Detection of Different Chickpea Varieties Using a Computer Vision System Based on Computational Intelligence

Document Type : Research Paper

Authors

Department of Biosystem Engineering, Faculty of Agriculture, University of Mohaghegh Ardabili, Ardabil, Iran

Abstract

Abstract
Chickpea is one of the legumes that have different nutritional uses. It is important to identify different chickpea varieties. For this reason, the aim of the present study was to develop a computer vision system to identify three similar chickpea varieties, namely, Adel, Arman and Azad using different artificial neural network hybrids including hybrid ANN- DE, hybrid ANN- CA, hybrid ANN-BA and hybrid ANN- ICA. In order to perform the segmentation, the image was first converted to YCbCr color space, and chickpeas were isolated from the background. Then, different properties were extracted in two color and texture areas based on gray surface co-occurrence matrix (GLCM). The YCBCR, YIQ, CMY, HSV and HSI color spaces were also extracted. Textural properties are extracted using the gray-area coherent matrix based on the position of the pixels of equal value. The multilayer perceptron artificial neural network divides the whole data into three categories data for training, the data for validation, and the third data for testing. Result showed that in all three classifications, the area under the curve for the correct class is lower than for the others, which means that the classifiers did not correctly identify the examples for this class and there is probably a high degree of sharing of selective affective properties between Adel, Arman and Azad classes. All classifiers have acceptable performance. Comparing the values of the classifier parameters, it can be seen that hybrid ANN-CA has higher values than the other classifiers, so it can be concluded that this classifier performs better than the other two classifiers. The results showed that the correct detection rates of hybrid ANN-CA, hybrid ANN-BA and hybrid ANN-ICA were 98.92, 99.46 and 92.92, respectively.
 

Keywords

References

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Agricultural Mechanization
Volume 5, Issue 1
March 2021
Pages 21-31

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History

  • Receive Date: 09 June 2021
  • Accept Date: 09 June 2021

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