Machine vision-based algorithms to detect sunburn pomegranate for use in a sorting machine
Parnian Rezaei, Abbas Hemmat, Nima Shahpari, Seyed Ahmad Mireei
Abstract
Sunburn is a widespread problem in pomegranate marketing. About 30 % of the harvested pomegranates suffer from sunburn. In this research, an automatic sorting machine was designed, developed, and tested to separate the sunburn pomegranates. To detect sunburn on pomegranates, three novel image processing algorithms were proposed: skin spot detection, brightness intensity distribution, and statistical surface parameter. Two supervised machine learning algorithms comprising artificial neural networks and support vector machines were utilized to classify the fruits. The proposed algorithms were used to control the developed sorting machine to separate the sunburn pomegranates from the healthy ones. Finally, the performance of the proposed algorithms was evaluated by the accuracy and response time of the sunburn sorting function of the developed machine. The brightness intensity distribution algorithm with the accuracy and response time of 98 % and 0.88 s, respectively, is suggested as the fastest and the most accurate algorithm.