Multi-objective dynamic detection of seeds based on machine vision

An approach to inspecting massive numbers of moving seeds was studied based on the techniques of dynamic inspection and machine vision. A progressive scanning CCD camera with external trigger function was used for real-time capture of dynamic images of seeds. The methods based on R channel of RGB (Red, Green and Blue) and region-dependent segmentation were adopted to reduce the data size of image processing and improve the efficiency of seeds inspection. All the seeds were sorted into four grades according to their morphological characteristics, such as surface area, perimeter, major axis, minor axis, circularity and eccentricity. The detection experiments indicated that the eligible ratio of the classifications was about 81.90% by this real-time inspection system.