Review of Palaeobotany and Palynology 189 (2013) 50–56

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Review of Palaeobotany and Palynology

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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, ….. xn} is called universe, that consists of a finite set the opportunity to work with more pollen grains, with less need for of objects. Q is a finite set of attributes. Q consists of condition and deci- an experienced expert's information for the identification of the sion attributes, Q=A∪d,whereA is condition attributes and d is decision pollen types. The development of these systems will contribute to attribute. V ¼ ∪V aa∈A is called value set of a (Yu et al., 2006). the identification of the species of other families and provide new techniques for plant taxonomy and palynology. 2.3.3. Indiscernibility relation Every set of attributes (A) determines an equivalence relation 2. Material and methods in the universe (U). With any BA, there is an associated equivalence relation. This relation is referred to as a B-indiscernibility relation and can 2.1. Determination of the distinctive characteristics be denoted by IND(B), which partitions the universe (U) into a set of

equivalence classes {X1,X2,…,Xn}, which is commonly referred to as a In this study, 11 different microscopic characters (polar axis (P), classification and denoted by U/IND(B). The equivalence classes of the equatorial axis (E), P/E, exine, intine, tectine, nexine, columellea, B-indiscernibility relation are denoted by [x]B.TheB-indiscernibility rela- colpus L, and colpus W), obtained from 20 different pollen grains, tion is defined as follows (Yang and Wu, 2009): were used for the analysis. The characters were chosen according to features' for the classifications of Onopordum family. The long axis of the pollen grain is called the polar axis and the short one is called ; ∈ : ∀ ∈ ; INDðÞ¼ A fgððÞx1 x2 UxU a B axðÞ¼1 axðÞ2 1Þ the equatorial axis. Equatorial axis often inappropriately used as a synonym of equatorial diameter (Punt et al., 2007). The shape of the pollen grain is determined by the ratio of these axis measurements.

The pollen wall (exine) is designed to protect the sperm nucleus Where IND(B) is called the B-indiscernibility relation. If (x1,x2)∈IND(B), from desiccation and irradiation during transport from the anther to then records x1 and x2 are indiscernible from each other by attributes the stigma. The exine is separated to 3 different sections, the tectine, from B attributes (Toshiko et al., 2006). nexine, and columellae, and these can be of different thicknesses. Pollen may have aperture and opening on the exine layer, and in 2.3.4. Set approximations this study, we used the colpus length and weight for the determina- Though some objects in an information table cannot be exactly tion of the aperture structure. A cellulose wall (intine) lies within distinguished given the set of attributes, they could be approximately the outer pollen wall, which is made of the biopolymer sporopollenin. distinguished. Because of this, the data set is defined as a pair of sets, The exine and intine ratio is an important distinctive character and i.e. lower and upper approximations. The lower approximation of X is 52 Y. Kaya et al. / Review of Palaeobotany and Palynology 189 (2013) 50–56

Fig. 1. The measurement region of pollen grains. a set of objects of U, that is surely X, and for B attributes B-lower form: IF α THEN β. Here α denotes a conjunction of descriptors that approximation is defined as (Duoqian et al., 2009): only involve attributes of some reduct (rule's antecedent) and let β

n o (rule's consequent) denote a descriptor decD=v,whereD is a decision ∪ ∈ p : attribute and v is the allowed decision value (Frank and Hans, 2004). BX ¼ xi Ux½i INDðÞ B X ð2Þ

