Computer vision algorithm for barley kernel identification, orientation estimation and surface structure assessment

This paper presents an algorithm for analyzing barley kernel images to evaluate cereal grain quality and perform grain classification. The input data comprised digital images of kernels obtained from an optical scanner. The algorithm identified individual kernels’ smooth and wrinkled regions, described their orientation relative to the axis of symmetry, crease visibility and germ location. We were also able to determine the size of the wrinkled and smooth areas on a grain’s surface, which allowed automatic grain classification and kernel quality assessment. The proposed algorithm was tested using barley grain images, and validated by comparison with the evaluation results of a professional assessor. The validation of the algorithm confirmed that it is efficient and robust allowing accurate description of over 93% of kernel samples in comparison with the expert.

► An algorithm to analyze barley kernel images was proposed. ► The algorithm identified the wrinkled and smooth regions of individual kernels with a mean accuracy of 99%. ► The algorithm eliminated the need for human involvement in the assessment process.