An expert classification system of pollen of Onopordum using a rough set approach

Although pollen grains have a complicated 3-dimensional structure, they can be distinguished from one another by their specific and distinctive characteristics. Using microscopic differences between the pollen grains, it may be possible to identify them by family or even at the genus level. However for the identification of pollen grains at the taxon level, we require expert computer systems. For this purpose, we used 20 different pollen 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 ratio, colpus length, colpus weight, exine, intine, tectum, nexine, columellea, and echinae length) were measured for the analysis. The data set was formed from 600 samples, obtained from 20 different taxa, with 30 different images. The 440 samples were used for training and the remaining 160 samples were used for testing. The proposed 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. ► We model an expert system for pollen identification by morphological features. ► We examine the differences between the pollen grains by rough set approach. ► Improving databases for palynologic studies based on microscopic features. ► To contribute new techniques based on computer system for modern classification.