Detection of Unripe Kernels and Foreign Materials in Chickpea Mixtures Using Image Processing

Somayeh Salam, Kamran Kheiralipour and Fuji Jian
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Somayeh Salam: Mechanical Engineering of Biosystems Department, Ilam University, Ilam 69315-516, Iran
Fuji Jian: Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada

Agriculture, 2022, vol. 12, issue 7, 1-10

Abstract: The existence of dockage, unripe kernels, and foreign materials in chickpea mixtures is one of the main concerns during chickpea storage and marketing. Novel algorithms based on image processing were developed to detect undesirable, foreign materials, and matured chickpea kernels in the chickpea mixture. Images of 270 objects including 54 sound samples and 36 samples of each undesired object were prepared and features of these acquired images were extracted. Different models based on linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural networks (ANN) methods were developed by using MATLAB. Three classification algorithms based on LDA, SVM, and ANN methods were developed. The classification accuracy in training, testing, and overall detection showed the superiority of ANN (99.4, 92.6, and 94.4%, respectively) and LDA (91.1, 94.0, and 91.9%, respectively) over the SVM (100, 53.7, and 88.5%, respectively). The developed image processing technique can be incorporated with a vision-based real-time system.

Keywords: chickpea; impurity; separation; classification; image processing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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