Online tomato sorting based on shape, maturity, size, and surface defects using machine vision

Online sorting of tomatoes according to their features is an important postharvest procedure. The purpose of this research was to develop an efficient machine vision-based experimental sorting system for tomatoes. Relevant sorting parameters included shape (oblong and circular), size (small and large), maturity (color), and defects. The variables defining shape, maturity, and size of the tomatoes were eccentricity, average of color components, and 2-D pixel area, respectively. Tomato defects include color disorders, growth cracks, sunscald, and early blight. The sorting system involved the use of a CCD camera, a microcontroller, sensors, and a computer. Images were analyzed with an algorithm that was developed using Visual Basic 2008. In order to evaluate the accuracy of the algorithms and system performance, 210 tomato samples were used. Each detection algorithm was applied to all images. Data about the type of each sample image, including healthy or defective, elongated or rounded, small or large, and color, were extracted. Results show that defect detection, shape and size algorithm, and overall system accuracies were 84.4%, 90.9%, 94.5%, and 90%, respectively. System sorting performance was estimated at 2517 tomatoes h^–1 with 1 line.