• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.3
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• pp.137-145
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• 2022

Aluminum (Al) is one of the major factors adversely affects crop growth and productivity in acidic soils. In this study, the effect of Al on plants in soil was investigated by comparing the protein expression profiles of alfalfa roots exposed to Al stress treatment. Two-week-old alfalfa seedlings were exposed to Al stress treatment at pH 4.0. Total protein was extracted from alfalfa root tissue and analyzed by two-dimensional gel electrophoresis combined with MALDI-TOF/TOF mass spectrometry. A total of 45 proteins differentially expressed in Al stress-treated alfalfa root tissues were identified, of which 28 were up-regulated and 17 were down-regulated. Of the differentially expressed proteins, 7 representative proteins were further confirmed for transcript accumulation by RT-PCR analysis. The identified proteins were involved in several functional categories including disease/defense (24%), energy (22%), protein destination (9%), metabolism (7%), transcription (5%), secondary metabolism (4%), and ambiguous classification (29%). The identification of key candidate genes induced by Al in alfalfa roots will be useful to elucidate the molecular mechanisms of Al stress tolerance in alfalfa plants.

• Research in Plant Disease
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• v.19 no.4
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• pp.265-272
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• 2013

Totally sixty three bacteria were isolated from lower stems showing symptoms of bacterial wilt on pepper plants in 14 counties of 7 provinces, Korea. The isolates showed strong pathogenicity on red pepper (cv. Daewang) and tomato (cv. Seogwang) seedlings. All virulent bacteria were identified as Ralstonia solanacearum based on colony types, physiological and biochemical tests and polymerase chain reaction (PCR). All R. solanacearum isolates from peppers were race 1. The bacterial isolates consisted of biovar 3 (27%) and biovar 4 (73%). Based on polymorphic PCR bands generated by repetitive sequence (rep-PCR), the 63 R. solanacearum isolates were divided into 12 groups at 70% similarity level. These results will be used as basic materials for resistant breeding program and efficient control against bacterial wilt disease of pepper.

• 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.

• Korean Journal of Environment and Ecology
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• v.33 no.1
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• pp.9-15
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• 2019

Artiodactyla, which is an even-toed mammal, widely inhabits worldwide. In recent years, wild Artiodactyla species have attracted public attention due to the rapid increase of crop damage and road-kill caused by wild Artiodactyla such as water deer and wild boar and the decrease of some species such as long-tailed goral and musk deer. In spite of such public attention, however, there have been few studies on Artiodactyla in Korea, and no studies have focused on the trend analysis of Artiodactyla, making it difficult to understand actual problems. Many recent studies on trend used text-mining and co-occurrence analysis to increase objectivity in the classification of research subjects by extracting keywords appearing in literature and quantifying relevance between words. In this study, we analyzed texts from research articles of three countries (Korea, China, and Japan) through text-mining and co-occurrence analysis and compared the research subjects in each country. We extracted 199 words from 665 articles related to Artiodactyla of three countries through text-mining. Three word-clusters were formed as a result of co-occurrence analysis on extracted words. We determined that cluster1 was related to "habitat condition and ecology", cluster2 was related to "disease" and cluster3 was related to "conservation genetics and molecular ecology". The results of comparing the rates of occurrence of each word clusters in each country showed that they were relatively even in China and Japan whereas Korea had a prevailing rate (69%) of cluster2 related to "disease". In the regression analysis on the number of words per year in each cluster, the number of words in both China and Japan increased evenly by year in each cluster while the rate of increase of cluster2 was five times more than the other clusters in Korea. The results indicate that Korean researches on Artiodactyla tended to focus on diseases more than those in China and Japan, and few researchers considered other subjects including habitat characteristics, behavior and molecular ecology. In order to control the damage caused by Artiodactyla and to establish a reasonable policy for the protection of endangered species, it is necessary to accumulate basic ecological data by conducting researches on wild Artiodactyla more.