of paddy rice in the Korean Peninsula.
    Jeong S; Ko J; Kang M; Yeom J; Ng CT; Lee SH; Lee YG; Kim HY
    Sci Total Environ; 2020 Apr; 714():136632. PubMed ID: 31982739
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN.
    Gong L; Yu M; Jiang S; Cutsuridis V; Pearson S
    Sensors (Basel); 2021 Jul; 21(13):. PubMed ID: 34283083
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Rice leaf diseases prediction using deep neural networks with transfer learning.
    N K; Narasimha Prasad LV; Pavan Kumar CS; Subedi B; Abraha HB; V E S
    Environ Res; 2021 Jul; 198():111275. PubMed ID: 33989629
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China.
    Li Z; Ding L; Xu D
    Sci Total Environ; 2022 Apr; 815():152880. PubMed ID: 34998760
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth.
    Jeong S; Ko J; Shin T; Yeom JM
    Sci Rep; 2022 May; 12(1):9030. PubMed ID: 35637314
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Distillation of crop models to learn plant physiology theories using machine learning.
    Yamamoto K
    PLoS One; 2019; 14(5):e0217075. PubMed ID: 31141528
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning.
    Bellis ES; Hashem AA; Causey JL; Runkle BRK; Moreno-GarcĂ­a 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]  

  • 13. An ensemble deep learning approach for predicting cocoa yield.
    Olofintuyi SS; Olajubu EA; Olanike D
    Heliyon; 2023 Apr; 9(4):e15245. PubMed ID: 37089327
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Securing China's rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models.
    Mokhtar A; He H; Nabil M; Kouadri S; Salem A; Elbeltagi A
    Sci Rep; 2024 Jun; 14(1):14699. PubMed ID: 38926368
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia.
    Mohanasundaram S; Kasiviswanathan KS; Purnanjali C; Santikayasa IP; Singh S
    Int J Plant Prod; 2023; 17(1):1-16. PubMed ID: 36405847
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing.
    Morales G; Sheppard JW; Hegedus PB; Maxwell BD
    Sensors (Basel); 2023 Jan; 23(1):. PubMed ID: 36617083
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models.
    Jang J; Abbas A; Kim M; Shin J; Kim YM; Cho KH
    Water Res; 2021 May; 196():117001. PubMed ID: 33744657
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Is deeper always better? Evaluating deep learning models for yield forecasting with small data.
    Sabo F; Meroni M; Waldner F; Rembold F
    Environ Monit Assess; 2023 Sep; 195(10):1153. PubMed ID: 37672152
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Effects of wind speed and wind direction on crop yield forecasting using dynamic time warping and an ensembled learning model.
    Bediako-Kyeremeh B; Ma T; Rong H; Osibo BK; Mamelona L; Nti IK; Amoah L
    PeerJ; 2024; 12():e16538. PubMed ID: 38881862
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Deep learning-based prediction of plant height and crown area of vegetable crops using LiDAR point cloud.
    J R; Nidamanuri RR
    Sci Rep; 2024 Jun; 14(1):14903. PubMed ID: 38942825
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.