Applying acoustic emission and neural network to classify wheat seeds from weed seeds
Keywords:
weed seeds, wheat seeds, classification, identification, acoustic emission, signal processing, neural networkAbstract
In the present study, an expert weed seeds recognition system combining acoustic emissions analysis, Multilayer Feedforward Neural Network (MFNN) classifier was developed and tested for classifying wheat seeds. This experiment was performed for classifying two major important wheat varieties from five species of weed seeds. In order to produce sound signals, a 60o inclined glass plate was used. Fast Fourier Transform (FFT), Phase and Power Spectral Density (PSD) of impact signals were calculated. All features of sound signals are computed via a 1024-point FFT. After feature generation, 60% of data sets were used for training, 20% for validation, and remaining samples were selected for testing. The optimized MFNN model was found to have 500-12-2 and 500-10-2 architectures forReferences
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[2] Maghirang E B, Dowell F E, Baker J E, Throne J E. Automated detection of single wheat kernels containing live or dead insects using near?infrared reflectance spectroscopy. American Society of Agricultural and Biological Engineers, 2003; 46(4): 1277-1282.
[3] Jansen P I. Seed production quality in Trifolium balansae and T. resupinatum: the role of seed color. Seed Science and Technology, 1995; 23: 353-364.
[4] Ahmad I S, Reid J F, Paulsen M R, Sinclair J B. Color classifier for symptomatic soybean seeds using image processing. Plant Disease, 1999; 83: 320-327.
[5] Petersen P E H, Krutz G W. Automatic identification of weed seeds by color machine vision. Seed Science and Technology, 1992; 20: 193-208.
[6] Chtioui Y, Bertrand D, Datte?e Y, Devaux M F. Identification of seeds by color imaging: comparison of discriminant analysis and artificial neural networks. Journal of the Science Food and Agriculture, 1996; 71: 433-441.
[7] Granitto P M, Navone H D, Verdes P F, Ceccatto H A. Weed seeds identification by machine vision. Computer and Electronics in Agriculture, 2002; 33: 91-103.
[8] M Granitto, Pablo F, Verdes Pablo, Alejandro Ceccatto H. Large-scale investigation of weed seed identification by machine vision. Computers and Electronics in Agriculture, 2005; 47: 15-24.
[9] Pearson T, Cetin A E, Tewfik A H, Haff R P. Feasibility of impact-acoustic emissions for detection of damaged wheat kernels. Digital Signal Processing, 2005; 17(3): 617-633.
[10] Ince N F, Onaran I, Pearson T, Tewfik A H, Cetin A E, Kalkan H, et al. Identification of damaged wheat kernels and cracked-shell hazelnuts with impact acoustics time-frequency patterns. American Society of Agricultural and Biological Engineers, 2008; 51(4): 1461-1469.
[11] Mahmoudi A, Omid M, Aghagolzadeh A, Borgayee A M. Grading of Iranian's export pistachio nuts based on artificial neural networks. International Journal of Agriculture & Biology, 2006; 8(3): 371-376.
[12] MathWorks 2008. MATLAB User
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Published
2012-12-11
How to Cite
KhalifaHamzehghasem, S. (2012). Applying acoustic emission and neural network to classify wheat seeds from weed seeds. International Journal of Agricultural and Biological Engineering, 5(4), 68–73. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/476
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Agro-product and Food Processing Systems
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