Hyperspectral estimation model of total phosphorus content for citrus leaves

Article


Huang, Shuangping, Hong, Tiansheng, Yue, Xuejun, Wu, Weibin, Cai, Kun and Li, Yunyu. 2013. "Hyperspectral estimation model of total phosphorus content for citrus leaves ." Transactions of the Chinese Society for Agricultural Machinery. 44 (4), pp. 202-207+195. https://doi.org/10.6041/j.issn.1000-1298.2013.04.035
Article Title

Hyperspectral estimation model of total phosphorus content for citrus leaves

Article CategoryArticle
AuthorsHuang, Shuangping (Author), Hong, Tiansheng (Author), Yue, Xuejun (Author), Wu, Weibin (Author), Cai, Kun (Author) and Li, Yunyu (Author)
Journal TitleTransactions of the Chinese Society for Agricultural Machinery
Journal Citation44 (4), pp. 202-207+195
Number of Pages6
Year2013
Place of PublicationBeijing, China
Digital Object Identifier (DOI)https://doi.org/10.6041/j.issn.1000-1298.2013.04.035
Web Address (URL)http://www.j-csam.org/ch/reader/view_abstract.aspx?file_no=20130435
Abstract

Field experiments were conducted on 117 planted Luogang citrus trees in the crab village of Guangzhou. 234 pairs of data sample were collected in two different development stages, respectively, germination period and fruit picking period. Hyperspectral reflection data was used as high-dimensional vector description. Phosphorus content measured by chemical method as true label and to predict the phosphorus content of citrus leaves. Two mainstream multivariate regression analysis algorithms, partial least squares and support vector regression, were used for modeling and prediction after various preprocessing on spectral reflectance data. Calibration and validation sets were used to evaluate the predictive performance of model. Two regression analysis methods respectively achieved coefficient of determination of 0.905 and 0.881, the MSE of 0.005 and 0.004, the mean relative error of 0.0264 and 0.0312, respectively. The experimental results showed that it is an effective way to predict phosphorus level based on hyperspectral reflection data.

Keywordscitrus leaves; content of phosphorus; hyperspectral; partial least squares; support vector regression; China
ANZSRC Field of Research 2020300802. Horticultural crop growth and development
310803. Plant cell and molecular biology
409901. Agricultural engineering
Public Notes

Publisher copyright.

Byline AffiliationsSouth China Agricultural University, China
Faculty of Engineering and Surveying
Institution of OriginUniversity of Southern Queensland
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