Grading of Bulk Food Grains and Fruits Using Computer Vision

Human beings recognize and grade fruits, grains, flowers and many other agriculture and horticulture produce based on shape, color and pattern. The farmers carry their produce to the market in bulk. At present, the produce and their quality are rapidly assessed through visual inspection by human inspectors. The decision-making capabilities of human-inspectors are subjected to external influences such as fatigue, vengeance, bias, etc., causing injustice to the farmers. The farmers are very much affected by this manual activity in terms of returns for their crop. Hence, these tasks require the automation through development of a computer vision system (CVS) as an alternative for this manual practice leveraging the technology. Since each crop exhibits different color, shape, size and pattern in bulk, pattern recognition and classification methodology is developed to grade the different types of produce. The images of different types of food grains and flowers are preprocessed. The color and texture features are extracted and a neural network based classifier is developed to grade the images of different types of produce into four different grades, namely A (Excellent), B (Very good), C (Good) and D (Poor). It is reported in the literature that computer vision systems are being deployed in interpretation and recognition of images in different applications of science and engineering. The researchers have reported some work on development of CVS for recognition and grading of certain agriculture/horticulture produce.

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

[2]  J. A. Marchant,et al.  Computer vision for potato inspection without singulation , 1990 .

[3]  A. Ghazanfari,et al.  Grading Pistachio Nuts Using A Neural Network Approach , 1996 .

[4]  Michael Recce,et al.  High speed vision-based quality grading of oranges , 1996, Proceedings of International Workshop on Neural Networks for Identification, Control, Robotics and Signal/Image Processing.

[5]  Marvin R Paulsen,et al.  Computer image analyses for detection of maize and soybean kernel quality factors , 1989 .

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

[7]  Ho-Hsien Chen,et al.  THE DEVELOPMENT OF A MACHINE VISION SYSTEM FOR SHIITAKE GRADING , 2004 .

[8]  Anupun Terdwongworakul,et al.  Multivariate data analysis for classification of pineapple maturity , 2008 .

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

[10]  D. Savakar,et al.  Recognition and Classification of Food Grains, Fruits and Flowers Using Machine Vision , 2009 .

[11]  C. T. Morrow,et al.  Grading of Mushrooms Using a Machine Vision System , 1994 .

[12]  James Archibald,et al.  Development of a machine vision system for automatic date grading using digital reflective near-infrared imaging , 2008 .

[13]  E. R. Davies,et al.  Texture analysis for foreign object detection using a single layer neural network , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[14]  Daniele D. Giusto,et al.  Detection of foreign bodies in food by thermal image processing , 2004, IEEE Transactions on Industrial Electronics.

[15]  M. A. Shahin And S.J. Symons,et al.  A machine vision system for grading lentils , 2001 .

[16]  Lei Tian,et al.  MACHINE VISION IDENTIFICATION OF TOMATO SEEDLINGS FOR AUTOMATED WEED CONTROL , 1997 .

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

[18]  T. C. Pearson,et al.  Machine Vision Detection of Early Split Pistachio Nuts , 1996 .

[19]  Basavaraj S. Anami,et al.  Suitability of Feature Extraction Methods in Recognition and Classification of Grains, Fruits and Flowers , 2011 .

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

[21]  Kazuhiro Nakano,et al.  Application of neural networks to the color grading of apples , 1997 .

[22]  Hamit Köksel,et al.  A classification system for beans using computer vision system and artificial neural networks , 2007 .

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

[24]  C.W. de Silva,et al.  Classifier design for computer grading systems for food processing , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.