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  • Open Access

    ARTICLE

    Modeling and Estimating Soybean Leaf Area Index and Biomass Using Machine Learning Based on Unmanned Aerial Vehicle-Captured Multispectral Images

    Sadia Alam Shammi1,2, Yanbo Huang1,*, Weiwei Xie1,2, Gary Feng1, Haile Tewolde1, Xin Zhang3, Johnie Jenkins1, Mark Shankle4

    Phyton-International Journal of Experimental Botany, Vol.94, No.9, pp. 2745-2766, 2025, DOI:10.32604/phyton.2025.068955 - 30 September 2025

    Abstract Crop leaf area index (LAI) and biomass are two major biophysical parameters to measure crop growth and health condition. Measuring LAI and biomass in field experiments is a destructive method. Therefore, we focused on the application of unmanned aerial vehicles (UAVs) in agriculture, which is a cost and labor-efficient method. Hence, UAV-captured multispectral images were applied to monitor crop growth, identify plant bio-physical conditions, and so on. In this study, we monitored soybean crops using UAV and field experiments. This experiment was conducted at the MAFES (Mississippi Agricultural and Forestry Experiment Station) Pontotoc Ridge-Flatwoods Branch… More >

  • Open Access

    ARTICLE

    Spatial-Resolution Independent Object Detection Framework for Aerial Imagery

    Sidharth Samanta1, Mrutyunjaya Panda1, Somula Ramasubbareddy2, S. Sankar3, Daniel Burgos4,*

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1937-1948, 2021, DOI:10.32604/cmc.2021.014406 - 13 April 2021

    Abstract Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) More >

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