• Korean Journal of Soil Science and Fertilizer
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• v.22 no.3
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• pp.173-179
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• 1989

Characterization and classification of the potential acid sulfate soils found on flood-plains in Yeongnam area were summarized as follows: 1. The "Potential acid sulfate soil" layer(s) were appeared in the around 2-4m substrata of soil profiles and characterized by the fine texture, high reduction and physical unripened soft mud deposits or having higher contents of organic matter with dark color. 2. The contents of total sulfur (T-S) in those soils were ranged around 0.45-0.9% and the materials exhibited a strong acidity upon the oxidation with $H_2O_2$. Although the T-S contents was low as much as 0.15%, the sulfidic materials were also acidified strongly by the oxidation with $H_2O_2$ in the condition of lower content of carbonates. As defined in Soil Taxonomy of USDA, most of the sulfidic materials contained less than 3 times carbonate ($CaCO_3$ equivalent wt. %), but there were some which abundant in shell fragments, contained more than 3 times carbonate by weight percentage and that not much acidified by the oxidation with $H_2O_2$. 3. The contents of T-S correlated negatively with the pH oxidized by $H_2O_2$ and with the fizzing time (minutes) due to addition of $H_2O_2$. 4. The potential acid sulfate soils could be defined as soil materials that had sulfidic layer(s) more than 20cm thick within 4m of the soil profile and contained more than 0.15% of T-S with less than 3 times carbonate ($CaCO_3$ equiv. %). A tentative interpretative soil classification system was proposed, i.e., "Weak potential acid sulfate (T-S, 0.15-0.5%)", "Moderate potential acid sulfate (T-S, 0.5-0.75%)", and "Strong potential acid sulfate (T-S, more than 0.75%)". Finally, it was proposed that the "Detailed soil survey with high intensity" should be carried out in the areas of agricultural engineering works such as arableland readjustment works, in advance.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.1
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• pp.74-80
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• 1989

Twentyseven rice cultivars were compared Quantitatively and classified by 15 agronomic traits. For this, 12 Tongil-type rice cultivars and 15 Japonica-type rice cultivars were transplanted at the experimental farm of the Yeongnam Crop Experiment Station on May 25 in 1987. Jangseongbyeo belonging to Tongil-type had the greatest dissimilarity total among 27 rice cultivars and followed by Chilseongbyeo, Gayabyeo, Hangangchalbyeo, Gihobyeo (Tongil-type) and Sangpoongbyeo (Japonica-type) while cultivars having greatest similarity total were Daecheongbyeo and Yeongsanbyeo and followed by Nagdongbyeo, Sinsunchalbyeo, Gwangmyeongbyeo, Yeongdeogbyeo, Palgongbyeo and Dongjinbyeo that were all belonging to Japonica-type cultivars. CuItivar pairs having the greatest dissimilarity coefficient (50-55％) were combinations of Gayabyeo and Sangpungbyeo, Gihobyeo, or Seonambyeo, combinations of Chilseongbyeo and Jangseongbyeo, Sangpung-byeo, Chucheongbyeo, Gihobyeo or Seonambyeo and combinations of }angseongbyeo and Gwangmyeong-byeo, Sangpungbyeo, Gihobyeo, Daechangbyeo or H waseongbyeo. On the other hand, cultivar pairs having the greatest similarity coefficient (above 85 ％) were combinations of Dongjinbyeo and Sinsunchalbyeo, Daecheongbyeo, Somjinbyeo or Yeongsanbyeo, combination of Sangpungbyeo and Gihobyeo, combinations of Yeongdeogbyeo and Yeongsanbyeo or Palgongbyeo, combination of Gwangmyeongbyeo and Yeongdeogbyeo, Combinations of Nagdongbyeo and Gwangmyeongbyeo, Yeongdeogbyeo, Daecheongbyeo, Hwaseongbyeo or Yeongsanbyeo, combinations of Sinsunchalbyeo and Daecheongbyeo or Yeongsanbyeo, combinations of Somjinbyeo and Daecheongbyeo or Yeongsanbyeo and combination of Daecheongbyeo nd Yeongsanbyeo. Two-dimensional ordination analysis method provided the information on cultivar classification between Japonica-type and Tongil-type cultivars except a few cultivars. Only two cultivars, Joongweonbyeo and Seomjinbyeo were located opposit area that implied Tongil-type cultivar Joongweonbyeo was rather close to Japonica-type while Japonica-type cultivar Seomjinbyeo was rather close to Tongil-type, respectively. Sinsunchalbyeo and Daecheongbyeo, on the other hand, were located on the line between two cultivar groups. The method of two-dimensional ordination analysis concluded as feasible approach to classify cultivars Quantitatively for the practical purpose.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.62 no.2
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• pp.149-155
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• 2017

