Onopordum Polen 2.Pdf
Total Page：16
File Type：pdf, Size：1020Kb
Review of Palaeobotany and Palynology 189 (2013) 50–56 Contents lists available at SciVerse ScienceDirect Review of Palaeobotany and Palynology journal homepage: www.elsevier.com/locate/revpalbo Research papers An expert classification system of pollen of Onopordum using a rough set approach Yılmaz Kaya a, S. Mesut Pınar b, M. Emre Erez c,⁎, Mehmet Fidan c a Siirt University, Engineering and Architecture Faculty, Computer Engineering, 56100 Siirt, Turkey b Yüzüncü Yıl University, Faculty of Science, Department of Biology, 65080 Van, Turkey c Siirt University, Faculty of Science and Art, Department of Biology, 56100 Siirt, Turkey article info abstract Article history: Although pollen grains have a complicated 3-dimensional structure, they can be distinguished from one another Received 1 June 2012 by their specific and distinctive characteristics. Using microscopic differences between the pollen grains, it Received in revised form 29 October 2012 may be possible to identify them by family or even at the genus level. However for the identification of pollen Accepted 16 November 2012 grains at the taxon level, we require expert computer systems. For this purpose, we used 20 different pollen Available online 7 December 2012 types, obtained from the genus Onopordum L. (Asteraceae). For each pollen grain, 30 different images were photographed by microscope system and 11 different characteristic features (polar axis, equatorial axis, P/E Keywords: pollen ratio, colpus length, colpus weight, exine, intine, tectum, nexine, columellea, and echinae length) were measured pollen identification for the analysis. The data set was formed from 600 samples, obtained from 20 different taxa, with 30 different expert system images. The 440 samples were used for training and the remaining 160 samples were used for testing. The pro- rough set posed method, a rough set-based expert system, has properly identified 145 of 160 pollen grains correctly. The success of the method for the identification of pollen grains was obtained at 90.625% (145/160). We can expect to achieve more efficient results with different genuses and families, considering the successful results in the same genus. Moreover, using computer-based systems in revision studies will lead us to more accurate and efficient results, and will identify which characters will be more effective for pollen identification. According to the literature, this is the first study for the identification and comparison of pollen of the same genus by using the measurements of distinctive characteristics with computer systems. © 2012 Elsevier B.V. All rights reserved. 1. Introduction similarity. However, pollen grains show some differences in terms of the thickness of the layers, according to their taxon. The best example Pollen is produced in the male organs of flowers and it is necessary of using the pollen layer differences was the genus Polygonum from for fertilization in angiosperm. Pollen grains produced by different Polygonaceae. Many species were presented in this genus over a long plant species differ in shape, size, and color. The distinctive character- period of time, and then they were separated to 7 different genuses, istics usually indicate the family of plants and give some knowledge according to their pollen morphology and chromosome number about its geographical region. Moreover, the biometric characteristics (Yıldız, 2005). An innovative methodology to discriminate Urticaceae of pollen grains of the same genus have been shown to have different species (Urtica membranacea, Urtica urens and Parietaria judaica)using properties from one another when examined under a microscope computer vision has been put forward. This approach is based on the system. definition of digital shape parameters to represent a pollen grain. Palynologic features have been very valuable in delimiting taxa or The system performance has more than an 86% of success rate for all ex- aiding in phylogenetic inferences (Clark et al., 1980). The morpho- periments done. They used area, diameter, mean distance to centroide logic and microscopic characters of pollen grains have frequently and roundness (De Sá-otero et al., 2004). In our study we have used been regarded as systematically significant in angiosperm. However, eleven different features for identification. Pollen discrimination and it requires an experienced person with extensive knowledge or an classification by Fourier transform infrared (FT-IR) microspectroscopy expert system for estimation and identification (Li and Flenley, and machine learning was used for identification of 11 plant species be- 1999). Most of the quantitive palynologic characters are very similar longing to different 7 families. The KNN classifier they built got an over- in a genus and it is difficult to distinguish them from one another. all accuracy of 84%, and for nine out of the 11 considered plant species, Furthermore, recognizing the differences between the pollen grains the obtained accuracy was greater than or equal to 80% (Dell'Anna et al., of the