Applications of computer vision techniques in the agriculture and food industry: a review

Over the last decades, parallel to technological development, there has been a great increase in the use of visual inspection systems. These systems have been widely implemented, particularly in the stage of inspection of product quality, as a means of replacing manual inspection conducted by humans. Much research has been published proposing the use of such tools in the processes of sorting and classification of food products. This paper presents a review of the main publications in the last ten years with respect to new technologies and to the wide application of systems of visual inspection in the sectors of precision farming and in the food industry.

[1]  Daoliang Li,et al.  IFIP Advances in information and communication technology , 2007 .

[2]  E. Madieta.  APPLE COLOUR MEASUREMENTS. SOME METROLOGICAL APPROACHES , 2003 .

[3]  Bernard Gosselin,et al.  Automatic defect segmentation of ‘Jonagold’ apples on multi-spectral images: A comparative study , 2006 .

[4]  Re Gonzalez,et al.  R.C. Eddins, Digital image processing using MATLAB, vol. Gatesmark Publishing Knoxville , 2009 .

[5]  Ewert Bengtsson,et al.  A Feature Set for Cytometry on Digitized Microscopic Images , 2003, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.

[6]  Suwanee Boonmung,et al.  Physical Properties Analysis of Mango using Computer Vision , 2005 .

[7]  N. S. Visen,et al.  AE—Automation and Emerging Technologies: Identification and Segmentation of Occluding Groups of Grain Kernels in a Grain Sample Image , 2001 .

[8]  Xavier P. Burgos-Artizzu,et al.  Real-time image processing for crop / weed discrimination in maize fields , 2012 .

[9]  Gustavo Camps-Valls,et al.  Hyperspectral detection of citrus damage with Mahalanobis kernel classifier , 2007 .

[10]  Jacco C. Noordam,et al.  High-speed potato grading and quality inspection based on a color vision system , 2000, Electronic Imaging.

[11]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[12]  Jacques Wainer,et al.  Automatic Produce Classification from Images Using Color, Texture and Appearance Cues , 2008, 2008 XXI Brazilian Symposium on Computer Graphics and Image Processing.

[13]  D. V. Byrne,et al.  Evaluation of pork colour: prediction of visual sensory quality of meat from instrumental and computer vision methods of colour analysis. , 2003, Meat science.

[14]  Gerhard Jahns,et al.  Measuring image analysis attributes and modelling fuzzy consumer aspects for tomato quality grading , 2001 .

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

[16]  Jie Zou,et al.  Visible models for interactive pattern recognition , 2007, Pattern Recognit. Lett..

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

[18]  D. Stajnko,et al.  Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging , 2004 .

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

[20]  Alberto Tellaeche,et al.  A new vision-based approach to differential spraying in precision agriculture , 2008 .

[21]  José Blasco,et al.  Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision , 2009 .

[22]  Daniel E. Guyer,et al.  Evaluation of different pattern recognition techniques for apple sorting , 2008 .

[23]  Andreas Koschan,et al.  Digital Color Image Processing , 2008 .

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

[25]  Noel D.G. White,et al.  Wheat class identification using monochrome images , 2008 .

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

[27]  Jiewen Zhao,et al.  Apple color grading based on organization feature parameters , 2007, Pattern Recognit. Lett..

[28]  Domingo Mery,et al.  Development of a computer vision system to measure the color of potato chips , 2006 .

[29]  José Blasco,et al.  Short communication.Automatic inspection of the pomegranate (Punica granatum L.) arils quality by means of computer vision , 2008 .

[30]  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 .

[31]  E Misimi,et al.  Quality grading of Atlantic salmon (Salmo salar) by computer vision. , 2008, Journal of food science.

[32]  Digvir S. Jayas,et al.  Comparison of illuminations to identify wheat classes using monochrome images , 2008 .

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

[34]  Ismael Lopez-Juarez,et al.  Ann Analysis in a Vision Approach for Potato Inspection , 2008 .

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

[36]  Nyuk Ling Chin,et al.  Bread crust thickness measurement using digital imaging and L a b colour system , 2009 .

[37]  Ronnier Luo,et al.  Measuring colour appearance of red wines , 2007 .

[38]  Da-Wen Sun,et al.  Inspection of the distribution and amount of ingredients in pasteurized cheese by computer vision , 2007 .

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

[40]  Noel D.G. White,et al.  Comparison of a Neural Network and a Non-parametric Classifier for Grain Kernel Identification , 2003 .

[41]  Tom Duckett,et al.  A computer vision system for appearance-based descriptive sensory evaluation of meals , 2007 .

[42]  Noel D.G. White,et al.  Evaluation of the effect of moisture content on cereal grains by digital image analysis , 2007 .

[43]  Cheng-Jin Du,et al.  Prediction of beef eating quality from colour, marbling and wavelet texture features. , 2008, Meat science.

[44]  Andrew R. East,et al.  Colour vision system evaluation of bicolour fruit: A case study with ‘B74’ mango , 2008 .

[45]  Zou Xiaobo,et al.  Apple color grading based on organization feature parameters , 2007 .

[46]  D. Jayas,et al.  Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images , 2008 .

[47]  Alberto Tellaeche,et al.  Analysis of natural images processing for the extraction of agricultural elements , 2010, Image Vis. Comput..

[48]  G. van Straten,et al.  A vision based row detection system for sugar beet , 2005 .

[49]  Margarita Ruiz-Altisent,et al.  Olive classification according to external damage using image analysis. , 2008 .

[50]  Cheng-Jin Du,et al.  Comparison of three methods for classification of pizza topping using different colour space transformations , 2005 .

[51]  Juliana Sanches,et al.  Image processing techniques for lemons and tomatoes classification , 2008 .

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

[53]  José Blasco,et al.  Machine Vision System for Automatic Quality Grading of Fruit , 2003 .

[54]  Wiesnerová Dana,et al.  Computer image analysis of seed shape and seed color for flax cultivar description , 2008 .

[55]  Grzegorz Cielniak,et al.  Visual detection of blemishes in potatoes using minimalist boosted classifiers , 2010 .

[56]  Pierre Loonis,et al.  A novel colorimetry analysis used to compare different drying fish processes , 2004 .

[57]  Jiewen Zhao,et al.  On-Line Detecting Size and Color of Fruit by Fusing Information from Images of Three Color Camera Systems , 2008, CCTA.

[58]  Noel D.G. White,et al.  AE—Automation and Emerging Technologies: Specialist Neural Networks for Cereal Grain Classification , 2002 .

[59]  N. S. Visen,et al.  Cereal Grain and Dockage Identification using Machine Vision , 2003 .

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

[61]  Rizauddin Ramli,et al.  Development of Jatropha Curcas color grading system for ripeness evaluation , 2009 .

[62]  N. S. Visen,et al.  AE—Automation and Emerging Technologies: Evaluation of Neural Network Architectures for Cereal Grain Classification using Morphological Features , 2001 .

[63]  H. T. Søgaard,et al.  Determination of crop rows by image analysis without segmentation , 2003 .

[64]  Bundit Jarimopas,et al.  An experimental machine vision system for sorting sweet tamarind , 2008 .

[65]  D. Stajnko,et al.  Modelling of Apple Fruit Growth by Application of Image Analysis , 2005 .

[66]  Da-Wen Sun,et al.  Emerging non-contact imaging, spectroscopic and colorimetric technologies for quality evaluation and control of hams: a review , 2010 .

[67]  Da-Wen Sun,et al.  Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams. , 2009, Meat science.

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

[69]  Alberto Tellaeche,et al.  A computer vision approach for weeds identification through Support Vector Machines , 2011, Appl. Soft Comput..