Digital Insect Identification Based On Support Vector Machine

Chinese Agricultural Science Bulletin ›› 2014, Vol. 30 ›› Issue (7) : 286-291. DOI: 10.11924/j.issn.1000-6850.2013-2289
23

Digital Insect Identification Based On Support Vector Machine

Author information +
History +

Abstract

Based on support vector machine (SVM), a novel method for digital insect identification was proposed, and applied in identifying seven species of butterflies with intersectional coordinates of venation in the internal of forewings. The basic principles were as follows: firstly, the intersectional coordinates of venations in the internal of seven species’ forewings were obtained automatically by DrawWing which was a program for numerical description of insect wings. Secondly, binary model was composed by the each type of sample and other samples. Thirdly, the redundant features or unnecessary features were filtered by using support vector classification, and the retained features were used to construct the classification model. Accuracy of the seven prediction model was 98.64%, and higher than the reference model, that the new method of identification in the field of insect has a good prospect.

Key words

support vector machine (SVM); digital identification; features filtering; insect identification

Cite this article

Download Citations
Digital Insect Identification Based On Support Vector Machine. Chinese Agricultural Science Bulletin. 2014, 30(7): 286-291 https://doi.org/10.11924/j.issn.1000-6850.2013-2289

References

[1] 李志勤,闫洪波,李成德.昆虫分类的主要技术手段[J].河北林果研究,2006.21(4):398-403.
[2] Liu J D. The expert system for identification of tortricinae (Lepidoptera) using image analysis of venation[J].Entomol.Sin., 1996,3(1) :1-8.
[3] Weeks P J, O'Neill M A, Gaston K J, et al. Species-identification of wasps using principal component associative memories[J]. Image Vis. Comput.,1998(17):861-866.
[4] Christian P K, Alexander V B, Susanna M S, et al. Inferring developmental modularity from morphological integration: Analysis of individual variation and asymmetry in bumblebee wings [J]. The American Naturalist,2001,157(1):11-23.
[5] 沈佐锐,于新文.昆虫数学形态学研究及其应用展望[J].昆虫学报, 1998,41(增刊):140-148.
[6] 赵汗青,沈佐锐,于新文.数学形态学在昆虫分类学上的应用研究.Ⅰ. 在目级阶元上的应用研究[J].昆虫学报,2003,46(1):45-50.
[7] 潘鹏亮,杨红珍,沈佐锐,等.翅脉的数学形态特征在蝴蝶分类鉴定中的应用研究[J].昆虫分类学报,2008,30(2):151-160.
[8] Vapnik V N. The nature of statistical learning theory[M]. New York: Springer Verlag Press,1995:87-189.
[9] Wang T Y, Chiang H M. Fuzzy support vector machine for multiclass text categorization[J].Inform. Process. manag,2007,43(4):914- 929.
[10] 周翔,陈会,张锴,等.复杂背景下的图像文本区域定位方法研究[J].计算机工程与应用,2013,49(12):101-105.
[11] 蔡曦,胡昌华,刘炳杰,等.基于 aiNet算法优化 SVM模型的惯性器件故障预报[J].计算机仿真,2007,24(10):31-34.
[12] 袁哲明,张永生,熊洁仪.基于 SVR的多维时间序列分析及其在农业科学中的应用[J].中国农业科学,2008,41(8):2485-2492.
[13] 李星,陈渊,张永生,等.基于支持向量回归与地统计学的多维时间序列分析[J].中国农学通报,2011,27(29):133-138.
[14] 贺元元,张雪英,刘晓峰.多类分类预选取的 SVM在语音识别中的应用[J].计算机工程与应用,2013,49(7):115-118.
[15] 梁桂兆,李志良,周原,等.一种新多肽表征方法及支持向量机用于肽 HPLC 定量结构-保留建模预测[J].物理化学学报,2006,22(9): 1052-1055.
[16] Chang C C, Lin C J. LIBSVM: a library for support vector machines [EB/OL]. Software available at http://www.csie.ntu.edu.tw/~cjlin/ libsvm.
[17] Zhang L, Zhang B. Relationship between support vector set and Kernel Functions in SVM[J]. J.Comput. Sci. & Technol,2002,17(5): 549-555.
Share on Mendeley

Accesses

Citation

Detail

Sections
Recommended

/