same genus with the naked eye is difficult because of their 2009). The Asteraceae (Compositae) is an exceedingly large and widespread family of vascular plants. This family is represented by 1600 genera and ⁎ Corresponding author. E-mail addresses: [email protected] (Y. Kaya), [email protected] about 23,000 species worldwide (Kubitzki, 2007), and 143 genera and (S.M. Pınar), [email protected] (M.E. Erez), mfi[email protected] (M. Fidan). approximately 1484 species in Turkey (Davis, 1975; Davis et al., 1988; 0034-6667/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.revpalbo.2012.11.004 Y. Kaya et al. / Review of Palaeobotany and Palynology 189 (2013) 50–56 51 Özhatay et al., 1994, 1999, 2009; Güner et al., 2000; Erik and Tarıkahya, can differ from plant to plant. Pollen grains may have furrows and 2004; Özhatay and Kültür, 2006). Also the pollen grains of Asteracea the number of furrows or pores helps classify the flowering plants, family are very similar to each other so they are suitable for identification which is known as ornamentation. Ornamentation (i.e fovaelote, studies by the computerized methods. reticulate, echinate) is also a distinctive character for pollen identifi- Onopordum L. is a genus of about 60 species of thistles belonging cation (Erdtman, 1969; Faegri and Iversen, 1975). to the family Asteraceae, native to Europe (mainly the Mediterranean region), northern Africa, the Canary Islands, the Caucasus, and south- 2.2. Determination of images and the measurements west and central Asia (Kubitzki, 2007). In Turkey, the genus com- prises 19 species, including 2 subspecies, a total of 20 taxa (Danin, The material used for this study was collected from wild populations 1975; Davis et al., 1988; Güner et al., 2000; Özhatay et al., 2009). in Turkey, which belong to 20 taxa, belonging to 19 species. Specimens The pollen type is monad and the shape is oblate-spheroidal. The of the plants have been deposited in the herbarium of VANF. Permanent pollen shapes of the family are very similar and difficult to separate slides for the pollen reference collection have been deposited at the from one another. Microscopic dissimilarity has to be used to identify Department of Biology, Faculty of Science, Yüzüncü Yıl University, the differences. Van, Turkey. The pollen slides were prepared using the technique In this study, pattern recognition methods have been used to de- of Wodehouse (1935). The material used to prepare the slides was termine the type of pollen grains. Computerized pattern recognition glycerin-jelly mixed with 1% safranin. The prepared slides were studied techniques are used in a wide range, such as analysis of biological under an Olympus CX31 light microscope, using oil immersion. The signals in medicine, voice recognition, security systems to recognize measurements were based on 30 readings from each specimen. Resolu- a face or fingerprint, trading robots or machines in seeing and recog- tions of the images were 710×720 pixels. Morphological measure- nizing objects, and the analysis of remotely sensed images. ments were made with a stream software program. Polar axis, Any machine learning methods could be used to classify pollen equatorial axis, P/E ratio, colpus length, colpus weight, exine, intine, grains based on morphological features. However, in this study, an tectine, nexine, columellae, and echinae length were also measured automatic pollen recognition system, based on the rough set (RS) (Fig. 1) Terminology was used according to Erdtman (1969), Faegri approach has been proposed. The RS is a mathematical approach and Iversen (1975),andPunt et al. (2007). developed by Pawlak, which is used for different purposes, such as the selection of attributes, the implication of attributes, the reduction 2.3. Analyze method of attributes, the implication of decision-making rules, and pattern recognition (Pawlak, 1995; Yumin et al., 2010). The other advantages 2.3.1. The rough set theory of the RS method; be able to identify the most effective morphological The rough set theory (RST), was introduced by (Pawlak, 1982). It features, disposal of redundant features from data set, and creation is an important technique for the identification and recognition of of effective decision rules based on morphological features that are common patterns in uncertain and incomplete data. It proved to be effective on classification. an excellent mathematical tool to deal imprecise data in various The purpose of this study is to develop a computer-based identifi- domains. Some basic of definitions in RS theory are given as follows. cation method for classifying and identifying the pollen grains of 20 taxa belonging to the Asteraceae family. The classification and identi- 2.3.2. Information system fication of pollen can be more accurately performed with automatic In rough set theory (RST), the data set is collected as a table called the diagnostic systems, and the recognition process will also take place information system. Information systems are defined as S=(U,Q,V), more quickly, with less cost. Such automatic systems will allow for where U={x1, x2, ….