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[Hyperspectral Remote Sensing Estimation Models for Pasture Quality].

Wei-wei Ma, Cai-lan Gong, Yong Hu

    Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
    |February 25, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a non-destructive method using hyperspectral data to estimate pasture quality. It accurately predicts crude protein, fat, and fiber content, offering a faster, eco-friendly alternative for animal husbandry.

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    Area of Science:

    • Agricultural Science
    • Remote Sensing
    • Spectroscopy

    Background:

    • Accurate pasture quality assessment is vital for animal husbandry.
    • Current methods for measuring crude protein (CP), crude fat (CFA), and crude fiber (CFI) are costly, time-consuming, and generate waste.
    • A need exists for real-time, non-destructive pasture quality evaluation techniques.

    Purpose of the Study:

    • To develop and validate a real-time, non-destructive method for estimating pasture quality using hyperspectral data.
    • To correlate pasture quality parameters (CP, CFA, CFI) with spectral data, including first derivatives of reflectance (FDR) and wavelet coefficients (WCs).
    • To establish predictive models for pasture nutritional content.

    Main Methods:

    • Field hyperspectral data (350-2500 nm) collected from 19 pasture types.
    • Laboratory analysis of pasture samples for CP, CFA, and CFI concentrations.
    • Analysis of spectral data, FDR, band ratios, and WCs to establish relationships with pasture quality parameters.
    • Development and validation of linear, exponential, and polynomial regression models.

    Main Results:

    • Pasture quality parameters showed strong correlations with FDR and specific WCs (Morlet, Coiflets, Gassian).
    • Optimal predictive models identified: polynomial (Coiflets WCs), polynomial (FDR), and exponential (FDR) for CP, CFA, and CFI, respectively.
    • Model validation yielded R² values between 0.646 and 0.762 (p < 0.01).

    Conclusions:

    • Hyperspectral data, particularly FDR and WCs, can effectively estimate pasture quality.
    • The developed models provide a basis for real-time pasture quality prediction.
    • This approach offers a significant advancement over traditional, destructive methods in animal husbandry.