• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1653-1671
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• 2022

Crop diseases affect crop production, more than 30 billion USD globally. We proposed a classification study of crop species and diseases using deep learning algorithms for corn, cucumber, pepper, and strawberry. Our study has three steps of species classification, disease detection, and disease classification, which is noteworthy for using captured images without additional processes. We designed deep learning approach of deep learning convolutional neural networks based on Mask R-CNN model to classify crop species. Inception and Resnet models were presented for disease detection and classification sequentially. For classification, we trained Mask R-CNN network and achieved loss value of 0.72 for crop species classification and segmentation. For disease detection, InceptionV3 and ResNet101-V2 models were trained for nodes of crop species on 1,500 images of normal and diseased labels, resulting in the accuracies of 0.984, 0.969, 0.956, and 0.962 for corn, cucumber, pepper, and strawberry by InceptionV3 model with higher accuracy and AUC. For disease classification, InceptionV3 and ResNet 101-V2 models were trained for nodes of crop species on 1,500 images of diseased label, resulting in the accuracies of 0.995 and 0.992 for corn and cucumber by ResNet101 with higher accuracy and AUC whereas 0.940 and 0.988 for pepper and strawberry by Inception.

• Journal of Ecology and Environment
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• v.35 no.2
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• pp.91-98
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• 2012

This study was performed in order to suggest the possibility of tree species classification using high-resolution QuickBird-2 images spectral characteristics comparison(digital numbers [DNs]) of tree species, tree species classification, and accuracy verification. In October 2010, the tree species of three conifers and eight broad-leaved trees were examined in the areas studied. The spectral characteristics of each species were observed, and the study area was classified by image classification. The results were as follows: Panchromatic and multi-spectral band 4 was found to be useful for tree species classification. DNs values of conifers were lower than broad-leaved trees. Vegetation indices such as normalized difference vegetation index (NDVI), soil brightness index (SBI), green vegetation index (GVI) and Biband showed similar patterns to band 4 and panchromatic (PAN); Tukey's multiple comparison test was significant among tree species. However, tree species within the same genus, such as $Pinus$ $densiflora-P.$ $rigida$ and $Quercus$ $mongolica-Q.$ $serrata$, showed similar DNs patterns and, therefore, supervised classification results were difficult to distinguish within the same genus; Random selection of validation pixels showed an overall classification accuracy of 74.1% and Kappa coefficient was 70.6%. The classification accuracy of $Pterocarya$ $stenoptera$, 89.5%, was found to be the highest. The classification accuracy of broad-leaved trees was lower than expected, ranging from 47.9% to 88.9%. $P.$ $densiflora-P.$ $rigida$ and $Q.$ $mongolica-Q.$ $serrata$ were classified as the same species because they did not show significant differences in terms of spectral patterns.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.467-469
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• 2003

This research aimed to develop forest type classification technique for the mixed forest with coniferous and broad-leaved species using the high resolution satellite data. QuickBird data was used as satellite data. The method of this research was to extract satellite data for every single tree crown using image segmentation technique, then to evaluate the accuracy of classification by changing grouping criteria such as tree species, families, coniferous or broad-leaved species, and timber prices. As a result, the classification of tree species and families level was inaccurate, on the other hand, coniferous or broad-leaved species and timber price level was high accurate.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.1-9
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• 2021

In this study, we evaluated the accuracy of deep learning-based tree species classification model trained by using high-resolution images. We selected five species classed, i.e., pine, birch, larch, korean pine, mongolian oak for classification. We created 5,000 datasets using high-resolution orthophoto and forest type map. CNN deep learning model is used to tree species classification. We divided training data, verification data, and test data by a 5:3:2 ratio of the datasets and used it for the learning and evaluation of the model. The overall accuracy of the model was 89%. The accuracy of each species were pine 95%, birch 89%, larch 80%, korean pine 86% and mongolian oak 98%.

• Journal of the Korean Wood Science and Technology
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• v.47 no.1
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• pp.101-109
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• 2019

This paper examines the classification of five coniferous species, including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cedar (Cryptomeria japonica), and cypress (Chamaecyparis obtusa), using near-infrared (NIR) spectra. Fifty lumber samples were collected for each species. After air-drying the lumber, the NIR spectra (wavelength = 780-2500 nm) were acquired on the wide face of the lumber samples. Soft independent modeling of class analogy (SIMCA) was performed to classify the five species using their NIR spectra. Three types of spectra (raw, standard normal variated, and Savitzky-Golay $2^{nd}$ derivative) were used to compare the classification reliability of the SIMCA models. The SIMCA model based on Savitzky-Golay $2^{nd}$ derivatives preprocessing was determined as the best classification model in this study. The accuracy, minimum precision, and minimum recall of the best model (PCA models using Savitzky-Golay $2^{nd}$ derivative preprocessed spectra) were evaluated as 73.00%, 98.54% (Korean pine), and 67.50% (Korean pine), respectively.