2.3.7. Proposed method for pollen classification The upper approximation of X is the set of objects of U that are Classification is an important theme in machine learning. Classi- possibly in X, for B attributes B-upper approximation defined as: fication is a data mining technique used to predict group member- n o ship for data instances. Rough sets are a common technique B X ¼ ∪ x ∈Ux½ ∩X≠φ : ð3Þ i i INDðÞ B applied to data mining problems. The block diagram pertaining to the model used in this study is shown in Fig. 2. The study consists These definitions state that objects x∈ BX, belong certainly to of 4 blocks. The processes in these blocks can be briefly summarized X, while records x∈BX could belong to X. The difference between as follows. BX and BX is called the B-boundary region of X and is denoted as follows: Block 1: Obtaining 30 images belonging to the different positions of the pollen foreach of the 20 species. − : BNBðÞ¼X BX BX ð4Þ Block 2: Obtaining morphological characteristic measurements in Table 1 from eachpollen image. The BNB(X) consists of objects that do not certainly belong to X on Block 3: The feature reduction process was accomplished for the set of B attributes. The positive region POSB(d) that joins BX, con- pollen data set. Themost effective features were chosen in the fi tains all objects of U that can be certainly classi ed to the decision pollen identification. classes by using the B attributes. POS (d) is shown below (Edita B Block 4: Creation of the decision rules database using a reduced fea- et al., 2010; James and Gwo-Hshiung, 2010): ture data set. The new pollen was classified according to the rules in the database. ∪ : POSBðÞ¼d BXi ð5Þ In this study, 30 pollen are used for each of the 20 taxa. The size of ∈ = Xi U INDðÞ B the information system consists of 600 instances. The decision rules are derived from 440 examples of 600 instances. The remaining 160 samples were used to test the model developed by the RS. 2.3.5. Reduction of attributes Feature reduction is defined as the process of selecting suitable fea- 3. Results tures from feature set in order to explain the information system through minimum feature number. Including BA,ifPOS(B)=POS(A), 3.1. Measurement results information system can be explained with B that is formed of less feature number. Moreover, an information system may have more In the evaluation of the 11 different microscopic measurements than one reducted feature set. The set obtained from the intersection for each pollen grain, it has been noticed that they are in fact very of reducted sets acquired by an information system means core attri- similar to one another and the standard deviations are very minor bute set of A feature set (Jinn-Tsai and Yi-Shih, 2007). degrees (Table 1). According to the measurements, the ornamenta- tion is echinate, the pollen type is 3-zonocolporate, and the pollen 2.3.6. Decision rules shape is oblate-spheroidal. As a data mining technique, one of the most important reasons for ap- plying rough sets is the generation of decision rules. When given an in- 3.2. Rough set classification results formation system, rough sets can generate decision rules for objects of known classes and predict classes to which new objects belong. The ex- In this study, an automatic pollen recognition system was proposed pression condA =v,whereA is an attribute and v is value of attribute, based on the RS approach. Microscopic and morphological characters of called the descriptor. Now, it is possible to investigate the rules of the the pollen images were extracted at the first stage of the proposed Y. Kaya et al. / Review of Palaeobotany and Palynology 189 (2013) 50–56 53

Fig. 2. Proposed method for pollen identification. method. In the next phase, the decision rules database was obtained to one another. For instance, when the P and E features are evaluated, using the reduced feature sets of the RS, after the redundant features there is a linear relationship between the P and E features. were removed, which have little effect on the automatic pollen recogni- Table 2 shows the rates of the correct classification of the pollen tion system. The most effective features for the identification of the grains. The diagonal of this table also shows the number of samples pollen grain type are P, E, colpus length, colpus width, exin, intini, classified correctly for each pollen grain. Of the 160 test pollen, 145 nexine, and columellae, from different 11 features and the distribution were correctly identified (Table 1). The overall success of the RS of these 8 features is shown in Fig. 3. The decision rules were extracted method in recognition of the pollen grains was 90.625% (145/160). using 8 features and the new pollen grains are classified according All 8 of the valued columns were classified exactly correct. The failure to these decision rules. Finally, 600 pollen of 20 different taxa were classifications were observed for the P3, P5, P10, and P20 pollen obtained for 30 different positions of each type of pollen. For the crea- grains. The reason for this misclassification of the pollen was because tion of the decision rules, 440 of 600 pollen grains were used and the all pollen grains are very similar at the species level. Part of the P3, remaining 160 pollen grains were for testing the proposed method, P10, and P20 pollen grains are classified as P8, P3, and P11. The according to the rules of the created decision rules. The classification similarity of the pollen grains that have been misclassified can be results are shown in Table 2. seen in Fig. 4. The diagonal of Fig. 3 represents the features that are used in iden- tifying the pollen grains. The graphs below and above the diagonal of 4. Discussion Fig. 3 were obtained by changing the places in the coordinate system. For instance, in the P and E feature, P is shown on the X-axis and E is Generally, the morphological characters are used for plant identifi- shown on the Y-axis, below the diagonal of Fig. 3. However, above the cation in the classical plant systematics. However, using morphological diagonal of Fig. 3, E is shown on the X-axis and P is shown on the characters alone leads to some complexity, especially for the identifica- Y-axis. These graphs represent the distribution of the features relative tion of species and subspecies. In revision studies in plant systematics,

Table 1 Measurement results of 20 different pollen grains.

