K. Gao, T. White, K. Palaniappan, M. Warmund, and F. Bunyak

IEEE International Conference on Image Processing, pgs. 2826-2830, 2017

plant seed morphometry, image analysis, mobile application

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Abstract

This paper presents an Android-based mobile application named MUSeed that automatically computes seed morphometry utilizing image processing techniques. Unlike most of the existing tools, MUSeed does not impose restrictions on arrangement of seeds since it is capable of handling touching seed instances. First, RGB color space is converted to RG-chromaticity space to reduce the influence of shadows and illumination variations. Then K-means clustering is performed to segment the seeds from the background. To split touching seeds in the image, a Watershed with Edge-Augmented Markers (WEAM) algorithm and Concave Point Analysis (CPA) are developed. After that, a fitness function is performed to select the most appropriate result. MUSeed is benchmarked against other similar tools on 7 cultivars of American elderberry seeds and shows promising results.