Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach

One of the main problems in the post-harvest processing of citrus is the detection of visual defects in order to classify the fruit depending on their appearance. Species and cultivars of citrus present a high rate of unpredictability in texture and colour that makes it difficult to develop a general, unsupervised method able of perform this task. In this paper we study the use of a general approach that was originally developed for the detection of defects in random colour textures. It is based on a Multivariate Image Analysis strategy and uses Principal Component Analysis to extract a reference eigenspace from a matrix built by unfolding colour and spatial data from samples of defect-free peel. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute defective maps based on the T^2 statistic. In addition, a multiresolution scheme is introduced in the original method to speed up the process. Unlike the techniques commonly used for the detection of defects in fruits, this is an unsupervised method that only needs a few samples to be trained. It is also a simple approach that is suitable for real-time compliance. Experimental work was performed on 120 samples of oranges and mandarins from four different cultivars: Clemenules, Marisol, Fortune, and Valencia. The success ratio for the detection of individual defects was 91.5%, while the classification ratio of damaged/sound samples was 94.2%. These results show that the studied method can be suitable for the task of citrus inspection.

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

[2]  W. S. Lee,et al.  Identification of citrus disease using color texture features and discriminant analysis , 2006 .

[3]  Tünde Vı́zhányó,et al.  Enhancing colour differences in images of diseased mushrooms , 2000 .

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

[5]  M. Mirmehdi,et al.  TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Matti Pietikäinen,et al.  Optimising Colour and Texture Features for Real-time Visual Inspection , 2002, Pattern Analysis & Applications.

[7]  José Blasco,et al.  Original paper: Automatic sorting of satsuma ( Citrus unshiu ) segments using computer vision and morphological features , 2009 .

[8]  James A. Throop,et al.  Quality evaluation of apples based on surface defects: development of an automated inspection system , 2005 .

[9]  Daniel E. Guyer,et al.  Comparison of Artificial Neural Networks and Statistical Classifiers in Apple Sorting using Textural Features , 2004 .

[10]  P. Geladi,et al.  Multivariate image analysis , 1996 .

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

[12]  Da-Wen Sun,et al.  Learning techniques used in computer vision for food quality evaluation: a review , 2006 .

[13]  Yud-Ren Chen,et al.  Machine vision technology for agricultural applications , 2002 .

[14]  Jacques Wainer,et al.  Automatic fruit and vegetable classification from images , 2010 .

[15]  Weikang Gu,et al.  Computer vision based system for apple surface defect detection , 2002 .

[16]  Gustavo Camps-Valls,et al.  Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits , 2008 .

[17]  H. Ramon,et al.  Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks , 2004 .

[18]  Xu Liming,et al.  Automated strawberry grading system based on image processing , 2010 .

[19]  John F. MacGregor,et al.  Texture analysis of images using principal component analysis , 2001, SPIE Optics East.

[20]  David L. Olson,et al.  Advanced Data Mining Techniques , 2008 .

[21]  Da-Wen Sun,et al.  Recent applications of image texture for evaluation of food qualities—a review , 2006 .

[22]  D. L. Peterson,et al.  Identifying defects in images of rotating apples , 2005 .

[23]  Alberto Ferrer,et al.  Integration of colour and textural information in multivariate image analysis: defect detection and classification issues , 2007 .

[24]  Zou Xiaobo,et al.  In-line detection of apple defects using three color cameras system , 2010 .

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

[26]  David W. Penman,et al.  Determination of stem and calyx location on apples using automatic visual inspection , 2001 .

[27]  Da-Wen Sun,et al.  Inspection and grading of agricultural and food products by computer vision systems—a review , 2002 .

[28]  E. R. Davies,et al.  The application of machine vision to food and agriculture: a review , 2009 .

[29]  José Blasco,et al.  Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm , 2007 .