Novel segmentation method and measurement system for various grains with complex touching

Abstract

Thousand kernel weight is one of the essential indicators for evaluating grain quality. Manual work and mechanical counter are traditional means for thousand kernel weight measurement, but they cannot meet the requirements of modern applications in efficiency and intelligence. Machine vision is a powerful and promising technology that can replace traditional measurement means in many fields. Grain segmentation and counting are significant challenges in thousand kernel weight measurement relying on machine vision. This study proposed a novel segmentation method for complex-touching grains of different species, shapes, and sizes. In the method, three specialized algorithms were applied to segment non-touching, simple-touching, and complex-touching grains in the captured images to improve segmentation accuracy. The K-means clustering algorithm was used to extract non-touching grains, and a layered watershed algorithm was created to segment simple-touching grains. A splitting line detection algorithm was innovatively designed by the determination of concave corner point sets, line detection, and line extension to realize accurate segmentation of complex-touching grains. Error correction was carried out at the end of the proposed method to reduce under-segmentation and over-segmentation. Furthermore, a thousand kernel weight measurement system was developed based on the proposed segmentation method. The results for 1000 images containing 640 thousand grains showed that the developed system achieved an average measurement accuracy of 99.65 % and an average measurement time of 1.54 s. The measurement accuracy could be further improved to 100 % by the manual correction. The developed system was distinctly superior in accuracy, efficiency, and application scope for the thousand kernel weight measurement of various grains with complex touching.