Date Grading using Machine Learning Techniques on a Novel Dataset

Abstract

Dates grading is a crucial stage in the dates' factories. However, it is done manually in most of the Middle Eastern industries. This study, using a novel dataset, identifies the suitable machine learning techniques to grade dates based on the image of the date. The dataset consists of three different types of dates, namely, Ajwah, Mabroom, and Sukkary with each having three different grades. The dates were obtained from Manafez company and graded by their experts. The color, size and texture of the dates are the features that have been considered in this work. To determine the color, we have used color properties in RGB (red, green, and blue) color space. For measuring the size, we applied the best least-square fitting ellipse. To analyze the texture, we used Weber local descriptor to distinguish between texture patterns. In order to identify the suitable grading classifier, we have experimented three approaches, namely, k-nearest neighbor (KNN), support vector machine (SVM) and convolutional neural network (CNN). Our experiments have shown that CNN is the best classifier with an accuracy of 98% for Ajwah, 99% for Mabroom, and 99% for Sukkary. Hence, the CNN classifier has been incorporated in our date grading system.

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“…For classification, ANN, SVM, and random forests were used, with the random forests producing the best results. The study by Raissouli et al [14] proposed a 6-layer CNN to classify three varieties of dates into different grades that outperformed the KNN and SVM models. Bhargava and Bansal [15] proposed an SVM classifier model for the quality evaluation of mono-and bi-colored apples using a combination of statistical, geometrical, Gabor, and Fourier features.…”