Pomegranate seed clustering by machine vision

Application of new procedures for reliable and fast recognition and classification of seeds in the agricultural industry is very important. Recent advances in computer image analysis made applicable the approach of automated quantitative analysis in order to group cultivars according to minor differences in seed traits that would be indiscernible in ocular inspection. In this work, in order to cluster 20 cultivars of pomegranate seed, nine image features and 21 physicochemical properties of them were extracted. The aim of this study was to evaluate if the information extracted from image of pomegranate seeds could be used instead of time-consuming and partly expensive experiments of measuring their physicochemical properties. After data reduction with principal component analysis (PCA), different kinds of overlapping between these two types of data were controlled. The results showed that clustering base on all variables of image features contain more similar cultivars with clustering base on physicochemical properties (66.67% for cluster 1, 75% for cluster 2, and 50% for cluster 3). Therefore, by applying image analysis technique, the seeds almost were placed in different pomegranate clusters without spending time and additional costs.