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84 ta ish

Ish: A Novel Fusion Method for Soybean Yield Prediction Using Sentinel-2 and PlanetScope Imagery

  1. Random Forests

    Leo Breiman

    Maqola200167 iqtibos
    ABI

  2. A soil-adjusted vegetation index (SAVI)

    Alfredo Huete

    Maqola198823 iqtibos
    ABI

  3. Red and photographic infrared linear combinations for monitoring vegetation

    Compton J. Tucker

    Maqola197919 iqtibos
    ABI

  4. Use of a green channel in remote sensing of global vegetation from EOS-MODIS

    Anatoly A. Gitelson, Yoram J. Kaufman, Mark N. Merzlyak

    Maqola199610 iqtibos
    ABI

  5. Big Data in Smart Farming – A review

    J. Wolfert, Lan Ge, C.N. Verdouw +1

    Sharh maqola20178 iqtibos
    ABI

  6. Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

    Jinru Xue, Baofeng Su

    Sharh maqola20177 iqtibos
    ABI

  7. Sarlavhasiz

    Boshqa7 iqtibos
    ABI

  8. Sarlavhasiz

    Boshqa5 iqtibos
    ABI

  9. A survey of deep neural network architectures and their applications

    Weibo Liu, Zidong Wang, Xiaohui Liu +3

    Maqola20165 iqtibos
    ABI

  10. Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

    Sudhanshu Panda, Daniel P. Ames, Suranjan Panigrahi

    Maqola20105 iqtibos
    ABI

  11. Support vector regression

    Fan Zhang, Lauren J. O’Donnell

    Bob20195 iqtibos
    ABI

  12. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans

    Thomas J. Jackson, Daoyi Chen, Michael H. Cosh +5

    Maqola20034 iqtibos
    ABI

  13. Importance of phenological observations and predictions in agriculture

    M. Ruml, Todor Vulić

    Maqola20054 iqtibos
    ABI

  14. Combining PlanetScope and Sentinel-2 images with environmental data for improved wheat yield estimation

    Nizom Farmonov, Khilola Amankulova, József Szatmári +4

    MaqolaRemote Sensing in AgricultureInternational Journal of Digital Earth20233 iqtibos
    ABI

  15. Crop Yield Prediction Using Deep Neural Networks

    Saeed Khaki, Lizhi Wang

    Maqola20193 iqtibos
    ABI

  16. Time-series analysis of Sentinel-2 satellite images for sunflower yield estimation

    Khilola Amankulova, Nizom Farmonov, László Mucsi

    Maqola20223 iqtibos
    ABI

  17. A comparison of random forest regression and multiple linear regression for prediction in neuroscience

    Paul F. Smith, Siva Ganesh, Ping Liu

    Maqola20133 iqtibos
    ABI

  18. SEN2-AGRI – CROP TYPE MAPPING PILOT STUDY USING SENTINEL-2 SATELLITE IMAGERY IN INDIA

    D. Vijayasekaran

    Maqola20193 iqtibos
    ABI

  19. Sarlavhasiz

    Boshqa3 iqtibos
    ABI

  20. Spatial patterns of seed dispersal, their determinants and consequences for recruitment

    Ran Nathan, Helene C. Muller‐Landau

    Maqola20002 iqtibos
    ABI

  21. Use of Remote‐Sensing Imagery to Estimate Corn Grain Yield

    John F. Shanahan, James S. Schepers, D. D. Francis +5

    Maqola20012 iqtibos
    ABI

  22. Studying the Feasibility of Assimilating Sentinel-2 and PlanetScope Imagery into the SAFY Crop Model to Predict Within-Field Wheat Yield

    V. S. Manivasagam, Yuval Sadeh, Gregoriy Kaplan +2

    Maqola20212 iqtibos
    ABI

  23. Global estimation of crop productivity and the impacts of global warming by GIS and EPIC integration

    Guoxin Tan, Ryosuke Shibasaki

    Maqola20032 iqtibos
    ABI

  24. Post-processing methods to eliminate erroneous grain yield measurements: review and directions for future development

    Greg Lyle, Brett A. Bryan, Bertram Ostendorf

    Maqola20132 iqtibos
    ABI

  25. Soybean yield prediction from UAV using multimodal data fusion and deep learning

    Maitiniyazi Maimaitijiang, Vasit Sagan, Paheding Sidike +3

    Maqola20192 iqtibos
    ABI