An Evaluation of Utilizing Geometric Features for Wheat Grain Classification using X-ray Images
PBN-AR
Instytucja
Instytut Badań Systemowych Polskiej Akademii Nauk
Informacje podstawowe
Główny język publikacji
angielski
Czasopismo
COMPUTERS AND ELECTRONICS IN AGRICULTURE (40pkt w roku publikacji)
ISSN
0168-1699
EISSN
Wydawca
ELSEVIER SCI LTD
DOI
URL
Rok publikacji
2018
Numer zeszytu
Strony od-do
260-268
Numer tomu
144
Identyfikator DOI
Liczba arkuszy
0,45
Słowa kluczowe
en
grain classification
principal component analysis
factor analysis
correlations
morphological features
image processing
X-ray imaging
object recognition
Streszczenia
Język
en
Treść
Nowadays, with the rapid development of digital image processing, there has been a notable increase in elaborating advanced tools for studying the internal structure of objects. This may be very helpful in characterizing certain morphological traits of grains, as well as in quantifying the differences between them. The current research was carried out to study the structure of the traits and to determine their importance in relation to grain classification and identification. To achieve better performance and deeper understanding of their usefulness, the investigation was done by means of both principal component analysis and multivariate factor analysis. Herein, the percentage of variation explained by the first three factors reached a high 89.97%. Thus, the presented methodology supported reliable discrimination of the wheat varieties as regards their shape descriptors. The conducted study confirmed the practical usefulness and effectiveness of the evolved method when applied to the many practical tasks wherein the image analysis commonly employed in multivariate statistical methods is recommended.
Inne
System-identifier
IBSPAN-A-2021
CrossrefMetadata from Crossref logo
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