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

Omid Omidi ARJENAKI; Parviz Ahmadi MOGHADDAM; Asad Moddares MOTLAGH

doi:https://doi.org/10.3906/tar-1201-10

Makale detayları

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

Yazarlar:

Omid Omidi ARJENAKI | Parviz Ahmadi MOGHADDAM | Asad Moddares MOTLAGH

Yayıncı:

TUBITAK

Ana Başlıklar:

Tarım

Dergi:

Turkish Journal of Agriculture and Forestry 2013 Vol.37 Issue 1, pp.62-68

Belge Tipi:

Makale

Yayın Tarihi:

12/2012

Belge Dili:

İngilizce

Document ID (Org No.):

2000807759

Sıklık:

İki Aylık

eISSN:

1303-6173

pISSN:

1300-011X

DOI:

https://doi.org/10.3906/tar-1201-10

Anahtar kelimeler:

Özet:

 

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.

 

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.

Makale detayları

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

Yazarlar:

Omid Omidi ARJENAKI | Parviz Ahmadi MOGHADDAM | Asad Moddares MOTLAGH

Yayıncı:

TUBITAK

Ana Başlıklar:

Tarım

Dergi:

Turkish Journal of Agriculture and Forestry 2013 Vol.37 Issue 1, pp.62-68

Belge Tipi:

Makale

Yayın Tarihi:

12/2012

Belge Dili:

İngilizce

Document ID (Org No.):

2000807759

Sıklık:

İki Aylık

eISSN:

1303-6173

pISSN:

1300-011X

DOI:

https://doi.org/10.3906/tar-1201-10

Key words: Grading

machine vision

sorting

tomato

Sayı

Makale detayları

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

Yazarlar: Omid Omidi ARJENAKI | Parviz Ahmadi MOGHADDAM | Asad Moddares MOTLAGH

Yayıncı: TUBITAK

Ana Başlıklar: Tarım

Dergi: Turkish Journal of Agriculture and Forestry 2013 Vol.37 Issue 1, pp.62-68

Belge Tipi: Makale

Yayın Tarihi: 12/2012

Belge Dili: İngilizce

Document ID (Org No.): 2000807759

Sıklık: İki Aylık

eISSN: 1303-6173

pISSN: 1300-011X

DOI: https://doi.org/10.3906/tar-1201-10

Key words: Grading

machine vision

sorting

tomato

Sayı

Yazarlar:

Omid Omidi ARJENAKI | Parviz Ahmadi MOGHADDAM | Asad Moddares MOTLAGH

Yayıncı:

TUBITAK

Ana Başlıklar:

Tarım

Dergi:

Turkish Journal of Agriculture and Forestry 2013 Vol.37 Issue 1, pp.62-68

Belge Tipi:

Makale

Yayın Tarihi:

12/2012

Belge Dili:

İngilizce

Document ID (Org No.):

2000807759

Sıklık:

İki Aylık

eISSN:

1303-6173

pISSN:

1300-011X

DOI:

https://doi.org/10.3906/tar-1201-10