Potassium content prediction model of citrus leaves in different phenological period

Article


Huang, Shuangping, Yue, Xuejun, Hong, Tiansheng, Wu, Weibin and Li, Yunyu. 2013. "Potassium content prediction model of citrus leaves in different phenological period ." Journal of Jiangsu University (Natural Science Edition) . 34 (5), pp. 529-535. https://doi.org/10.3969/j.issn.1671-7775.2013.05.007
Article Title

Potassium content prediction model of citrus leaves in different phenological period

Article CategoryArticle
AuthorsHuang, Shuangping (Author), Yue, Xuejun (Author), Hong, Tiansheng (Author), Wu, Weibin (Author) and Li, Yunyu (Author)
Journal TitleJournal of Jiangsu University (Natural Science Edition)
Journal Citation34 (5), pp. 529-535
Number of Pages7
Year2013
Place of PublicationJiangsu, 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.

Keywordscitrus leaves; hyperspectral; modeling and prediction; phonological period; potassium content
ANZSRC Field of Research 2020300802. Horticultural crop growth and development
310112. Structural biology (incl. macromolecular modelling)
340101. Analytical spectrometry
Byline AffiliationsSouth China Agricultural University, China
Faculty of Engineering and Surveying
Institution of OriginUniversity of Southern Queensland
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