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| Potassium content prediction model of citrus leaves in different phenological period |
| 1.Key Laboratory of Key Technology on Agricultural Machine and Equipment, South China Agricultural University, Guangzhou, Guangdong 510642, China; 2.Division of Citrus Machinery, China Agriculture Research System, Guangzhou, Guangdong 510642, China; 3.College of Engineering, South China Agricultural University, Guangzhou, Guangdong 510642, China; 4. Faculty of Engineering and Surveying, University of Southern Queensland, Toowoomba QLD4350, Australia |
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Abstract Based on reflectance spectra, the potassium (K) content prediction model was established to realize nondestructive testing of K content in citrus trees. Field experiments were conducted on 117 planted Luogang citrus trees in the Crab Village, and the data was collected on fresh and healthy citrus leaves in four dominant phenological periods. The hyperspectrometer ASD FieldSpec3 and the flame photometry were used to detect spectral reflectance data and Kcontents,respectively. A series of experiments were conducted to analyze the sensitive frequency band of Kcontents and the modeling regularity of prediction in different phonological periods. The results show that there is frequency drift of Kcontents relevant sensitive band in different phenological periods. Compared with MLR, SVR and PLS, better prediction results can be obtained based on Kcontents relevant sensitive frequency band. The R2 of 0.994 and the mean square error of 0.120 with mean relative error of 1.33% are obtained in SVR model on validation set, which illuminates that SVR can well predict Kcontents in whole growth periods based on reflectance spectra, regardless of frequency drift and the discrepant model performance.
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Received: 06 December 2012
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2013, Vol. 34 
