Potassium content prediction model of citrus leaves in different phenological period

Potassium content prediction model of citrus leaves in different phenological period

Based on reflectance spectra, the potassium (K) content prediction model was established to realize non-destructive 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 hyper-spectrometer ASD FieldSpec3 and the flame photometry were used to detect spectral reflectance data and K-contents, respectively. A series of experiments were conducted to analyze the sensitive frequency band of K-contents and the modeling regularity of prediction in different phonological periods. The results show that there is frequency drift of K-contents relevant sensitive band in different phenological periods. Compared with MLR, SVR and PLS, better prediction results can be obtained based on K-contents 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 K-contents in whole growth periods based on reflectance spectra, regardless of frequency drift and the discrepant model performance.