Computer vision sensing of stress cracks in corn kernels

Maintaining high quality of corn is important to both corn producers and buyers. Stress crack detection remains one of the most important tasks in corn quality inspection. Such a measure of quality would be helpful in assessing not only the end-use values of the corn, but also the drying method used and the amount of expected breakage due to subsequent handling procedures. A computer vision system was developed for automatic detection of corn stress cracks which simulates the processes that the human visual system uses to perceive the stress cracks from the corn kernel in the conventional candling method. The stress crack detection system consisted of four consecutive stages; windowing, edge detection, feature representation, and classification. A set of performance criteria was developed to evaluate the stress crack detection system and used to compare the performance of different configurations on several corn varieties. Evaluation results showed that the system configured with the circular band operator, the Duda road operator, and the Hough transform performed best; with success rates of 78.2% and failure rates of 8.2% of the classification made by one human expert. The performance measures of the system with this configuration were equal or superior to that of other human inspectors. The accuracy of the system was 91.8% when the system was used to distinguish only stress cracked-kernels from sound kernels.