• Korean Journal of Plant Taxonomy
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• v.39 no.1
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• pp.29-34
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• 2009

The Chrysanthemum zawadskii complex is demarcated from other species by having the white and pink ligulate flowers. Its morphological characters are greatly diversified, so that various classification systems have been suggested. The character of leaf thickness has been mentioned as the characteristic for recognizing some of infra-specific taxa within this complex. In this study, we used longitudinal leaf sections to investigate the leaf thickness and cell number of leaf blades of 13 populations including those of the members of the C. zawadskii complex, as well as 4 populations of the related species of C. boreale and C. indicum. From the result, it was clear that the leaves were thicker in populations of C. boreale, C. indicum and C. zawadskii complex (diminishing in that order), and that the leaves were composed of about 9 cell layers in all populations. Within the C. zawadskii complex, leaf shape and thickness varied among the populations. It was very interesting that the taxa with restricted distribution, like C. zawadskii var. tenuisectum, C. zawadskii var. alpinum, C. zawadskii var. lucidum, and C. zawadskii subsp. coreanum had a thicker leaves than found among widely occurring taxa. From this, leaf thickness is supposed to be an adaptation to the unique habitat of each population.

• Plant Resources
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• v.2 no.2
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• pp.75-80
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• 1999

Twenty two varieties of pecan including wild types were classified based on 6 characters measured by principal component analysis score distance. The results are summarized as fellow. Twenty two varieties were classified into 5 groups based in PCA score distance. Five groups were distinctly characterized by many morphological characters. Total variation could be explained by 51%, 95%, 99% with first, third and fifth principal components respectively. Varimax rotation of the factor loading of the first factors indicated that the first component was highly loaded with leaf characters, the second component with fruit characters, but fruit length was negative loaded. The second, the third and the fourths groups of cultivars had very close genetic parentage similarity.

• Journal of Plant Biology
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• v.10 no.1_2
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• pp.3-20
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• 1967

1. Comparative studies, the number, form and pattern of ramification, on the petiole stele types of 3 orders, 11 families, 41 genera and 104 species of ferns found in Korea were carried out. 2. The number, form and pattern of the ramified steles were found to be different according to the taxa studied. 3. The stele types of petiole may be classified as unibranch, bibranch, tribranch, and polybranch. The species belonging to each stele type were found to have similar embyrological characteristics among them. Therefore, it might be reasonable to assume that the stele type can be used as a basis for classifing family lines. 4. The number of ramified steles in the petiole were found to be in general agreement with that of the leaf traces, though a few exceptional cases were found. 5. It is well known that there is a large degree of disgreement among the taxanomists on the classification of ferns. The classification of ferns by means of petiole stele types may ease this difficulty in certain extent.

• Journal of the Korean Chemical Society
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• v.63 no.2
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• pp.78-84
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• 2019

High-resolution $^1H$ NMR spectroscopic technique has been widely used as one of the most powerful analytical tools in food chemistry as well as to define molecular structure. The $^1H$ NMR spectra-based metabolomics has focused on classification and chemometric analysis of complex mixtures. The principal component analysis (PCA), an unsupervised clustering method and used to reduce the dimensionality of multivariate data, facilitates direct peak quantitation and pattern recognition. Using a combination of these techniques, the various green teas and black teas brewed were investigated via metabolite profiling. These teas were characterized based on the leaf size and country of cultivation, respectively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.43 no.4
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• pp.205-208
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• 1998

Silica bodies (phytoliths) are becoming of wide use for pedology, archaeology, paleobotany and paleoecology in botany. This study investigated morphological differences of silica bodies in the lamina of wild, indica type, and japonica type rice. Phytoliths in the epidermis of lamina showed noticeable difference among tested plants. Besides, there were also significant differences in the shape and distribution of the silica bodies around stomata and trichomes. Silica bodies in the lamina of the rice plants could be used to classify subspecies of Oryza genus.

• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.86-87
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• 2004

• Smart Media Journal
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• v.8 no.3
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• pp.31-40
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• 2019

To understand how a Convolutional Neural Network (CNN) model captures the features of a pattern to determine which class it belongs to, in this paper, we use Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize and analyze how well a CNN model behave on the CNU weeds dataset. We apply this technique to Resnet model and figure out which features this model captures to determine a specific class, what makes the model get a correct/wrong classification, and how those wrong label images can cause a negative effect to a CNN model during the training process. In the experiment, Grad-CAM highlights the important regions of weeds, depending on the patterns learned by Resnet, such as the lobe and limb on 미국가막사리, or the entire leaf surface on 단풍잎돼지풀. Besides, Grad-CAM points out a CNN model can localize the object even though it is trained only for the classification problem.

• Korean Journal of Plant Resources
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• v.19 no.1
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• pp.174-179
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• 2006

A cDNA library was constructed from leaf samples of 4-year-old Panax ginseng cultured in a field. 3,000 EST from a size selected leaf cDNA library were analyzed. The 349 of 2,896 cDNA clones has related with energy metabolism genes. The 349 known genes were categorized into nine groups according to their functional classification, aerobic respiration(48.4%), accessory proteins of electron transport and membrane associated energy conservation(17.2%), glycolysis and gluconeogenesis(3.4%), electron transport and membrane associated energy conservation(2.9%), respiration(2.0%), glycolysis methylglyoxal bypass(1.7%), metabolism of energy reserves(0.6%) and alcohol fermentation(0.3%).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3708-3728
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• 2021

Indian herbal plants are used in agriculture and in the food, cosmetics, and pharmaceutical industries. Laboratory-based tests are routinely used to identify and classify similar herb species by analyzing their internal cell structures. In this paper, we have applied computer vision techniques to do the same. The original leaf image was preprocessed using the Chan-Vese active contour segmentation algorithm to efface the background from the image by setting the contraction bias as (v) -1 and smoothing factor (µ) as 0.5, and bringing the initial contour close to the image boundary. Thereafter the segmented grayscale image was fed to a leaky capacitance fired neuron model (LCFN), which differentiates between similar herbs by combining different groups of pixels in the leaf image. The LFCN's decay constant (f), decay constant (g) and threshold (h) parameters were empirically assigned as 0.7, 0.6 and h=18 to generate the 1D feature vector. The LCFN time sequence identified the internal leaf structure at different iterations. Our proposed framework was tested against newly collected herbal species of natural images, geometrically variant images in terms of size, orientation and position. The 1D sequence and shape features of aloe, betel, Indian borage, bittergourd, grape, insulin herb, guava, mango, nilavembu, nithiyakalyani, sweet basil and pomegranate were fed into the 5-fold Bayesian regularization neural network (BRNN), K-nearest neighbors (KNN), support vector machine (SVM), and ensemble classifier to obtain the highest classification accuracy of 91.19%.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.959-968
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• 2019

In the agricultural industry, wireless sensor network technology has being applied by utilizing various sensors and embedded systems. In particular, a lot of researches are being conducted to diagnose diseases of crops early by using sensor network. There are some difficulties on traditional research how to diagnose crop diseases is not practical for agriculture. This paper proposes the algorithm which enables to investigate and analyze the crop leaf image taken by image camera and detect the infected area within the image. We applied the enhanced k-means clustering method to the images captured at horticulture facility and categorized the areas in the image. Then we used the edge detection and edge tracking scheme to decide whether the extracted areas are located in inside of leaf or not. The performance was evaluated using the images capturing tomato leaves. The results of performance evaluation shows that the proposed algorithm outperforms the traditional algorithms in terms of classification capability.