P E P/E Colpus L. Colpus W. Exine Intine Tectine Echinae Nexine Columellae

P1 45.90±1.06 47.80±1.39 0.96±0.01 30.76±1.33 17.60±0.47 6.93±0.22 0.72±0.06 0.73±0.04 2.16±0.13 0.96±0.04 3.07±0.30 P2 50.04±0.80 53.04±0.75 0.94±0.01 31.96±1.15 19.28±0.89 7.28±0.37 0.77±0.05 0.71±0.05 2.55±0.11 0.96±0.03 3.05±0.37 P3 56.07±1.24 58.45±1.26 0.95±0.01 35.12±0.92 21.22±0.83 9.87±0.47 0.79±0.04 0.76±0.03 2.63±0.1 0.96±0.02 5.51±0.47 P4 50.12±0.85 52.21±0.93 0.96±0.010 32.31±1.51 18.82±0.80 8.36±0.07 0.74±0.03 0.75±0.02 2.51±0.13 0.95±0.03 4.14±0.14 P5 51.76±1.77 53.78±2.33 0.96±0.01 33.28±0.41 21.96±0.29 9.30±0.25 0.75±0.03 0.73±0.03 2.55±0.08 0.95±0.01 5.05±0.25 P6 58.01±0.65 59.94±0.85 0.96±0.01 35.05±0.85 21.20±0.57 10.25±0.12 0.75±0.04 0.73±0.03 2.58±0.08 0.97±0.03 5.95±0.15 P7 58.49±1.87 60.92±2.33 0.96±0.01 35.32±1.31 21.40±0.46 9.96±0.24 0.94±0.02 0.76±0.01 2.59±0.09 1.01±0.03 5.59±0.27 P8 57.81±1.39 60.08±1.46 0.96±0.01 35.76±0.93 21.97±0.59 9.72±0.13 0.87±0.02 0.77±0.03 2.59±0.09 1.01±0.03 5.33±0.17 P9 54.23±1.03 56.73±1.16 0.95±0.01 33.31±0.51 20.07±0.95 9.07±0.16 0.88±0.02 0.74±0.03 2.56±0.09 1.01±0.01 4.74±0.19 P10 56.6±1.01 57.83±0.93 0.97±0.01 34.79±0.81 20.63±0.37 9.67±0.24 0.87±0.03 0.76±0.02 2.59±0.06 1.01±0.02 5.29±0.26 P11 57.14±0.99 58.27±1.06 0.98±0.01 30.79±0.48 18.28±0.63 9.43±0.22 0.84±0.02 0.79±0.03 2.60±0.11 0.99±0.03 5.03±0.24 P12 57.52±1.53 59.19±1.37 0.97±0.01 32.62±0.27 19.69±0.41 9.69±0.23 0.84±0.03 0.82±0.03 2.51±0.11 1.00±0.03 5.35±0.26 P13 57.46±0.79 58.90±0.61 0.97±0.01 34.16±1.54 21.42±0.79 9.04±0.17 0.86±0.02 0.77±0.02 2.57±0.09 1.06±0.03 4.62±0.17 P14 51.21±1.03 53.70±1.06 0.95±0.01 34.65±0.93 17.62±0.87 5.96±0.35 0.79±0.04 0.70±0.04 2.08±0.08 0.85±0.10 2.33±0.32 P15 45.80±0.75 48.54±0.66 0.94±0.01 29.18±0.35 17.31±0.21 6.80±0.21 0.79±0.06 0.71±0.04 1.54±0.1 1.00±0.04 3.54±0.24 P16 51.69±1.09 52.85±1.15 0.97±0.01 32.20±0.56 19.07±0.46 8.93±0.29 0.80±0.03 0.79±0.04 2.46±0.09 1.12±0.03 4.55±0.33 P17 53.78±1.01 56.10±0.93 0.95±0.01 33.45±0.56 21.24±0.62 8.83±0.17 0.76±0.05 0.78±0.04 2.49±0.09 0.92±0.03 4.62±0.18 P18 51.01±0.76 53.03±0.72 0.96±0.01 30.87±0.66 17.80±0.46 8.43±0.12 0.73±0.03 0.69±0.03 1.96±0.11 0.93±0.02 4.82±0.16 P19 45.01±0.67 46.46±0.79 0.96±0.01 27.94±0.47 15.68±0.33 6.47±0.21 0.73±0.05 0.71±0.04 1.93±0.08 0.96±0.02 2.86±0.23 P20 51.62±0.58 53.73±0.81 0.96±0.01 31.70±0.59 17.73±0.49 8.91±0.25 0.75±0.03 0.70±0.04 2.13±0.11 0.99±0.03 5.07±0.28 54 Y. Kaya et al. / Review of Palaeobotany and Palynology 189 (2013) 50–56

