RESEARCH PAPER
Hyperspectral imaging coupled with multivariate analysis and artificial intelligence to the classification of maize kernels
 
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1
Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
2
Institute of Sciences and Technologies for Sustainable Energy and Mobility, National Research Council (STEMS-CNR), Via Canal Bianco 28, 44124 Ferrara, Italy
3
Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Kraków, Poland
4
Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
5
Faculty of Agriculture, University of South Bohemia in České Budějovice, Studentská 1668, 370 05 České Budějovice, Czech Republic
6
Faculty of Engineering, Slovak University of Agriculture in Nitra, Hlinku 2, 949 76 Nitra, Slovakia
CORRESPONDING AUTHOR
Marek Gancarz   

m.gancarz@ipan.lublin.pl
Final revision date: 2022-02-13
Acceptance date: 2022-03-08
Publication date: 2022-04-07
 
Int. Agrophys. 2022, 36(2): 83–91
 
HIGHLIGHTS
  • • Hyperspectral imaging system can quickly classify maize cultivars using LDA methods.
  • • Length and width a single kernel did not affect the cultivars classification significantly.
  • • Discrimination accuracy decreases with the lower number of predictor variables.
  • • Non-destructive hyperspectral imaging can used with the weight and dimensions measurement.
KEYWORDS
TOPICS
ABSTRACT
Maize (Zea mays) is one of the key crops in the world, taking third place after wheat and rice in terms of cultivated area. This study aimed to demonstrate the potential of non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm to discriminate between and classify maize kernels in three cultivars by using non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm. Three cultivars of maize kernels were exposed to hyperspectral imaging with 20 replications. Predictor variables included 28 intensities of reflection wave for spectral imaging and 4 variables in terms of the weight, length, width, and thickness of a single kernel. The classification was successfully performed through Linear Discriminant Analysis and Artificial Neural Network methods, taking into account 32, 15, and 5 predictor variables. According to the results, Linear Discriminant Analysis with 32 predictor variables is characterized by a high degree of accuracy (95%). The most important predictor variables included the reflection wave intensity of the third peak, the wavelength intensity of 490 nm, the wavelength intensity of 580 nm, and the weight and thickness of a single kernel.
CONFLICT OF INTEREST
The authors declare that there is no conflict of interest regarding the publication of this paper.
 
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