<title>Image analysis identification of broken and sound shelled corn bulk samples</title>

Abstract

Multispectral image analysis was used to identify broken and sound kernels in bulk samples of corn. Images in a 512 X 512 pixel format were acquired with 50 nm bandpass filters in the visual and near infrared regions of the spectrum. Samples of broken and sound corn kernels were assessed. A search for pixels which represented endosperm and sound tissue of the kernels was done by relating the gray values from different bandwidth images at the same topological location. Data analysis was done using means of 4 X 4 arrays of normalized pixel values and derived features to create a pattern for sample recognition. The most effective bandwidths for identification of endosperm tissue was determined followed by a search of pixel coordinates to identify endosperm areas. Binarization of the endosperm areas, reflective spots and shade between kernels was done as a preprocessing step. Samples were then classified by evaluation of 256 X 256 pixel subimages of each sample. A 100% correct recognition rate of the broken and sound corn classes was achieved.