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  • Prediction of soil macro- and micro-elements in sieved and ground air-dried soils using laboratory-based hyperspectral imaging technique

    Author(s)
    Malmir, Mohammad
    Tahmasbian, Iman
    Xu, Zhihong
    Farrar, Michael B
    Bai, Shahla Hosseini
    Griffith University Author(s)
    Xu, Zhihong
    Hosseini-Bai, Shahla
    Farrar, Michael
    Year published
    2019
    Metadata
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    Abstract
    Hyperspectral image analysis in laboratory-based settings has the potential to estimate soil elements. This study aimed to explore the effects of soil particle size on element estimation using visible-near infrared (400–1000 nm) hyperspectral imaging. Images were captured from 116 sieved and ground soil samples. Data acquired from hyperspectral images (HSI) were used to develop partial least square regression (PLSR) models to predict soil available aluminum (Al), boron (B), calcium (Ca), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), sodium (Na), phosphorus (P) and zinc (Zn). The soil available Al, ...
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    Hyperspectral image analysis in laboratory-based settings has the potential to estimate soil elements. This study aimed to explore the effects of soil particle size on element estimation using visible-near infrared (400–1000 nm) hyperspectral imaging. Images were captured from 116 sieved and ground soil samples. Data acquired from hyperspectral images (HSI) were used to develop partial least square regression (PLSR) models to predict soil available aluminum (Al), boron (B), calcium (Ca), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), sodium (Na), phosphorus (P) and zinc (Zn). The soil available Al, Fe, K, Mn, Na and P were not predicted with high precision. However, the developed PLSR models predicted B (R2CV = 0.62 and RMSECV = 0.15), Ca (R2CV = 0.81 and RMSECV = 260.97), Cu (R2CV = 0.74 and RMSECV = 0.27), Mg (R2CV = 0.80 and RMSECV = 43.71) and Zn (R2CV = 0.76 and RMSECV = 0.97) in sieved soils. The PLSR models using reflectance of ground soil were also developed for B (R2CV = 0.53 and RMSECV = 0.16), Ca (R2CV = 0.81 and RMSECV = 260.79), Cu (R2CV = 0.73 and RMSECV = 0.29), Mg (R2CV = 0.79 and RMSECV = 45.45) and Zn (R2CV = 0.76 and RMSECV = 0.97). RMSE of different PLSR models, developed from sieved and ground soils for the corresponding elements did not significantly differ based on the Levene's test. Therefore, this study indicated that it was not necessary to grind soil samples to predict elements using HSI.
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    Journal Title
    GEODERMA
    Volume
    340
    DOI
    https://doi.org/10.1016/j.geoderma.2018.12.049
    Subject
    Environmental sciences
    Biological sciences
    Agricultural, veterinary and food sciences
    Publication URI
    http://hdl.handle.net/10072/383108
    Collection
    • Journal articles

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