Diversity of Plum Stones Based on Image Texture Parameters and Machine Learning Algorithms
Ewa Ropelewska
Abstract
The objective of this study was to evaluate the usefulness of machine learning based on image texture parameters to discriminate plum stone cultivars. The plums of cultivars ‘Emper’, ‘Kalipso’, and ‘Polinka’ were sampled. For each cultivar, one hundred images of plum stones were acquired using a digital camera. Processing of the plum stone images included the conversion of the images to individual color channels, image segmentation, region of interest (ROI) determination, and texture parameter extraction. Then, the discriminant analysis, including the texture selection and building discriminative models for the evaluation of the diversity of the plum stone cultivars, was carried out. The obtained results of discrimination of plum stone cultivars were very accurate and confirmed the effectiveness of image processing to evaluate the cultivar diversity. The most satisfactory results, reaching 96.67% for the average accuracy for three cultivars (97% for ‘Emper’, ‘Kalipso’, and 96% for ‘Polinka’), were obtained for the models built based on combined textures selected from all the color channels using the IBk classifier. The developed procedure can be of practical importance for the correct identification of plum stone cultivars and avoiding their mixing to preserve cultivar uniformity.