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| Rapid detection of chlorophyll content in corn leaves by using least squares-support vector machines and hyperspectral images |
| College of Engineering, China Agricultural University, Beijing 100083, China) |
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Abstract For the rapid and non-destructive detection of chlorophyll content in corn leaves, representative corn leaves with different N levels were collected. 60 calibration samples and 16 validation samples were prepared. Hyperspectral images in the range of 400～1 100 nm were collected and relevant chlorophyll content was measured according to the National Standard. Standard normalized variation, 13 points smoothing, and first derivative were applied as pretreatment method. According to the correlation coefficient, the wave band of 470～760 nm was selected as analysis object. Least squaressupport vector machines were used to establish the model between the corn leaves′ chlorophyll content and the hyperspectral data.The optimal parameters of LS-SVM were obtained by application of gridsearch based on crossvalidation. The results of LS-SVM model indicate technical support for hyperspectral application in remote sensing, with correlation coefficient of 096 and calibration coefficient of 093, respectively.
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2011, Vol. 32 