To establish cultivation areas for the stable production of yacon, this study investigated the productivity and functional component contents of yacon in eight regions of Korea from 2011 to 2013. The results of principal component analysis using these data were as follows. A survey of 16 agricultural traits and meteorological data in the eight yacon cultivation areas showed that five factors (average temperature, maximum temperature, minimum temperature, frost-free days, and fructooligosaccharide content) were highly significant at the p < 0.001 level. Among the 16 agricultural traits and meteorological data used in the main component analysis of yacon cultivation areas, approximately eight contributed to the first principal component, and approximately four contributed to each of the second and third principal components. In particular, factors related to productivity, fructooligosaccharide content, and temperature change were considered important criteria for the classification of cultivation areas. The cultivation areas were divided into three groups by principal component analysis. In Group I, containing the Jinbu and Bonghwa areas in the mid-highland region at 500-560 m above sea level, the product yield was the highest at 2,622-3,196 kg/10a, the fructooligosaccharide content was also the highest at 9.04-9.62%, and the mean temperature was $17.3-18.5^{\circ}C$. In Group II, the areas Suncheon, Okcheon, Yeoju, and Gangneung, at 20-180 m above sea level, had the lowest yield, relatively lower fructooligosaccharide content, and the highest temperature. The areas in Group III showed values intermediate between those of Group I and Group II. For the different yacon cultivation areas, the product quantity and fructooligosaccharide content differed according to the environmental temperature, and the temperature conditions and number of frost-free days are considered important indicators for cultivation sites. Therefore, in terms of producing yacon with high quality, cultivation at 500-560 m is considered to give a higher yield and functional fructooligosaccharide content.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.521-535
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• 2023

When converting rice fields into fields,sorghum (sorghum bicolor L. Moench) has excellent moisture resistance, enabling stable production along with soybeans. Therefore, it is a crop that is expected to improve the self-sufficiency rate of domestic food crops and solve the rice supply-demand imbalance problem. However, there is a lack of fundamental statistics,such as cultivation fields required for estimating yields, due to the traditional survey method, which takes a long time even with a large manpower. In this study, U-Net was applied to RGB images based on unmanned aerial vehicle to confirm the possibility of non-destructive segmentation of sorghum cultivation fields. RGB images were acquired on July 28, August 13, and August 25, 2022. On each image acquisition date, datasets were divided into 6,000 training datasets and 1,000 validation datasets with a size of 512 × 512 images. Classification models were developed based on three classes consisting of Sorghum fields(sorghum), rice and soybean fields(others), and non-agricultural fields(background), and two classes consisting of sorghum and non-sorghum (others+background). The classification accuracy of sorghum cultivation fields was higher than 0.91 in the three class-based models at all acquisition dates, but learning confusion occurred in the other classes in the August dataset. In contrast, the two-class-based model showed an accuracy of 0.95 or better in all classes, with stable learning on the August dataset. As a result, two class-based models in August will be advantageous for calculating the cultivation fields of sorghum.

