Title :
Classification of defects in rice kernels by using image processing techniques
Author :
Chandra, Jayanta K. ; Barman, Anjan ; Ghosh, A.
Author_Institution :
Dept. of Electr. Eng., Future Inst. of Eng. & Manage., Kolkata, India
Abstract :
In this paper a machine vision based, efficient method is proposed that classifies various types of defects on rice kernels. The methods used for extraction of features representing defects in rice kernels are based on image processing techniques. The proposed method has been tested on different types of defects on rice sample, plenty in numbers. A satisfactory success rate is obtained which validates the proposed method.
Keywords :
agriculture; feature extraction; image classification; defect classification; feature extraction; image processing techniques; machine vision; rice kernels; Feature extraction; Inspection; Kernel; Machine vision; Shape; Training; GLCM; defects in rice kernels; kNN; shape number; signatures; statistical parameters;
Conference_Titel :
Automation, Control, Energy and Systems (ACES), 2014 First International Conference on
Conference_Location :
Hooghy
Print_ISBN :
978-1-4799-3893-3
DOI :
10.1109/ACES.2014.6807991