Hyperspectral estimation model of total phosphorus content for citrus leaves

Hyperspectral estimation model of total phosphorus content for citrus leaves

Field experiments were conducted on 117 planted Luogang citrus trees in the crab village of Guangzhou. 234 pairs of data sample were collected in two different development stages, respectively, germination period and fruit picking period. Hyperspectral reflection data was used as high-dimensional vector description. Phosphorus content measured by chemical method as true label and to predict the phosphorus content of citrus leaves. Two mainstream multivariate regression analysis algorithms, partial least squares and support vector regression, were used for modeling and prediction after various preprocessing on spectral reflectance data. Calibration and validation sets were used to evaluate the predictive performance of model. Two regression analysis methods respectively achieved coefficient of determination of 0.905 and 0.881, the MSE of 0.005 and 0.004, the mean relative error of 0.0264 and 0.0312, respectively. The experimental results showed that it is an effective way to predict phosphorus level based on hyperspectral reflection data.

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