Inspection and Grading of Surface Defects of Fruits by Computer Vision

Computer vision is a rapid, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy provide one alternative for an automated, non-destructive and cost-effective technique to accomplish ever-increasing production and quality requirements. This method of inspection has found applications in the agricultural industry, including the inspection and grading of fruits. This paper provides an introduction to main defection and grading approaches of fruit external defects, including image processing and pattern recognition methods based on fruit two-dimensional (2D) and three-dimensional (3D) information, and hyperspectral and multispectral imaging. In addition, their advantages and disadvantages are also discussed.

[1]  Yang Tao,et al.  DUAL-CAMERA NIR/MIR IMAGING FOR STEM-END/CALYX IDENTIFICATION IN APPLE DEFECT SORTING , 2000 .

[2]  Moon S. Kim,et al.  Development of simple algorithms for the detection of fecal contaminants on apples from visible/near infrared hyperspectral reflectance imaging , 2007 .

[3]  M. Destain,et al.  Defect segmentation on 'Jonagold' apples using colour vision and a Bayesian classification method , 1999 .

[4]  F. Mendoza,et al.  Application of Image Analysis for Classification of Ripening Bananas , 2006 .

[5]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[6]  Daniel Schmoldt,et al.  Computers and Electronics in Agriculture , 2017 .

[7]  M. J. Delwiche,et al.  Machine vision methods for defect sorting stonefruit , 1994 .

[8]  M. Destain,et al.  Development of a multi-spectral vision system for the detection of defects on apples , 2005 .

[9]  Moon S. Kim,et al.  Detection of Fecal Contamination on Cantaloupes Using Hyperspectral Fluorescence Imagery , 2005 .

[10]  Q. Yang,et al.  Classification of apple surface features using machine vision and neural networks , 1993 .

[11]  Moon S. Kim,et al.  Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations , 2004 .

[12]  C. T. Morrow,et al.  Machine Vision Inspection of ‘Golden Delicious’ Apples , 1995 .

[13]  Da-Wen Sun,et al.  Inspecting pizza topping percentage and distribution by a computer vision method , 2000 .

[14]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[15]  Edward J. Delp,et al.  An unsupervised color image segmentation algorithm for face detection applications , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[16]  Ning Wang,et al.  Early detection of apple bruises on different background colors using hyperspectral imaging , 2008 .

[17]  M. S. Kim,et al.  MULTISPECTRAL DETECTION OF FECAL CONTAMINATION ON APPLES BASED ON HYPERSPECTRAL IMAGERY: PART I. APPLICATION OF VISIBLE AND NEAR–INFRARED REFLECTANCE IMAGING , 2002 .

[18]  José Blasco,et al.  Multispectral inspection of citrus in real-time using machine vision and digital signal processors , 2002 .

[19]  Qingsheng Yang,et al.  Apple Stem and Calyx Identification with Machine Vision , 1996 .

[20]  W. R. Windham,et al.  CALIBRATION OF A PUSHBROOM HYPERSPECTRAL IMAGING SYSTEM FOR AGRICULTURAL INSPECTION , 2003 .

[21]  D. E. Chan,et al.  Development of Hyperspectral Imaging Technique for the Detection of Chilling Injury in Cucumbers; Spectral and Image Analysis , 2006 .

[22]  Josse De Baerdemaeker,et al.  Bruise detection on ‘Jonagold’ apples using hyperspectral imaging , 2005 .

[23]  Moon S. Kim,et al.  Hyperspectral reflectance and fluorescence line-scan imaging for online defect and fecal contamination inspection of apples , 2007 .

[24]  Josse De Baerdemaeker,et al.  Detecting Bruises on ‘Golden Delicious’ Apples using Hyperspectral Imaging with Multiple Wavebands , 2005 .

[25]  J. A. Throop,et al.  OPTICAL DETECTION OF BRUISES AND EARLY FROST DAMAGE ON APPLES , 1991 .

[26]  Vincent Leemans,et al.  A real-time grading method of apples based on features extracted from defects , 2004 .

[27]  Yang Tao,et al.  3D surface reconstruction of apples from 2D NIR images , 2005, SPIE Optics East.

[28]  Yud-Ren Chen,et al.  Systematic approach for using hyperspectral imaging data to develop multispectral imagining systems: Detection of feces on apples , 2006 .

[29]  G. Camps-Valls,et al.  Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins , 2008 .

[30]  Bernard Gosselin,et al.  Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition , 2007 .

[31]  Bim Prasad Shrestha,et al.  Integrating multispectral reflectance and fluorescence imaging for defect detection on apples , 2006 .

[32]  Moon S. Kim,et al.  Development of a Simple Algorithm for the Detection of Chilling Injury in Cucumbers from Visible/Near-Infrared Hyperspectral Imaging , 2005, Applied spectroscopy.

[33]  Y. R. Chen,et al.  Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part II. Application of hyperspectral fluorescence imaging , 2002 .

[34]  Ning Wang,et al.  Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks , 2009 .

[35]  José Blasco,et al.  Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features , 2009 .

[36]  Q. Yang,et al.  Finding stalk and calyx of apples using structured lighting , 1993 .

[37]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[38]  Donald L. Peterson,et al.  IDENTIFICATION OF APPLE STEM AND CALYX USING UNSUPERVISED FEATURE EXTRACTION , 2004 .

[39]  José Blasco,et al.  Citrus sorting by identification of the most common defects using multispectral computer vision , 2007 .

[40]  Colm P. O'Donnell,et al.  Hyperspectral imaging – an emerging process analytical tool for food quality and safety control , 2007 .