Abstract:
The aim of this study was to develop and validate q
ualitative and quantitative models
to discriminate different types of maize and also e
stimate biochemical constituents.
Spectral data were taken from the central leaf of r
andomly-chosen plants grown in field
trials in 2011 and 2012. Leaf chlorophyll and prote
in content and stalk protein content
were determined in the same plants. Four different
Support Vector Machine (SVM)
models were generated and validated in this study.
In qualitative models, maize type was
designated as dependent variable while Full Spectra
l (FS) data (400-1,000 nm) and
Spectral Indices (SI) data (34 indices/bands) were
independent variables. In the two
quantitative models (SVMR-FS and SVMR-SI), independ
ent variables were the same,
whereas dependent variables were assigned as the qu
antitatively measured traits. Results
showed the qualitative models to be a robust method
of classification for distinguishing
different maize types, such as High Oil Maize (HOM)
, High Protein Maize (HPM) and
standard (NORMAL) maize genotypes. The SVMC-FS mode
l was superior to SVMC-SI
in terms of the genotypic classification of maize p
lants. Quantitative models with full
spectral data gave more robust prediction than the
others. The best prediction result
(RMSEC= 222.4 μg g
-1
, R
2
for Cal= 0.739, SEP= 213.3 μg g
-1
; RPD= 2.04 and r= 0.877)
was obtained from the SVMR-FS model developed for c
hlorophyll content. Indirect
estimation models, based on relationships between l
eaf-based spectral measurements and
leaf and stalk protein content, were less satisfact
ory.
Keywords
: Genotypic classification, Support Vector Machine,
Zea mays.