• Korean Journal of Environmental Agriculture
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• v.25 no.2
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• pp.147-156
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• 2006

This study was conducted to investigate natural distribution of aquatic plane and to find out natural aquatic plants which highly absorb nutrient N and P. We surveyed vegetation within ${\pm}2m$ from streamside in 12 tributaries of the West Nakdong river watershed during May to October in 2003. Hydrophytes surveyed in tributaries of the West Nakdong river watershed were 27 families, 61 genera, 76 species, 3 varieties. Major dominance species of aquatic plants were Z. latifolia, P. communis, P. thunbergii, P. arundinacea, P. japonica, and P. distichum var. indutum. Aquatic plants having high production ability of biomass were Z. latifolia, P. communis, P. arundinacea, P. japonica, and E. crus-galli var. echinata. In the vertical distribution of hydrophytes within streams, dominant species were P. thunbergii and P. japonica in the upper stream, but dominant species in the downstream were P. communis and Z. latifolia. Species diversity or aquatic, plants was reduced, but their biomass and nutrient (T-N and T-P) content per the natural area unit $(m^2)$ were increased in the downsteaam. Nutrient N and P content of aquatic plants per the natural area unit were high at Joman river, Pyeonggangcheon, Bulam drainage canal, and Hogyecheon. Fifty-seven species of aquatic plants having high biomass were grounped into 4 categories $(I{\sim}IV)$ according to their nutrient content per dry weight unit. I group $(T-N,\;\geqq20gkg^{-1}\;&\;P_2O_5,\;\geqq7gkg^{-1})$ was comprised of 3 submerged plants (H. verticillata, P. crispus, and C. demersum), e emergent plants (O. javanica, P. distichum var. indutum, and R. sceleratus), 1 suspended plant (T. japonica), and 1 riparian plant (A. lobatum). Otherwise, in classification of natural hydrophytes according to their nutrient content per natural area unit, Z. latifolia, P. communis, P. longiseta, P. arundinacea, and P. distichum var. indutum possessing great biomass productivity as emergent plants were included in I group $(T-N,\;\geqq1gm^{-2}\;&\;P_2O_5,\;\geqq0.7gm^{-2})$.

• Korean Journal of Soil Science and Fertilizer
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• v.17 no.2
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• pp.125-133
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• 1984

To obtain the basic date for the improvement of cultural and managemental problems caused by soil characteristics and soil productivity in rice cultivation of Honam area, morphological characteristics of rice soils were investigated in Mangeng-Dongjin and Yeongsan Water-sheds, and compaired differences between two major Watersheds. The results obtained are summarized as follows: 1. According to U.S.D.A. Soil Taxonomy Classification System, eight great groups are distributed in rice soils of two major Watersheds. More than 50% of rice paddy soils are classified as Haplaquepts. 2. Two Watersheds are quite different in soil parent materials. In Mangeong-Dongjin Watershed, most soils (55.1%) are derived from fluvic-marine deposits. Remainders are derived from local alluvium (24.7%) and alluvium (14.2%). But in Yeongsan Watershed, the order is local alluvium>alluvium>fluvio-marine deposits. 3. Rice soils occur mostly in coastal and inland flat-site with the slope of less than 2% (57.8%) in Mangeong-Dongjin Watersheds. However, in Yeongsan Watershed, flat-site and low undulating terrace are mostly distributed (52.9%). 4. About 81.9, 61.4 and 53.3% of rice soils are classified as fine textured in Yeongsan, Dongjin, and Mangeong Watersheds, respectively. 5. More normal paddy soils and less sandy paddy soils are distributed in Yeongsan Watershed. The results indicate that more rice soils are classified as productivity classes of I and II in Yeongsan Watershed than in Mangeong-Dongjin Watersheds.