Fig. 3. Distribution graph of features in reduced set. Y. Kaya et al. / Review of Palaeobotany and Palynology 189 (2013) 50–56 55

Table 2 Confusion matrix of 160 test pollen.

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20

P1 7 000000000 0 0 0 0 1 0 0 0 0 0 P20800000000 0 0 0 0 0 0 0 0 0 0 P30050000200 0 0 1 0 0 0 0 0 0 0 P40008000000 0 0 0 0 0 0 0 0 0 0 P50000610000 0 0 0 0 0 0 1 0 0 0 P60010070000 0 0 0 0 0 0 0 0 0 0 P70000008000 0 0 0 0 0 0 0 0 0 0 P80000000800 0 0 0 0 0 0 0 0 0 0 P90000000080 0 0 0 0 0 0 0 0 0 0 P100020000006 0 0 0 0 0 0 0 0 0 0 P110000000000 8 0 0 0 0 0 0 0 0 0 P120000000010 0 7 0 0 0 0 0 0 0 0 P130000000000 0 0 8 0 0 0 0 0 0 0 P140000000000 0 0 0 8 0 0 0 0 0 0 P150000000000 0 0 0 0 7 0 0 0 1 0 P160000000000 1 0 0 0 0 7 0 0 0 0 P170000100000 0 0 0 0 0 0 7 0 0 0 P180000000000 0 0 0 0 0 0 0 8 0 0 P190000000000 0 0 0 0 0 0 0 0 8 0 P200000000000 2 0 0 0 0 0 0 0 0 6

(P1. Onopordum polycephalum, P2. O. turcicum,P3.O.candidum, P4. O. armenum,P5.O.ilyricum,P6.O. carduchorum,P7.O. heteracanthum,P8.O. canum,P9.O. boissierianum, P10. O. myriacanthum,P11.O. barcteatum var. bracteatum,P12.O. barcteatum var. arachnoideum, P13. O. majori, P14. O. caricum,P15.O. davisii,P16.O. tauricum,P17.O. sibthorpianum, P18. O. anatolicum,P19.O. acanthium, P20. O. sirsangense). palynologic identifications, as well as molecular studies, are required for which pollen features were more effective for identification in the evaluation because of their distinctive properties. Therefore, accurate, Asteraceae (Onopordum) family. These determinations are essential expert, and specific identification of pollen grains is an important crite- criterion for palynologic studies. rion for the systematic evaluation of plant species. Morphologic and mi- croscopic features are different at the family level; however, evaluation 5. Conclusion of the differences among the same genus is more significant. The measurement results of the 20 different pollen grains were In recent years, many palynological studies were evaluated by com- very close to one another and had low standard errors. The pollen puter aided system. There is a lot of confusion in identification of plant grains of different families can be separated easily, thus successful species by classical taxonomy with morphologic characters however; classification results with these similarities encourage us to compare significance of these systems was appreciated by modern taxonomy pollen of different families and genuses. The rough set technique with computerized systems. Classic taxonomy cannot achieve to com- selected 8 features that were effective in identifying the pollen pare the all features of any living organism. We want to contribute the grains using 11 features. Moreover, in this study, we determined new techniques for modern classification. The computer based system will provide the accurate, rapid and reliable identification of pollen grains. In future studies, machine learning methods will be used for other identification fields.

Acknowledgment

This study has been supported by the YYU 2010-FBE-D131 and we thank to Lisaanne Meredith for kindly correcting the English version of the manuscript.

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