Machine Vision Based Techniques for Automatic Mango Fruit Sorting and Grading Based on Maturity Level and Size

In recent years automatic vision based technology has become more potential and more important to many areas including agricultural fields and food industry. An automatic electronic vision based system for sorting and grading of fruit like Mango (Mangifera indica L.) based on their maturity level and size is discussed here. The application of automatic vision based system, aimed to replace manual based technique for sorting and grading of fruit as the manual inspection poses problems in maintaining consistency in grading and uniformity in sorting. To speed up the process as well as maintain the consistency, uniformity and accuracy, a prototype electronic vision based automatic mango sorting and grading system using fuzzy logic is discussed. The automated system collects video image from the CCD camera placed on the top of a conveyer belt carrying mangoes, then it process the images in order to collect several relevant features which are sensitive to the maturity level and size of the mango. Gaussian Mixture Model (GMM) is used to estimate the parameters of the individual classes for prediction of maturity. Size of the mango is calculated from the binary image of the fruit. Finally the fuzzy logic techniques is used for automatic sorting and grading of mango fruit.

[1]  M. Nagata,et al.  Judgment on level of maturity for tomato quality using L*a*b* color image processing , 2003, Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003).

[2]  C. T. Morrow,et al.  Machine Vision for Color Inspection of Potatoes and Apples , 1995 .

[3]  Suchendra M. Bhandarkar,et al.  Automated Planning and Optimization of Lumber Production Using Machine Vision and Computed Tomography , 2008, IEEE Transactions on Automation Science and Engineering.

[4]  Yuan Cheng,et al.  Vision-Based Online Process Control in Manufacturing Applications , 2008, IEEE Transactions on Automation Science and Engineering.

[5]  E. S. Gopi Algorithm collections for digital signal processing applications using Matlab , 2007 .

[6]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[7]  George C. Runger,et al.  An Automated Feature Selection Method for Visual Inspection Systems , 2006, IEEE Transactions on Automation Science and Engineering.

[8]  Dikai Liu,et al.  Contrast Enhancement and Intensity Preservation for Gray-Level Images Using Multiobjective Particle Swarm Optimization , 2009, IEEE Transactions on Automation Science and Engineering.

[9]  Seong-Dae Kim,et al.  A new chain-coding algorithm for binary images using run-length codes , 1988, Comput. Vis. Graph. Image Process..

[10]  H. C. Garcia,et al.  Automated Refinement of Automated Visual Inspection Algorithms , 2009, IEEE Transactions on Automation Science and Engineering.

[11]  Grantham Pang,et al.  Regularity Analysis for Patterned Texture Inspection , 2009, IEEE Transactions on Automation Science and Engineering.

[12]  Wang Qi,et al.  Review on camera calibration , 2010, 2010 Chinese Control and Decision Conference.

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

[14]  Dah-Jye Lee,et al.  Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping , 2011, IEEE Transactions on Automation Science and Engineering.

[15]  Murray Shanahan,et al.  A Vision-Based Intelligent System for Packing 2-D Irregular Shapes , 2007, IEEE Transactions on Automation Science and Engineering.

[16]  Fred A. Payne,et al.  COLOR AND DEFECT SORTING OF BELL PEPPERS USING MACHINE VISION , 1990 .

[17]  Dongsheng Wang,et al.  Machine Vision Based Image Analysis for the Estimation of Pear External Quality , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[18]  Michael J. Delwiche,et al.  A Color Vision System for Peach Grading , 1988 .

[19]  C. Koley,et al.  A methodology for identification and localization of partial discharge sources using optical sensors , 2012, IEEE Transactions on Dielectrics and Electrical Insulation.