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The ability of a data fusion system composed of a computer vision system (CVS) and an electronic nose (e-nose) was evaluated to predict key physiochemical attributes and distinguish red-fleshed kiwifruit produced in three distinct regions in northern Iran. Color and morphological features from whole and middle-cut kiwifruits, along with the maximum responses of the 13 metal oxide semiconductor (MOS) sensors of an e-nose system, were used as inputs to the data fusion system. Principal component analysis (PCA) revealed that the first two principal components (PCs) extracted from the e-nose features could effectively differentiate kiwifruit samples from different regions. The PCA-SVM algorithm achieved a 93.33% classification rate for kiwifruits from three regions based on data from individual e-nose and CVS. Data fusion increased the classification rate of the SVM model to 100% and improved the performance of Support Vector Regression (SVR) for predicting physiochemical indices of kiwifruits compared to individual systems. The data fusion-based PCA-SVR models achieved validation R2 values ranging from 90.17% for the Brix-Acid Ratio (BAR) to 98.57% for pH prediction. These results demonstrate the high potential of fusing artificial visual and olfactory systems for quality monitoring and identifying the geographical growing regions of kiwifruits. [ABSTRACT FROM AUTHOR]

Saffron classification based on machine vision techniques as well as the expert's opinion is an objective and nondestructive method that can increase the accuracy of this process in real applications. The experts in Iran classify saffron into three classes Pushal, Negin, and Sargol based on apparent characteristics. Four hundred and forty color images from saffron for the three different classes were acquired, using a mobile phone camera. Twenty‐one color features and 99 textural features were extracted using image analysis. Twenty‐two classifiers were employed for classification using mentioned features. The support vector machine and Ensemble classifiers were better than other classifiers. Our results showed that the mean classification accuracy was up to 83.9% using the Quadratic support vector machine and Subspace Discriminant classifier. [ABSTRACT FROM AUTHOR]

• Classification of wheat varieties using image processing. • Extraction of texture feature using GLCM, GLRM and LBP algorithms. • The unsupervised classification results confirmed by supervised methods. • The features extracted from the GLRM algorithm had the best results by SVM model. Wheat is a major crop in Iran and plays an essential role in satisfying human nutritional needs. Depending on the geographical location as well as the climatic conditions of each region, different varieties of this crop can be cultivated. The purity of the seeds used for field cultivation has a substantial impact on both field efficiency and flour quality as the final product. To increase the seed supplied for cultivation in seed supply centers, the use of new non-destructive automatic methods for this purpose is indispensable. In this study, 7 varieties of wheat in the East Azerbaijan Province were investigated by employing a machine vision system. By extracting texture features using Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Pattern (LBP) algorithms and classifying using principal component analysis (PCA) as the unsupervised method, along with the Support Vector Machine (SVM) and Artificial Neural Network (ANN) as supervised methods, the system accuracy was surveyed. Results indicated that modeling using a combination of all extracted features (125 features) yielded the best results by the ANN model, which the Correct Classification Rate (CCR) was obtained 100% and 95.04% for training and testing datasets, respectively. The testing dataset CCR was improved using 20 selected features by the chi-square test feature selection method up to 98.10%, which was better than the modeling results with all extracted features. Results also revealed that by using image processing and texture features extraction methods, it was possible to identify the classification of different wheat varieties with over 95% accuracy. [ABSTRACT FROM AUTHOR]