Extracting External Features of Sweet Peppers Using Machine Vision System on Mobile Fruits Grading Robot

Abstract

Abstract Algorithms to extract the external features of sweet peppers were developed using the machine vision system. The objectives were to sort color, estimate size, classify shape, detect bruises and predict mass of sweet peppers. A test was performed using 372 samples of sweet pepper variety “TosahikariD”. The results showed that the three main unacceptable colors were recognized and their percentages were calculated by the distribution of hue by saturation. The size of each sample was estimated by three parameters: Feret’s V, maximum diameter and equivalent diameter, and they ranged from 55.3mm to 116.7 mm, 23.2mm to 61.6mm and 21.8mm to 73.1mm, respectively. Five parameters were selected and a neural network model was developed to classify the shape of sweet peppers. Test results indicated that the agreement rate for Shapes A, B, and total samples was 95.12, 100 and 95.70%, respectively. All 11 samples with bruises were successfully detected. The fruit mass was predicted using projection areas; the determination and correlation coefficient was 0.93 and 0.96, respectively.