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  • Title: [Analysis and estimation of the phosphorus content in cucumber leaf in greenhouse by spectroscopy].
    Author: Zhang XJ, Li MZ.
    Journal: Guang Pu Xue Yu Guang Pu Fen Xi; 2008 Oct; 28(10):2404-8. PubMed ID: 19123417.
    Abstract:
    A handheld spectroradiometer was used to measure the spectral reflectance of the crop with the measurable range from 325 nm to 1075 nm. Since the first derivative of the spectra can well eliminate spectral error, it was calculated for each spectrum. The cucumber leaves were also sampled and the phosphorus content was measured for each sample with chemical method. First, the correlation between the phosphorus content of the cucumber leaf and the spectral reflectance was analyzed but high coefficient was not obtained. It was shown that there is not high linear relation between those. Then, the analysis was conducted between the phosphorus content of the cucumber leaf and the first derivative of spectrum for each sample. The coefficients were improved. However, it was not high enough to establish an estimation model. It shows that non-linear model is needed to estimate the phosphorus content of the crop leaf based on spectral reflectance. Artificial neural network (ANN) and support vector machine (SVM), the modern ealgorithm for modeling and estimating, were used to establish the nonlinear models. From stepwise multi-regression, four wavelengths, 978, 920, 737 and 458 nm, were selected as modeling wavebands. For the Artificial Neural Network (ANN) model, the data of spectral reflectance in the four wavebands were taken as the input and the phosphorus content was taken as the output. And the number of the neurons in the middle layer, the learning rate, and the learning error were set as 25, 0.05, and 0.001, respectively. The calibration accuracy of the model was 0.995, and the validation accuracy reached to 0.712. For the Support Vector Machine (SVM) model, the selected kernel function was anova, and the penalty parameter C and the linear epsilon-insensitive loss function were set as 100 and 0.00001, respectively. The calibration accuracy of the model was closed to 1, and the validation accuracy reached to 0.754. It can be concluded that both nonlinear models are practical.
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