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    Título
    Yield prediction by machine learning from UAS‑based mulit‑sensor data fusion in soybean
    Autor
    Herrero-Huerta, Mónica
    Rodríguez-Gonzálvez, Pablo
    Rainey, Katy M.
    Facultad/Centro
    Escuela Superior y Tecnica de Ingenieros de Minas
    Área de conocimiento
    Ingeniería Cartografica, Geodesica y Fotogrametria
    Título de la revista
    Plant Methods
    Fecha
    2020-06-01
    ISSN
    1746-4811
    Abstract
    Nowadays, automated phenotyping of plants is essential for precise and cost-effective improvement in the efficiency of crop genetics. In recent years, machine learning (ML) techniques have shown great success in the classification and modelling of crop parameters. In this research, we consider the capability of ML to perform grain yield prediction in soybeans by combining data from different optical sensors via RF (Random Forest) and XGBoost (eXtreme Gradient Boosting). During the 2018 growing season, a panel of 382 soybean recombinant inbred lines were evaluated in a yield trial at the Agronomy Center for Research and Education (ACRE) in West Lafayette (Indiana, USA). Images were acquired by the Parrot Sequoia Multispectral Sensor and the S.O.D.A. compact digital camera on board a senseFly eBee UAS (Unnamed Aircraft System) solution at R4 and early R5 growth stages. Next, a standard photogrammetric pipeline was carried out by SfM (Structure from Motion). Multispectral imagery serves to analyse the spectral response of the soybean end-member in 2D. In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season.
    Materia
    Ingenierías
    Palabras clave
    Unmanned aircraft system (UAS)
    High throughput phenotyping,
    Soybean
    Structure from Motion (SfM)
    Machine learning (ML)
    Yield
    Point clouds
    Peer review
    SI
    URI
    http://hdl.handle.net/10612/14771
    DOI
    https://doi.org/10.1186/s13007-020-00620-6
    Editorial
    BioMed Central Ltd.
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