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

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.

A machine learning system to identify progress level of dry rot disease in potato tuber based on digital thermal image processing

“…The method was used due to achieve better results with fewer data 31 . The accuracy of SVM classifier models were evaluated for classification of different products 44 , 45 . The correct classification rates of the used methods were considered as model accuracy to compare their ability in identifying the progress level of the disease.…”

Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios

“…RGB, L* a* b*, I1I2I3, NRGB, CrCgCb, and HSV. The detailed definition of the color spaces mentioned above can be found elsewhere [ 42 , 43 , 44 , 45 , 46 ]. In addition, the gray level was obtained from RGB [ 47 , 48 , 49 , 50 ].…”

“…From the aforementioned image channels, 15 color and texture features were extracted, including minimum, mean, maximum, standard deviation, coefficient of variation, median, mode, skewness, kurtosis, homogeneity, covariance, contrast, correlation, entropy, and energy [ 47 , 51 , 52 ]. The detailed equations of the aforementioned features can be found elsewhere [ 42 , 45 , 46 , 47 , 51 , 52 ]. Overall, 15 × 19 = 285 features were extracted from each of the 160 samples.…”

“…The presence of redundant features could complicate the model development and data analysis. To this end, we used a quadratic sequential feature selection method (similar to [ 45 , 46 , 53 ] to identify and select optimum features for further analysis. The selected optimum features were used as inputs for the classification algorithms.…”

“…The performances of discriminant-based classifiers (i.e., LDA and QDA) were examined using CCR and MSE measures. Ultimately, the CCR measure was utilized to compare the performance of LDA, QDA, and the optimum ANN model [ 42 , 45 , 46 , 53 ].…”