6 January 1995 Assessing cereal grain quality with a fully automated instrument using artificial neural network processing of digitized color video images
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Proceedings Volume 2345, Optics in Agriculture, Forestry, and Biological Processing; (1995); doi: 10.1117/12.198900
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States
Abstract
A fully integrated instrument for cereal grain quality assessment is presented. Color video images of grains fed onto a belt are digitized. These images are then segmented into kernel entities, which are subject to the analysis. The number of degrees of freedom for each such object is decreased to a suitable level for Artificial Neural Network (ANN) processing. Feed- forward ANN's with one hidden layer are trained with respect to desired features such as purity and flour yield. The resulting performance is compatible with that of manual human ocular inspection and alternative measuring methods. A statistical analysis of training and test set population densities is used to estimate the prediction reliabilities and to set appropriate alarm levels. The instrument containing feeder belts, balance and CCD video camera is physically separated from the 90 MHz Pentium PC computer which is used to perform the segmentation, ANN analysis and for controlling the instrument under the Unix operating system. A user-friendly graphical user interface is used to operate the instrument. The processing time for a 50 g grain sample is approximately 2 - 3 minutes.
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Peter J. Egelberg, Olle Mansson, Carsten Peterson, "Assessing cereal grain quality with a fully automated instrument using artificial neural network processing of digitized color video images", Proc. SPIE 2345, Optics in Agriculture, Forestry, and Biological Processing, (6 January 1995); doi: 10.1117/12.198900; http://dx.doi.org/10.1117/12.198900
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KEYWORDS
Statistical analysis

Image segmentation

Video

Calibration

CCD cameras

Error analysis

Image processing

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