y, 11 groups of various corn varieties in the seedling
stage were selected, and 6 single leaves with representative
difference were selected from each group. Hyperspectral imaging
data of leaf samples were acquired by ground-based imaging spectrometer
PIS. Totally, there were 66 groups of measurement data
for each corn variety. For each leaf sample, spectral imaging information
of leaf base, leaf middle and leaf apex were acquired.
Therefore, there were a total of 198 specimens. Spectral extraction
was conducted from the original spectral images by remote sensing
image processing software EVNI 4.4 (the Environment for Visualizing
Images) (as shown in Fig. 2). Spectral values of leaf base,
leaf middle and leaf apex were respectively extracted and averaged,
and the original reflectance value was automatically
obtained. The band range of 400–1000 nm for full spectrum and
band range of 430–nm for experienced-spectrum region as well
as genetic algorithm were used to select the average spectral
reflectance of characteristic band range. Modes of single leaves in
the corn seedling stage were respectively established by use of
PLS and measured chlorophyll value.
Reflectance calculation method: Hyperspectral reflectance
value of corn seedling leaf was acquired, and it was converted into
spectral reflectance of target by used of synchronously determined
spectral reflectance of panel by formula (1).
Reftarget ¼ Radtarget= Radpanel Refpanel ð