Potassium content prediction model of citrus leaves in different phenological period
Article
Article Title | Potassium content prediction model of citrus leaves in different phenological period |
---|---|
Article Category | Article |
Authors | Huang, Shuangping (Author), Yue, Xuejun (Author), Hong, Tiansheng (Author), Wu, Weibin (Author) and Li, Yunyu (Author) |
Journal Title | Journal of Jiangsu University (Natural Science Edition) |
Journal Citation | 34 (5), pp. 529-535 |
Number of Pages | 7 |
Year | 2013 |
Place of Publication | Jiangsu, China |
Digital Object Identifier (DOI) | https://doi.org/10.3969/j.issn.1671-7775.2013.05.007 |
Web Address (URL) | http://d.wanfangdata.com.cn/Periodical/jslgdxxb201305007 |
Abstract | 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. |
Keywords | citrus leaves; hyperspectral; modeling and prediction; phonological period; potassium content |
ANZSRC Field of Research 2020 | 300802. Horticultural crop growth and development |
310112. Structural biology (incl. macromolecular modelling) | |
340101. Analytical spectrometry | |
Byline Affiliations | South China Agricultural University, China |
Faculty of Engineering and Surveying | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q21q6/potassium-content-prediction-model-of-citrus-leaves-in-different-phenological-period
Download files
1777
total views120
total downloads1
views this month1
downloads this month