Two grading criteria used in determining shrimp product quality and value by the shrimp industry are: 1. Presence or percentage of black spot, measured as a percentage of the total body surface. 2.  Shape quality referring to whole shrimp and broken pieces.  Black spots (melanoma) on the shrimp surface are evidence of aging shrimp and are considered defects that must be removed from the main production line. Shape quality is measured as the size and the completeness of the body. Broken shrimp pieces are considered a product defect and also must be removed from the main production line.  Black spot detection is a simple task for a well-designed machine vision system, which provides consistent and controlled lighting. Shape analysis, on the other hand, is a challenging task because it involves contour extraction and shape analysis. In this paper, a simple, fast, and accurate shape analysis method using Turn Angle Cross-correlation is developed for shrimp quality evaluation. Our analysis results validate that the performance of the proposed shape analysis method is suitable for real-time inspection for commercial applications.
         Quality evaluation of agricultural and food products is important for processing, inventory control, and marketing.  Fruit size and skin delamination are two important quality factors for the date industry, especially for high quality dates such as Medjools.  Unlike other near infrared spectrometric approaches, the developed machine vision system uses reflective near infrared imaging to evaluate date quality by analyzing two-dimensional images.  This paper presents the development and test results of a machine vision system for automatic date quality evaluation for commercial production.  Near infrared imaging, vision algorithms, and a variety of operational details of the system, including cameras, optics,illumination, electronics, control, and fruit carrier are presented.  The complete machine vision system has been built, field tested, and installed in a date packing facility.  Relative to manual grading, the operational system results in improved grading accuracy and a substantial reduction in operating costs.

 Project Sponsors:

  Datepac, LLC, Yuma, Arizona

 Collaborators:

 Graduate Students:

1. D. Zhang, D.J. Lee, and A. Desai, “Using Short-wave Infrared Imaging for Fruit Quality Evaluation," SPIE Electronic Imaging, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, vol. 9025-10, San Francisco, CA, USA, February 2-6, 2014.
2. D.J. Lee, G.M. Xiong, R.M. Lane, and D. Zhang, “An Efficient Shape Analysis Method for Shrimp Quality Evaluation,” IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV), p. 865-870, Guangzhou, China, December 5-7, 2012.
3. R.M. Lane, D.J. Lee, and D. Zhang, “Separating and Sorting Shrimp for Market Grades, Quality and Uniformity with Machine Vision”, Seafood Science Technology 36th Annual Conference and Trans-Atlantic Fisheries Technology Conference, Clearwater Beach, FL, USA, October 30-November 2, 2012.
4. D.J. Lee, R.B. Schoenberger, J.K. Archibald, and S.P. McCollum, “Development of a Machine Vision System for Automatic Date Grading Using Digital Reflective Near-Infrared Imaging,” Journal of Food Engineering, vol. 86/3, p. 388-398, June 2008.