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Journal Abstract Search

519 related items for PubMed ID: 32549903

  • 1. Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz).
    Selvaraj MG, Valderrama M, Guzman D, Valencia M, Ruiz H, Acharjee A.
    Plant Methods; 2020; 16():87. PubMed ID: 32549903
    [Abstract] [Full Text] [Related]

  • 2. 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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  • 3. 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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  • 4. 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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  • 7. Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system.
    Lu N, Zhou J, Han Z, Li D, Cao Q, Yao X, Tian Y, Zhu Y, Cao W, Cheng T.
    Plant Methods; 2019 Jan 13; 15():17. PubMed ID: 30828356
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  • 9. Precision estimation of winter wheat crop height and above-ground biomass using unmanned aerial vehicle imagery and oblique photoghraphy point cloud data.
    Li Y, Li C, Cheng Q, Chen L, Li Z, Zhai W, Mao B, Chen Z.
    Front Plant Sci; 2024 Jan 13; 15():1437350. PubMed ID: 39359624
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  • 10. Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features.
    Sun X, Yang Z, Su P, Wei K, Wang Z, Yang C, Wang C, Qin M, Xiao L, Yang W, Zhang M, Song X, Feng M.
    Front Plant Sci; 2023 Jan 13; 14():1158837. PubMed ID: 37063231
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  • 11. Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm.
    Pan W, Wang X, Sun Y, Wang J, Li Y, Li S.
    Plant Methods; 2023 Jan 23; 19(1):7. PubMed ID: 36691062
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  • 12. UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping.
    Nguyen C, Sagan V, Bhadra S, Moose S.
    Sensors (Basel); 2023 Feb 06; 23(4):. PubMed ID: 36850425
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  • 15. Ground penetrating radar: a case study for estimating root bulking rate in cassava (Manihot esculenta Crantz).
    Delgado A, Hays DB, Bruton RK, Ceballos H, Novo A, Boi E, Selvaraj MG.
    Plant Methods; 2017 Feb 06; 13():65. PubMed ID: 28794795
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  • 16. Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat.
    Yang M, Hassan MA, Xu K, Zheng C, Rasheed A, Zhang Y, Jin X, Xia X, Xiao Y, He Z.
    Front Plant Sci; 2020 Feb 06; 11():927. PubMed ID: 32676089
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  • 19. High-Throughput Phenotyping of Bioethanol Potential in Cereals Using UAV-Based Multi-Spectral Imagery.
    Ostos-Garrido FJ, de Castro AI, Torres-Sánchez J, Pistón F, Peña JM.
    Front Plant Sci; 2019 Feb 06; 10():948. PubMed ID: 31396251
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  • 20. Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data.
    Fei S, Hassan MA, Ma Y, Shu M, Cheng Q, Li Z, Chen Z, Xiao Y.
    Front Plant Sci; 2021 Feb 06; 12():730181. PubMed ID: 34987529
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