The Corn Seed Image Segmentation and Measurement of the Geometrical Features Based on Image Analysis

Abstract:

In this article, separation of touching grain kernels and measurement of geometric features in an image are presented. The objective of this work is to discriminate single corn kernel and some broken kernels, which are difficult to achieve on the existing machinery and equipment, especially for the counted number and quality inspection process. The digital image was obtained from flatted conveyor belt; the geometrical features were analyzed using Matlab R2009a software. It was used for recognizing multiple kernels for once. Intact and broken kernels were characterized by the changed geometrical features and combining SVM for the classification of them with accuracy 95%.Which meet the requirement of corn quality inspection comparable to subjective human inspection.

• Periodical: Applied Mechanics and Materials (Volumes 66-68)
• Pages: 1100-1105
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.66-68.1100
• Online since: July 2011
• Authors: Min Zhao, Wen Fu Wu, Ya Qiu Zhang
• Keywords: Broken Corn Kernels, Feature Extraction, Support Vector Machine (SVM), Watershed Transformation

References

[1] Zayas, I, H. Converse and J. Steel. Discrimination of whole from broken corn kernels with image analysis. Transactions of the ASAE (1990), 33: 1642-1646.

DOI: 10.13031/2013.31521

[2] Ng, H. F, W.F. Wilcke, R.V. Morey, et al. Machine vision evaluation of corn kernel mechanical and mold damage. Trans of the ASAE (1998), 41(2): 415-420.

DOI: 10.13031/2013.17166

[3] Shi Zhixing, Cheng Hong, Li Jiangtao. Characteristic parameters to identify varieties of corn seeds by image processing. Transactions of the CSAE (2008), 24(6): 193-195, in Chinese.

[4] Zheng Min Jiang, Huang Xiao Fang . Research about quality grading of corn seed based on digital image processing[D]. Wuhan University of Technology(2009), in Chinese.

[5] Li Wei, Lin Jiachun. Measuring Method of seed spacing on test rig based on image processing techniques. Transactions of the CSAE (2002), Vol18, No. 6: 165-168, in Chinese.

[6] Ning Ji Feng, He Dong Jian, Yang Shu Qin. Identification of tip cap and germ surface of corn kernel using computer vision. Transactions of the Chinese society of Agricultural Engineering (2004), 20(3): 117-119, in Chinese.

[7] P. Shatadal, D.S. Jayas, N. R. Bulley, Digital image analysis for software separation and classification of touching grains: 1. Disconnected algorithm, Transaction of ASAE (1995), Vol. 38, PP. 635-643.

DOI: 10.13031/2013.27876

[8] Ai Na, Wu Zuowei, Ren Jianghua. Support vector machine and artificial neural network. Journal of Shandong University of Technology: Science and Technology (2005), 19(5): 45～49, in Chinese.