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Discriminating Healthy Wheat Grains from Grains Infected with Fusarium graminearum Using Texture Characteristics of Image-Processing Technique, Discriminant Analysis, and Support Vector Machine Methods

Authors
  • Abbaspour-Gilandeh, Yousef1
  • Ghadakchi-Bazaz, Hamed2
  • Davari, Mahdi3
  • 1 Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Daneshgah Street , (Iran)
  • 2 University of Mohaghegh Ardabili, Iran , (Iran)
  • 3 College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Iran , (Iran)
Type
Published Article
Journal
Journal of Intelligent Systems
Publisher
De Gruyter
Publication Date
Aug 30, 2019
Volume
29
Issue
1
Pages
1576–1586
Identifiers
DOI: 10.1515/jisys-2018-0430
Source
De Gruyter
Keywords
License
Green

Abstract

Among agricultural plants, wheat, with valuable foodstuffs such as proteins, vitamins, and minerals, provides about 25% of the world’s food calories. Hence, providing its health conditions and quality is of great importance. One of the most important wheat diseases that causes a lot of damages to this product is Fusarium head blight (FHB). In most areas, the causal agent of disease is Fusarium graminearum. This disease not only decreases product quality and efficiency but also has harmful effects on humans and animals by mycotoxin production. FHB discrimination requires experimental work in special conditions and also experts, but these facilities may not be available at customs and other related grain health testing centers. In this study, discriminating healthy wheat grains and the grains infected with F. graminearum was performed with an image-processing technique, an accurate, rapid, and nondestructive method. First, healthy and infected wheat grains were selected, and then digital images of samples were prepared in randomized mass method using cameras and lightening chamber. Then using the image-processing technique, a total of 21 texture characteristics were obtained for each grain. Discrimination and classification of healthy and infected grains were done with 100% accuracy using extracted texture characteristics and two techniques mentioned above. The results of this research could be helpful in the development of automatic devices for rapid discrimination of healthy grains and grains infected with F. graminearum, one of the most destructive wheat diseases.

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