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A computer vision system for automated corn seed purity analysis

Electrophoresis gel analysis is a viable technique for determining the purity of hybrid corn seeds. Visually analyzing the electrophoretic gel images is a very tedious and time-consuming task. In this paper, a computer vision system integrating image processing and pattern recognition techniques with domain-specific structural information to automate the electrophoresis gel scoring procedure is presented. A set of image processing algorithms are developed to perform extraction of the region of interest, segmentation of samples, identification of bands within samples, and final classification of different types of seeds.
The image processing algorithms utilize the structural information and operator expertise to achieve high classification rate with fuzzy and incomplete information contained on the electrophoresis gels. The developed technique clearly demonstrates the potential of using computer vision in automating the gel scoring procedure. The developed technology may also be extended to other areas such as general one-dimensional electrophoresis and high performance thin layer chromatography.