Omid Omidi ARJENAKI, Parviz Ahmadi MOGHADDAM, Asad Moddares MOTLAGH

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.

Anahtar Kelimeler:

Key words: Grading, machine vision, sorting, tomato

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

Keywords:

Key words: Grading, machine vision, sorting, tomato,

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Blasco J, Aleixos N, Cubero S, Gómez SJ, Moltó E (2009) Automatic sorting of Satsuma (Citrus unshiu) segments using computer vision and morphological features. Comput Electron Agr 66: 1–
Gonzalez RC, Woods RE (2002) Digital Image Processing, 2nd ed. Prentice Hall, Inc., New Jersey.
Jarimopas B, Jaisin N (2008) An experimental machine vision system for sorting sweet tamarind. J Food Eng 89: 291–297.
Lino LAC, Sanches J, Dal FMI (2008) Image processing techniques for lemons and tomatoes classification. Bragantia Campinas 67: 785–789. van Assen HC, Egmont PM, Reiber JC (2002) Accurate object localization in gray level images using the center of gravity measure: accuracy versus precision. IEEE T Image Process 11: 1379–1384.
Velioglu SY, Mazza G, Gao L, Omah BD (1998) Antioxidant activity and total phenolics in selected fruits, vegetables, and grain products. J Agr Food Chem 46: 4113–4117.
Weisstein E (2011) Eccentricity. A Wolfram Web Resource. MathWorld, Wolfram Research Inc., available online at http:// mathworld.wolfram.com/Eccentricity.html.
Yud RC, Kuanglin C, Moon KS (2002) Machine vision technology for agricultural applications. Comput Electron Agr 28: 173–191.
Zhang Y, Yin X, Zou X, Zhao J (2009) On-line sorting maturity of cherry tomato by machine vision. IFIP AICT 295: 2223–2229.