Identification of damaged corn seeds using air-coupled ultrasound
Keywords:
corn seeds, damaged particles, recognition, air-coupling ultrasonic wave, pattern recognitionAbstract
Corn, an important staple in many countries around the world, is subject to a very inefficient germination rate due to worm-damaged seeds. However, air-coupled ultrasound is a rapid, safe and widely accepted method for the early detection of such damage. In this study, the current effectiveness and future prospects of this technique for identifying damaged seeds were explored. The presented procedure started with drawing a sample of 810 seed particles, consisting of 400 that were intact, 400 manually damaged and 10 damaged by worms. Then the principal component analysis (PCA) method was used to reduce the dimensions of air-coupling ultrasonic information and extract the top ten principal components. Finally, a KNN decision tree by using SIMCA software and a Fisher recognition model by using MATLAB software were constructed. The pattern recognition was established by using KNN, which has the most accurate recognition rate. The correct recognition rate of modeling for the front and back data of the intact particles was 98% and 100%, respectively; and for the manually damaged particles, 99% and 97%, respectively. The results show that the model developed by using air-coupled ultrasonic data can classify corn seed particles both with and without holes to provide a basis for the development of a seed selection system, which has a significant role in improving the clarity and the germination rate. Keywords: damaged corn seed identification, air-coupled ultrasonic, principal component analysis, KNN DOI: 10.3965/j.ijabe.20160901.1880 Citation: Jin Y Y, Gao W L, Zhang H, An D, Guo S H, Ahmed S I, Liu Y L. Identification of damaged corn seeds using air-coupled ultrasound. Int J Agric & Biol Eng, 2016; 9(1): 63-70.References
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[2] Yu Z Y, Tang G L. Technology elements of late japonica rice seeds clarity index analysis. Seed World, 2013; 12: 26–27. (in Chinese with English abstract)
[3] Mahmoud O, Asghar M, Mohammad H O. An intelligent system for sorting pistachio nut varieties. Expert Systems with Applications, 2009; 36: 11528–11535
[4] Onaran I, Pearson T C, Yardimci Y, Cetin A E. Detection of underdeveloped hazelnuts from fully developed nuts by impact acoustics. ASAE, 2006; 49(6): 1971–1976.
[5] Khalifahamzehghasem S, Hassan Komarizadeh M, Askari M. Recognition of filled walnuts and empty walnuts using acoustic signal processing. Int J Agric & Biol Eng, 2012; 5(3): 44–49.
[6] Mei Y M, Guo M. Study on the classification of corn kernels based on the decision tree and fuzzy logic. Journal of Huazhong Normal University, 2013; 47(4). (in Chinese with English abstract)
[7] Xiao Y A. Effect of ultrasornic on the cycas revoluta seed germination. Plant Physiology Communications, 1999, 35(4): 293 (in Chinese with English abstract)
[8] Zhao Y. Effect of different-time ultrasonic wave treatment on germination of brassica napus seed. Seed, 2012, 31(10): 90–92. (in Chinese with English abstract)
[9] Zhou Z G, Ma B Q, Sun Z M, Jiang J T. Application of Phase Coded Pulse Compression Method to Air-coupled Ultrasonic Testing Signal Processing. Journal of Mechanical Engineering, 2014; (1): 48–54. (in Chinese with English abstract)
[10] Kazys R, Demcenko A, Zukauskas E, Mazffika L. Air-coupled ultrasonic investigation of multi - layered composite materials. Ultrasonics 2006; 44: 819–822.
[11] Green R E. Non-contact ultrasonic techniques. Ultrasonics, 2004; 42(19): 9–16.
[12] Potter T J, Ghaffaro B, Mozurkewich G. Sub-wavelength resolution in air-coupled ultrasound images of spot welds. NDT and E International, 2005; 38: 374–380
[13] Chang J J, Lu C, Xiaocang X F. Detection of non-contact air-coupled ultrasonic principle and application. Nondestructive Test, 2013; (8): 6–11
[14] Li Z, Luo F L, Pan M C, Cui H. Application of support vector machine in flaw identification of aircraft bolts: Fourth International Symposium on Precision Mechanical Measurements, Hefei, Anhui, China, 2008; 1–6. (in Chinese with English abstract)
[15] Drai R, Khelil M, Benchaala A. Time frequency and wavelet transform applied to selected problems in ultrasonic NDE. NDT and E International, 2002; 35(8): 567–572.
[16] Vieira A P, de Moura E P, Goncalves L L, Rebellob J M A. Characterization of Welding Defects by Fractal Analysis of Ultrasonic Signals. Chaos, Solitons and Fractals, 2008; 38(3): 748–754.
[17] Zhu Z H, Liu T, Xie D J, Wang Q H, Ma M H. Nondestructive detection of infertile hatching eggs based on spectral and imaging information. Int J Agric & Biol Eng, 2015; 8(4): 69–76.
[18] Jiang L L, Yu X J, He Y. Identification of automobile transmission fluid using hyperspectral imaging technology. Int J Agric & Biol Eng, 2014; 7(4): 81–85.
[19] Witten I H, Frank E. Data mining: Practical machine learning tools and techniques, 2nd ed. Morgan Kaufmann Press, 2005; pp.560.
[20] Xu G G, Jia Y. Matlab implementation of pattern recognition and intelligent computation. Beihang University Press, 2012; pp.17.
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Published
2016-01-31
How to Cite
Yanyun, J., Wanlin, G., Han, Z., Dong, A., Sihan, G., Ahmed, S. I., & Yunling, L. (2016). Identification of damaged corn seeds using air-coupled ultrasound. International Journal of Agricultural and Biological Engineering, 9(1), 63–70. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1880
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Information Technology, Sensors and Control Systems
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