Features extraction for eggplant fruit grading system using machine vision

Vui Kiong Chong, Naoshi Kondo, Kazunori Ninomiya, Takao Nishi, Mitsuji Monta, Kazuhiko Namba, Qin Zhang

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


Machine vision based grading for agricultural crops has been well developed and accepted as an attractive grading method. However, machine vision based grading for eggplant fruit is not available yet. This study reports on the attempt to develop an eggplant grading machine using six CCD cameras as the sensing device. Feature extraction algorithms were developed to extract eggplant's features, i.e., length, diameter, volume, curvature, color homogeneity, calyx color, calyx area, and surface defect. The system could acquire six images per fruits covering the entire surface of the eggplant fruits. An agreement rate of 78.0% was achieved in the feasibility study where the machine vision based grading was compared with manual grading. The throughput of the developed system was 0.3 second per fruit. Details of the system, an outline of the algorithm, and performance results are reported in this article.

Original languageEnglish
Pages (from-to)675-684
Number of pages10
JournalApplied Engineering in Agriculture
Issue number5
Publication statusPublished - Dec 1 2008


  • Classification
  • Grading
  • Image processing
  • Image segmentation
  • Machine vision

ASJC Scopus subject areas

  • General Engineering


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