• Korean Journal of Soil Science and Fertilizer
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• v.41 no.6
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• pp.387-392
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• 2008

It is very important to characterize five major properties of topography, drainage class, soil texture, available soil depth, and gravel content for soil survey. We used multidimensional scaling method for analyzing five major properties for the soils originated from sedimentary rocks to understand their relationships. We simplified 5 major characteristics on soils derived from sedimentary rocks. That is, topographic factor was 15 to 9, soil texture was 32 to 6, drainage class was 6 to 5, available depth was 4, and gravel content was 3. For the viewpoint of eigenvector, from dimension 2, 3 to dimension 1, 4, mountain soils and more fine soils dominated. By eigenvalue, there was no tendency, but in details, was some tendency between small groups. Like this, closely observe exceptional distribution of soils, we need improved intra-group homogeneity based on weight control of soil factor, addition and subtraction of soil factors. Also, we carefully analyzed soil characteristics involved intra-group, then we need reconsideration of past classification units.

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.2
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• pp.59-64
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• 2006

To analyze the changes of soil physico-chemical properties after reclamation, we carried out an experiment for 75 samples of representative saline soils in South Korea. The more the years after reclamation is proceeded, the blighter the soil color is, soil horizon differentiation and structure is developed, but electrical conductivity(EC) decreased. After the soil survey, coarse textured soils were more quickly de-salined than the fine textured soils. In case of fine loamy textured Poseung series, it could be estimated that the series had Salic horizons in sub-order level of taxonomical classification. In other case of coarse loamy textured soil series, it could be estimated that the series had Sodic properties in sub-group level. Sodium contents of fine loamy textured soils were not decreased after reclamation, but those of coarse loamy textured Gwanghwal series reclaimed about 76 years ago were reclassified because of desalinization. To be desalined low to 4 dSm-1 of EC, it presumably takes about 108, or 12 years for fine loamy and coarse loamy textured soils, respectively.

• Weed & Turfgrass Science
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• v.3 no.3
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• pp.165-173
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• 2014

This review study was conducted to recommend the effective use of herbicide mixtures in Korea. The herbicide ingredients by Herbicide Resistancce Action Committee (HRAC) was classified into 23 groupes according to the mode of action (acetyl CoA carboxylase inhibitors, acetolactate synthase, photosystem I and II inhibitors, protoporphyrinogen oxidase inhibitors, carotenoid biosynthesis inhibitors, enolpyruvyl shikimate-3-phosphate synthase inhibitors, glutamine synthetase inhibitors, dihydropteroate synthetase inhibitors, mitosis inhibitors, cellulose inhibitors, oxidative phosphorylation uncouplers, fatty acid and lipid biosynthesis inhibitors, synthetic auxins, auxin transport inhibitors and potential nucleic acid inhibitors or non-descript mode of action). The rice herbicide mixtures registered in Korea were classified based on the guideline of HRAC. Accordingly, such a classification system for resistance management can help to avoid continuous use of the herbicide having the same mode of action in the same field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.7-14
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• 2021

Tomato crops are highly affected by tomato diseases, and if not prevented, a disease can cause severe losses for the agricultural economy. Therefore, there is a need for a system that quickly and accurately diagnoses various tomato diseases. In this paper, we propose a system that classifies nine diseases as well as healthy tomato plants by applying various pretrained deep learning-based CNN models trained on an ImageNet dataset. The tomato leaf image dataset obtained from PlantVillage is provided as input to ResNet, Xception, and DenseNet, which have deep learning-based CNN architectures. The proposed models were constructed by adding a top-level classifier to the basic CNN model, and they were trained by applying a 5-fold cross-validation strategy. All three of the proposed models were trained in two stages: transfer learning (which freezes the layers of the basic CNN model and then trains only the top-level classifiers), and fine-tuned learning (which sets the learning rate to a very small number and trains after unfreezing basic CNN layers). SGD, RMSprop, and Adam were applied as optimization algorithms. The experimental results show that the DenseNet CNN model to which the RMSprop algorithm was applied output the best results, with 98.63% accuracy.