الملخص الإنجليزي
Oman produces an average of 268,011 tonnes of dates annually. But the annual export from Oman is low mainly due to the poor quality of the processed and packaged dates. Surface crack is a type of defects which depreciates date quality. At present, cracked dates in processing and packing lines are removed manually by graders. The efficiency of manual sorting is affected by several factors such as the variation in human visual inspection. The objective of this study was to determine the efficiency of a computer vision system with RGB color camera to detect the surface cracks on dates. Three grades of 'Khalas' variety dates (high-crack dates, low crack dates and no-crack dates) were obtained from two commercial dates processing factories in Oman. After the confirmation of grade standards by a dates quality-expert, the samples were imaged individually using a color camera (105 dates in each grade). Eleven features were extracted from each image and used in classification models. Red, hue and value intensities of three grades of dates were significantly different from each other. In a three classes model, the classification accuracy was 62%, 58% and 78% for high-crack, low-crack and no-crack dates, respectively using linear discriminant analysis (LDA). LDA yielded a classification accuracy of 88% and 75% for the dates with-crack and without-crack, respectively in a two classes model. In pairwise discrimination, the highest classification (96%) was achieved between high-crack and no-crack dates, and the lowest accuracy (59%) was between low-crack and high-crack dates. The skin delamination was the main reason of the misclassification,