• Journal of Ecology and Environment
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• v.29 no.5
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• pp.425-432
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• 2006

The tadpoles of 12 Korean anuran species, including Bombina orientalis, Bufo gargarizans, B. stejnegeri, Hyla japonica, Kaloula borealis, Rana dybowskii, R. huanrenensis, R. coreana, R. nigromaculata, R. chosenica, R. rugosa, and R. catesbeiana, were classified based on their morphological characteristics. We collected eggs or tadpoles of the 12 Korean anuran species from Gangwon, Incheon, Chungcheong, and Gyeonggi districts in 2005 and 2006 breeding seasons. When the tadpoles reached at $27{\sim}37$ Gosner's developmental stages, we described morphological characteristics of the tadpoles of each anuran species and measured their physical parameters such as total length, body length, and body mass. After that, we chose 12 morphological characteristics to identify each species and to use them as classification keys such as eye location, caudal musculature pattern, spiracle location, oral disc morphology, and labial tooth row formula. In this paper, we presented classification keys, morphological characteristics, and drawings for the tadpoles of 12 anuran species.

• Korean Journal of Medicinal Crop Science
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• v.21 no.4
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• pp.268-275
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• 2013

This text was analyzed and investigated the distribution of medicinal plants in Cheongoksan Bonghwagun Korea, in order to search the medicinal resources that are used in modern medicine. Medicinal plants of the Korean Pharmacopoeia (10th edition) distributed in Cheongoksan Bonghwagun were consisted of 93 taxa ; 82 species, 10 varieties, 1 forma of 79 genus, 50 families. In medicinal plants of the Korean Pharmacopoeia, rate of native species and exotic species was 89.2% (83 taxa) and 10.8% (10 taxa) respectively. Family classification was the most of compositae of 8 taxa, and life form classification was most of herb of hemicryptophyte species. The classification by using parts were 34 taxa of root use and the classification of efficacy utilization was 24 taxa of Cheongyeolyak (heat-clearing drug) use.

• Archives of Pharmacal Research
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• v.27 no.12
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• pp.1270-1274
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• 2004

Random amplified polymorphic DNA (RAPD) analysis was used to determine the genetic relationships among seventeen species of the Acanthopanax species. The DNA isolated from the leaves of the samples was used as template in polymerase chain reaction (PCR) with twenty random decamer primers in order to distinguish plant subspecies at the level of their genomes. The RAPD patterns were compared by calculating pairwise distances using Dice similarity index, and produced to the genetic similarity dendrogram by unweighted pair-group method arithmetic averaged (UPGMA) analysis, showing three groups; a major cluster(twelve species), minor cluster (4 species) and single-clustering species. The results of RAPD were compatible with the morphological classification, as well as the chemotaxonomic classification of the Acanthopanax species. The Acanthopanax species containing 3,4-seco-lupane type triterpene compounds in their leaves corresponded to the major cluster, another species having oleanane or normal lupane type constituents to minor clusters, and one species not containing triterpenoidal compound to single-cluster.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.4
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• pp.489-497
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• 2021

Climate change is affecting the evolutionary trajectories of individual species and ecological communities, partly through the creation of new species groups. As population shift geographically and temporally as a result of climate change, reproductive interactions between previously isolated species are inevitable and it could potentially lead to invasion, speciation, or even extinction. Four species of abalone, genus Haliotis are present along the Korean coastline and these species are important for commercial and fisheries resources management. In this study, genetic markers for fisheries resources management were discovered based on genomic information, as part of the management of endemic species in response to climate change. Two thousand one hundred and sixty one single nucleotide polymorphisms (SNPs) were discovered using genotyping-by-sequencing (GBS) method. Forty-one SNPs were selected based on their features for species classification. Machine learning analysis using these SNPs makes it possible to differentiate four Haliotis species and hybrids. In conclusion, the proposed machine learning method has potentials for species classification of the genus Haliotis. Our results will provide valuable data for biodiversity conservation and management of abalone population in Korea.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.3
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• pp.69-84
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• 2021

This study was conducted as a basic data for the management of the Uiseong Traditional Irrigation Agricultural System by identifying plant diversity and distribution characteristics. The total number of plant taxa was identified as 88 families, 250 genera, 368 species, 7 subspecies, 9 varieties and 384 taxa. In the case of life form, the domancy form was in the therophytes(th), the radicoid form was a R₅(monophyte), the disseminule form was the gravity D₄(having no special modification for dissemination), and the growth form was the erect form was high. The number of plant taxa by land use type was higher in mountainoustype reservoirs and plain type reservoirs than other land use types. The distribution of plants by land use type according to the hemeroby grade was plantation in the case of 3 grade(meso-hemeroby) forests, and the understory were mainly photophilic plants. With 4 grade(β-euhemeroby), traditional cemetery, plain type reservoirs, mountainoustype reservoirs, stream, and rice terraces are areas with relatively low intensive management and have a wide variety of flora, whereas 5 grade(α-euhemeroby) orchard were mainly distributed with ruderal plant due to high intensive management. As for the number of plant taxa by reservoirs, the Wisgol pond in the case of plain type reservoirs, Ungok pond was high in mountainous type reservoirs. The protected species were rare plants 2 classification groups of Vulnerable(VU) species, 4 classification groups of Least Concrned(LC) species, 1 classification group of Data Deficient(DD) species, 5 classification groups of Korean endemic plants, and 49 classification groups of invasive alien plants, and the total naturalization index was 12.2%.