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http://dx.doi.org/10.7780/kjrs.2018.34.4.10

Selecting Significant Wavelengths to Predict Chlorophyll Content of Grafted Cucumber Seedlings Using Hyperspectral Images  

Jang, Sung Hyuk (Institute for Agricultural Machinery & ICT Convergence, Chonbuk National University)
Hwang, Yong Kee (Institute for Agricultural Machinery & ICT Convergence, Chonbuk National University)
Lee, Ho Jun (Department of Agricultural Machinery Engineering, Graduate School, Chonbuk National University)
Lee, Jae Su (Department of Agricultural Machinery Engineering, Graduate School, Chonbuk National University)
Kim, Yong Hyeon (Institute for Agricultural Machinery & ICT Convergence, Chonbuk National University)
Publication Information
Korean Journal of Remote Sensing / v.34, no.4, 2018 , pp. 681-692 More about this Journal
Abstract
This study was performed to select the significant wavelengths for predicting the chlorophyll content of grafted cucumber seedlings using hyperspectral images. The visible and near-infrared (VNIR) images and the short-wave infrared images of cucumber cotyledon samples were measured by two hyperspectral cameras. A correlation coefficient spectrum (CCS), a stepwise multiple linear regression (SMLR), and partial least squares (PLS) regression were used to determine significant wavelengths. Some wavelengths at 501, 505, 510, 543, 548, 619, 718, 723, and 727 nm were selected by CCS, SMLR, and PLS as significant wavelengths for estimating chlorophyll content. The results from the calibration models built by SMLR and PLS showed fair relationship between measured and predicted chlorophyll concentration. It was concluded that the hyperspectral imaging technique in the VNIR region is suggested effective for estimating the chlorophyll content of grafted cucumber leaves, non-destructively.
Keywords
Chlorophyll content; Grafted cucumber seedlings; Hyperspectral image; Preprocessing; Spectral reflectance; Statistical models;
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