Development of a Computer Vision System for the Automatic Quality Grading of Mandarin Segments

  • Authors:
  • José Blasco;Sergio Cubero;Raúl Arias;Juan Gómez;Florentino Juste;Enrique Moltó

  • Affiliations:
  • Centro de AgroIngeniería. Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Náquera km 5. 46113 Moncada (Valencia), España;Centro de AgroIngeniería. Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Náquera km 5. 46113 Moncada (Valencia), España;Centro de AgroIngeniería. Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Náquera km 5. 46113 Moncada (Valencia), España;Centro de AgroIngeniería. Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Náquera km 5. 46113 Moncada (Valencia), España;Centro de AgroIngeniería. Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Náquera km 5. 46113 Moncada (Valencia), España;Centro de AgroIngeniería. Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Náquera km 5. 46113 Moncada (Valencia), España

  • Venue:
  • IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
  • Year:
  • 2007

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Abstract

This work focuses on the development of a computer vision system for the automatic on-line inspection and classification of Satsuma segments. During the image acquisition the segments are in movement, wet and frequently in contact with other pieces. The segments are transported over six semi-transparent conveyor belts that advance at speed of 1 m/s. During on-line operation, the system acquires images of the segments using two cameras connected to a single computer and process the images in less than 50 ms. Extracting morphological features from the objects, the system identifies automatically pieces of skin and row material and separates entire segments from broken ones, discriminating between those with slight or large breaking degree. Combinations of morphological parameters were employed to decide the quality of each segment, classifying correctly 95% of sound segments.