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Journal Abstract Search

196 related items for PubMed ID: 30811435

  • 1. Multispectral imaging and unmanned aerial systems for cotton plant phenotyping.
    Xu R, Li C, Paterson AH.
    PLoS One; 2019; 14(2):e0205083. PubMed ID: 30811435
    [Abstract] [Full Text] [Related]

  • 2. Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery.
    Gao W, Zeng W, Li S, Zhang L, Wang W, Song J, Wu H.
    PLoS One; 2024; 19(6):e0300056. PubMed ID: 38905187
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  • 6. Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.).
    Quirós Vargas JJ, Zhang C, Smitchger JA, McGee RJ, Sankaran S.
    Sensors (Basel); 2019 Apr 30; 19(9):. PubMed ID: 31052251
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  • 7. Spatio-temporal monitoring of cotton cultivation using ground-based and airborne multispectral sensors in GIS environment.
    Papadopoulos A, Kalivas D, Theocharopoulos S.
    Environ Monit Assess; 2017 Jul 30; 189(7):323. PubMed ID: 28593563
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  • 8. Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning.
    Dhakal R, Maimaitijiang M, Chang J, Caffe M.
    Sensors (Basel); 2023 Dec 08; 23(24):. PubMed ID: 38139554
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  • 9. Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging.
    Zhang D, Zhou X, Zhang J, Lan Y, Xu C, Liang D.
    PLoS One; 2018 Dec 08; 13(5):e0187470. PubMed ID: 29746473
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  • 11. Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices.
    Zhang Y, Han W, Niu X, Li G.
    Sensors (Basel); 2019 Nov 29; 19(23):. PubMed ID: 31795309
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  • 13. Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography.
    Wu J, Wen S, Lan Y, Yin X, Zhang J, Ge Y.
    Plant Methods; 2022 Dec 08; 18(1):129. PubMed ID: 36482426
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  • 14. Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS.
    Yuan W, Li J, Bhatta M, Shi Y, Baenziger PS, Ge Y.
    Sensors (Basel); 2018 Nov 02; 18(11):. PubMed ID: 30400154
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  • 16. A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles.
    Wan L, Zhu J, Du X, Zhang J, Han X, Zhou W, Li X, Liu J, Liang F, He Y, Cen H.
    J Exp Bot; 2021 Jun 22; 72(13):4691-4707. PubMed ID: 33963382
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  • 17. Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV.
    Duan T, Zheng B, Guo W, Ninomiya S, Guo Y, Chapman SC.
    Funct Plant Biol; 2016 Feb 22; 44(1):169-183. PubMed ID: 32480555
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  • 18. [Estimation of dry matter accumulation in above-ground part of cotton by means of canopy reflectance spectra].
    Zhu Y, Wu HB, Tian YC, Yao X, Zhou ZG, Cao WX.
    Ying Yong Sheng Tai Xue Bao; 2008 Jan 22; 19(1):105-9. PubMed ID: 18419080
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  • 19. Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning.
    Sharma P, Leigh L, Chang J, Maimaitijiang M, Caffé M.
    Sensors (Basel); 2022 Jan 13; 22(2):. PubMed ID: 35062559
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  • 20. Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning.
    Zhang C, Lane B, Fernández-Campos M, Cruz-Sancan A, Lee DY, Gongora-Canul C, Ross TJ, Da Silva CR, Telenko DEP, Goodwin SB, Scofield SR, Oh S, Jung J, Cruz CD.
    Front Plant Sci; 2022 Jan 13; 13():1077403. PubMed ID: 36756236
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