Identification of pests hidden in wheat kernels based on support vector machine classifier

Identification of pests hidden in wheat kernels based on support vector machine classifier

Zhihui LiTong Zhen Yuhua Zhu 

College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China

Yellow River Conservancy Technical Institute, Kaifeng 475000, China

Corresponding Author Email: 
zhihui_li511@sina.com
Page: 
663-674
|
DOI: 
https://doi.org/10.3166/I2M.17.663-674
Received: 
|
Accepted: 
|
Published: 
31 December 2018
| Citation

ACCESS

Abstract: 

The identification of pests hidden in stored wheats, essential to grain storage safety, is a key difficulty in the research of target detection. This paper introduces the support vector machine (SVM) classifier to identify the pests hidden in wheat kernels, and selects the proper kernel function and parameters to classify various samples. It is verified that the proposed method could accurately detect the pests in wheat kernels. This research provides new insights into the application of pattern recognition in bio-photon detection of pests in stored grains.

Keywords: 

grain kernels, support vector machine, classification, characteristic parameter

1. Introduction
2. Signal acquisition
3. Problems of pattern classification based on SVM
4. Conclusion
Acknowledgment

The authors acknowledge the National key research and development project (No: 2017YFD0401004, No: 2017YFD0401003), Doctoral Fund of Henan University of Technology (Grant: 2017BS034); Food information processing and control laboratory of the key laboratory of ministry of education (Grant: KFJJ-2016-103), National Science Foundation of China (61741107), Key projects of Henan science and Technology Department (172102210230).

  References

Abdullahi N. K., Ahmad M. S., Abubakar A. (2018). Application of electrical resistivity tomography technique for delineation of gold mineralization in Bugai town, Birnin Gwari, Kaduna, North Western Nigeria. Environmental and Earth Sciences Research Journal, Vol. 5, No. 1, pp. 29-35. https://doi.org/10.18280/eesrj.050201

Alamdar F., Ghane S., Amiri A. (2016). On-line twin independent support vector machines. Neurocomputing, Vol. 186, pp. 8-21. https://doi.org/10.1016/j.neucom.2015.12.062

Bello A. A., Mamman M. B. (2018). Monthly rainfall prediction using artificial neural network: A case study of Kano, Nigeria. Environmental and Earth Sciences Research Journal, Vol. 5, No. 2, pp. 37-41. https://doi.org/10.18280/eesrj.050201

Chou J. S., Cheng M. Y., Wu Y. W. (2014). Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification. Expert System, Vol. 41, No. 8, pp. 3955–3964. https://doi.org/10.1016/j.eswa.2013.12.035

Cortes C., Vapnik V. (1995). Support-vector networks. Machine Learning, Vol. 20, No. 2, pp. 273-297. https://doi.org/10.1023/A:1022627411411

Devos O., Downey G., Duponchel L. (2014). Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils. Food Chemistry, Vol. 148, pp. 124-130. https://doi.org/10.1016/j.foodchem.2013.10.020

Feng Y., Palomar D. P. (2015). Palomar Normalization of linear support vector machines. IEEE Trans, Signal Process, Vol. 63, No. 7, pp. 4673-4688. https://doi.org/10.1109/TSP.2015.2443730

Li G., You J., Liu X. (2015). Support Vector Machine (SVM) based prestack AVO inversion and its applications. Journal of Applied Geophysics, Vol. 120, pp. 60-68. https://doi.org/10.1016/j.jappgeo.2015.06.009

Liang Y., Song H., Liu Q., Shi W. Y., Li L. (2014). Study on spectrum estimation in biophoton emission signal analysis of wheat varieties. Mathematical Problems in Engineering, No. 2, pp. 1-9. https://doi.org/10.1155/2014/606275

Maldonado S. (2014). Feature selection for high-dimensional class-imbalanced data sets using support vector machines. Information Sciences, Vol. 286, pp. 228-246. https://doi.org/10.1016/j.ins.2014.07.015

Maldonado S., Pérez J., Weber R. (2014). Feature selection for support vector machines via mixed integer linear programming. Information Sciences, Vol. 279, pp. 163-17. https://doi.org/10.1016/j.ins.2014.03.110

Nalepa J., Siminski K., Kawulok M. (2015). Towards parameter-less support vector machines. 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), No. 3, pp. 211-215. https://doi.org/10.1109/ACPR.2015.7486496

Sun Y., Leng B., Guan W. (2015). A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing, Vol. 166, pp. 109-121. https://doi.org/10.1016/j.neucom.2015.03.085

Thilina K. M., Choi K. W. N., Saquib N. (2012). Pattern classification techniques for cooperative spectrum sensing in cognitive radio networks. SVM and W-KNN Approaches, Vol. 12, No. 7, pp. 1260-1265. https://doi.org/10.1109/GLOCOM.2012.6503286

Van N. H., Patel V. M., Nasrabadi N. M. (2013). Design of non-linear kernel dictionaries for object recognition. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, Vol. 22, No. 12, pp. 5123-5135. https://doi.org/10.1109/TIP.2013.2282078

Wang F., Duan S., Liang Y., Shi W. (2014). Research on the ultra-weak luminescence of maize seeds. Journal of Chemical & Pharmaceutical Research, Vol. 6, No. 5, pp. 42-46.

Zhang K., Qian K., Chai Y. (2014). Research on fault diagnosis of Tennessee Eastman process based on KPCA and SVM. International Symposium on Computational Intelligence & Design, IEEE, Vol. 1, pp. 490-495. https://doi.org/10.1109/ISCID.2014.234