The integration of machine learning for species identification is becoming increasingly important in entomological research. However, automatic species identification faces significant challenges such as low resolution, sample discoration, and small dataset sizes, which impede the reliability of traditional machine learning methods. Building upon the previous research on quantification of the color patterns of Stenaptinus occipitalis jessoensis using R-based analysis, this study demonstrates how to overcome these challenges in training machine learning for species identification. This approach allowed us to successfully classify geographic variations of S. occipitalis. Our results demonstrate the model's ability to identify these variations, despite the small size of the image datasets. This advancement shed some light on the potential of machine learning to identify morphological variation in highly polymorphic species.

The aim of this study was to introduce meadow landscape planting in Korea. The survey site was the grasslands on the mid mountain at Jeju Island in Korea. In all, 96 plant taxa consisting of 32 families, 84 genera, 78 species, and 18 varieties were recorded during the investigation of grass-dominating grassland at the study site. An analysis of the grass vegetation types in the survey area revealed their potential for meadow landscape planting. The formal characteristics of plants such as the stem, leaf, and flower were dominant in this grassland. The principal communities were Miscanthus sinensis var purpurascens, Themeda triandra var. japonica(Willd.)-Imperata cylindrica var. koenigii, and Zoysia japonica Steud. A total of 74 plant species were compatible for meadow planting in the area. However, 22 plant species of weeds, climbers, woody, exotic, and invasive plants were excluded in these study sites because the meadow vegetation development might be hampered by them. Following the meadow planting design concept for plants, investigated plants were classified into several categories with two main concept criteria: the design and functional layers. The design layer was subdivided into structural and seasonal layers. Likewise, the functional layer was divided further into ground cover and filler layers. The results of this study suggested that some useful plants can be selected from natural meadows at Jeju for the future target planting of grasslands in Korea.