Color image based sorter for separating red and white wheat

A simple imaging system was developed to inspect and sort wheat samples and other grains at moderate feed-rates (30 kernels/s or 3.5 kg wheat/h). A single camera captured color images of three sides of each kernel by using mirrors, and the images were processed using a personal computer (PC). Real time image acquisition and processing was enabled on an ordinary PC under Windows XP operating system using the IEEE 1394 data transfer protocol, DirectX application software, and dual-core computer processor. Image acquisition and transfer to the PC required approximately 17 ms per kernel, and an additional 1.5 ms was required for image processing. After classification, the computer could output a signal from the parallel port to activate an air valve to divert (sort) kernels into a secondary container. Hard red and hard white wheat kernels were used in this study to test and demonstrate sorter capability. Simple image statistics and histograms were used as features. Discriminant analysis was performed with one, two, or three features to demonstrate classification improvements with increased numbers of features. The sorter was able to separate hard red kernels from hard white kernels with 95 to 99% accuracy, depending on the wheat varieties, feed-rate, and number of classification features. The system is an economical and useful instrument for sorting wheat and other grains with high accuracy.