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Journal Abstract Search

275 related items for PubMed ID: 32102358

  • 61. Detecting Wheat Powdery Mildew and Predicting Grain Yield Using Unmanned Aerial Photography.
    Liu W, Cao X, Fan J, Wang Z, Yan Z, Luo Y, West JS, Xu X, Zhou Y.
    Plant Dis; 2018 Oct; 102(10):1981-1988. PubMed ID: 30125137
    [Abstract] [Full Text] [Related]

  • 62. [Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat].
    Wang J, Jing YS, Huang WJ, Zhang JC, Zhao J, Zhang Q, Wang L.
    Guang Pu Xue Yu Guang Pu Fen Xi; 2015 Jun; 35(6):1649-53. PubMed ID: 26601384
    [Abstract] [Full Text] [Related]

  • 63. Early Visual Detection of Wheat Stripe Rust Using Visible/Near-Infrared Hyperspectral Imaging.
    Yao Z, Lei Y, He D.
    Sensors (Basel); 2019 Feb 23; 19(4):. PubMed ID: 30813434
    [Abstract] [Full Text] [Related]

  • 64. Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation.
    Pan H, Chen Z, Allard W, Ren J.
    Sensors (Basel); 2019 Jul 18; 19(14):. PubMed ID: 31323829
    [Abstract] [Full Text] [Related]

  • 65. Estimation of soybean yield parameters under lodging conditions using RGB information from unmanned aerial vehicles.
    Bai D, Li D, Zhao C, Wang Z, Shao M, Guo B, Liu Y, Wang Q, Li J, Guo S, Wang R, Li YH, Qiu LJ, Jin X.
    Front Plant Sci; 2022 Jul 18; 13():1012293. PubMed ID: 36589058
    [Abstract] [Full Text] [Related]

  • 66. Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm.
    Tan C, Zhou X, Zhang P, Wang Z, Wang D, Guo W, Yun F.
    PLoS One; 2020 Jul 18; 15(3):e0228500. PubMed ID: 32160185
    [Abstract] [Full Text] [Related]

  • 67. Multi-Year Mapping of Major Crop Yields in an Irrigation District from High Spatial and Temporal Resolution Vegetation Index.
    Yu B, Shang S.
    Sensors (Basel); 2018 Nov 06; 18(11):. PubMed ID: 30404139
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  • 68. 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 06; 48(8):766-779. PubMed ID: 33663681
    [Abstract] [Full Text] [Related]

  • 69. [Exploring novel hyperspectral band and key index for leaf nitrogen accumulation in wheat].
    Yao X, Zhu Y, Feng W, Tian YC, Cao WX.
    Guang Pu Xue Yu Guang Pu Fen Xi; 2009 Aug 06; 29(8):2191-5. PubMed ID: 19839336
    [Abstract] [Full Text] [Related]

  • 70. Inversion of soil water and salt information based on UAV hyperspectral remote sensing and machine lear-ning.
    Wang YJ, Ding QD, Zhang JH, Chen R, Jia K, Li XL.
    Ying Yong Sheng Tai Xue Bao; 2023 Nov 06; 34(11):3045-3052. PubMed ID: 37997416
    [Abstract] [Full Text] [Related]

  • 71. Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content.
    Zhang J, Zhang W, Xiong S, Song Z, Tian W, Shi L, Ma X.
    Plant Methods; 2021 Mar 31; 17(1):34. PubMed ID: 33789711
    [Abstract] [Full Text] [Related]

  • 72. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform.
    Hassan MA, Yang M, Rasheed A, Yang G, Reynolds M, Xia X, Xiao Y, He Z.
    Plant Sci; 2019 May 31; 282():95-103. PubMed ID: 31003615
    [Abstract] [Full Text] [Related]

  • 73. Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region.
    Sun C, Bian Y, Zhou T, Pan J.
    Sensors (Basel); 2019 May 26; 19(10):. PubMed ID: 31130689
    [Abstract] [Full Text] [Related]

  • 74. Estimation of wheat tiller density using remote sensing data and machine learning methods.
    Hu J, Zhang B, Peng D, Yu R, Liu Y, Xiao C, Li C, Dong T, Fang M, Ye H, Huang W, Lin B, Wang M, Cheng E, Yang S.
    Front Plant Sci; 2022 May 26; 13():1075856. PubMed ID: 36618628
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  • 75. UAV-Assisted Dynamic Monitoring of Wheat Uniformity toward Yield and Biomass Estimation.
    Yang Y, Li Q, Mu Y, Li H, Wang H, Ninomiya S, Jiang D.
    Plant Phenomics; 2024 May 26; 6():0191. PubMed ID: 38895609
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  • 76. Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta.
    Qi G, Zhao G, Xi X.
    Sensors (Basel); 2020 Nov 14; 20(22):. PubMed ID: 33202692
    [Abstract] [Full Text] [Related]

  • 77. UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.
    Wang Z, Ding J, Tan J, Liu J, Zhang T, Cai W, Meng S.
    Front Plant Sci; 2024 Nov 14; 15():1358965. PubMed ID: 38439983
    [Abstract] [Full Text] [Related]

  • 78. Canopy hyperspectral characteristics and yield estimation of winter wheat (Triticum aestivum) under low temperature injury.
    Xie Y, Wang C, Yang W, Feng M, Qiao X, Song J.
    Sci Rep; 2020 Jan 14; 10(1):244. PubMed ID: 31937859
    [Abstract] [Full Text] [Related]

  • 79. Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery.
    Lu N, Wang W, Zhang Q, Li D, Yao X, Tian Y, Zhu Y, Cao W, Baret F, Liu S, Cheng T.
    Front Plant Sci; 2019 Jan 14; 10():1601. PubMed ID: 31921250
    [Abstract] [Full Text] [Related]

  • 80. Remote sensing techniques and stable isotopes as phenotyping tools to assess wheat yield performance: Effects of growing temperature and vernalization.
    Rezzouk FZ, Gracia-Romero A, Kefauver SC, Gutiérrez NA, Aranjuelo I, Serret MD, Araus JL.
    Plant Sci; 2020 Jun 14; 295():110281. PubMed ID: 32534622
    [Abstract] [Full Text] [Related]

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