• Plant Biotechnology Reports
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• v.2 no.1
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• pp.41-46
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• 2008

Salinity stress is a major limiting factor in cereal productivity. Many studies report improvements in salt tolerance using model plants, such as Arabidopsis thaliana or standard varieties of rice, e.g., the japonica rice cultivar Nipponbare. However, there are few reports on the enhancement of salt tolerance in local rice cultivars. In this work, we used the indica rice (Oryza sativa) cultivar BR5, which is a local cultivar in Bangladesh. To improve salt tolerance in BR5, we introduced the Escherichia coli catalase gene, katE. We integrated the katE gene into BR5 plants using an Agrobacterium tumefaciens-mediated method. The introduced katE gene was actively expressed in the transgenic BR5 rice plants, and catalase activity in $T_1$ and $T_2$ transgenic rice was approximately 150% higher than in nontransgenic plants. Under NaCl stress conditions, the transgenic rice plants exhibited high tolerance compared with nontransgenic rice plants. $T_2$ transgenic plants survived in a 200 mM NaCl solution for 2 weeks, whereas nontransgenic plants were scorched after 4 days soaking in the same NaCl solution. Our results indicate that the katE gene can confer salt tolerance to BR5 rice plants. Enhancement of salt tolerance in a local rice cultivar, such as BR5, will provide a powerful and useful tool for overcoming food shortage problems.

• Korean Journal of Organic Agriculture
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• v.25 no.3
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• pp.555-567
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• 2017

This study suggests the yield forecast model for chilli pepper using artificial neural network. For this, we select the most suitable network models for chilli pepper's yield and compare the predictive power with adaptive expectation model and panel model. The results show that the predictive power of artificial neural network with 5 weather input variables (temperature, precipitation, temperature range, humidity, sunshine amount) is higher than the alternative models. Implications for forecasting of yields are suggested at the end of this study.

• Korean Journal of Organic Agriculture
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• v.26 no.2
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• pp.181-202
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• 2018

Most of eco-friendly agrifood processing enterprises in Korean rural area are small and medium-sized business. For this reason, it's hard for eco-friendly agrifood processing enterprises to neither analyze business performance for efficient business management nor establish their own business plan for rational decision-making. Therefore it's necessary to design effective mathematical programming model and to make practical application which can support rational management decision-making ensuring the stable business activity of eco-friendly agrifood processing enterprises. Accordingly this paper focuses on the designing and its application of multi-objective mathematical programming model using goal programming to support rational decision-making of eco-friendly agrifood processing enterprise. Hansalimanseongmachum Food Inc. which runs soy bean processing business making tofu based on regional-based soybean farms around Anseong City will be the specific case to apply multi-objective mathematical programming model in practice. And it will suggest measures to support rational management decision-making of other eco-friendly agrifood processing enterprises.

• The Korean Journal of Community Living Science
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• v.21 no.4
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• pp.619-631
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• 2010

The purpose of this study is to segment and examine urban farmers behavior by applying a two-step cluster analysis and multi-nominal logit model. The data were collected by a telephone survey with two-staged stratified random sampling in the cities around the country for the purpose of acquiring representative data. Respondents were asked to describe their awareness of urban agriculture, their agricultural activity, and sociodemographic characteristics. Among 2,000 cases, 381 cases(19.1%) which were of participants in urban agriculture were analysed in SPSS. From the findings, 27.3% of respondents had heard the word 'urban agriculture', and 25.5% of them regarded themselves as urban farmers. Four different clusters were derived from two-step clusters based on motive, place, companion, area and hours. They were 'Large scale hobby farming(cluster 1)', ‘Weekend farm/ hobby farming(cluster 2)', 'Land/ Self-supporting farming(cluster 3)', and 'Small scale hobby farming(cluster 4)'. The result of multinomial logistic regression showed that there were significant differences among these four segmented groups in terms of age, city size and housing type. In other words, there is quite a possibility that urbanites select different urban farming types according to their socio-demographic profiles. Therefore, the urbanite profiles can be used as the basis for promoting policy of several urban agriculture types. According to the result, policy directions for facilitating urban agriculture were presented.

• Korean Journal of Veterinary Research
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• v.44 no.3
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• pp.349-355
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• 2004

The aim of this study was to evaluate the expression of galectin-3, one of beta-galactoside-binding proteins, in the experimental autoimmune encephalomyelitis(EAE) model of Lewis rats or non-obese diabetic (NOD) mice. Western blot analysis showed that galectin-3 was weakly expressed in the spinal cords of complete Freund's adjuvant(CFA) immunized control rats. In EAE, however, galectin-3 expression was significantly increased at the peak stage(days 14 post-immunization), while it was decreased slightly at the recovery stage(day 21 post-immunization). Immunohistochemical analysis showed that galectin-3 was detected in some macrophages in demyelinating lesions of NOD mice, while galectin-3 was immunoreacted in some inflammatory cells in the perivascular cuffing in rat EAE lesions. Collectively, it is postulated that the expression of galectin-3 is significantly increased in response to neuroimmunological stimulation in the central nervous system, whereas it is weak in normal rats and mice.

• Korean Journal of Agricultural Science
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• v.41 no.3
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• pp.169-175
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• 2014

Objective of this study was to investigate effects of pre-processing method and number of sampling leaves on stability of the reflectance measurement for Chinese cabbage and kale leaves. Chinese cabbage and kale were transplanted and cultivated in a plant factory. Leaf samples of the kale and cabbage were collected at 4 weeks after transplanting of the seedlings. Spectra data were collected with an UV/VIS/NIR spectrometer in the wavelength region from 190 to 1130 nm. All leaves (mature and young leaves) were measured on 9 and 12 points in the blade part in the upper area for kale and cabbage leaves, respectively. To reduce the spectral noise, the raw spectral data were preprocessed by different methods: i) moving average, ii) Savitzky-Golay filter, iii) local regression using weighted linear least squares and a $1^{st}$ degree polynomial model (lowess), iv) local regression using weighted linear least squares and a $2^{nd}$ degree polynomial model (loess), v) a robust version of 'lowess', vi) a robust version of 'loess', with 7, 11, 15 smoothing points. Effects of number of sampling leaves were investigated by reflectance difference (RD) and cross-correlation (CC) methods. Results indicated that the contribution of the spectral data collected at 4 sampling leaves were good for both of the crops for reflectance measurement that does not change stability of measurement much. Furthermore, moving average method with 11 smoothing points was believed to provide reliable pre-processed data for further analysis.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.995-1010
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• 2020

In this study, a multivariate analysis model of partial least square regression (PLSR) was developed to predict the moisture content of green peppers using hyperspectral imaging (HSI). In HSI, illumination is essential for high-quality image acquisition and directly affects the analytical performance of the visible near-infrared hyperspectral imaging (VIS/NIR-HSI) system. When green pepper images were acquired using a direct lighting system, the specular reflection from the surface of the objects and their intensities fluctuated with time. The images include artifacts on the surface of the materials, thereby increasing the variability of data and affecting the obtained accuracy by generating false-positive results. Therefore, images without glare on the surface of the green peppers were created using a polarization filter at the front of the camera lens and by exposing the polarizer sheet at the front of the lighting systems simultaneously. The results obtained from the PLSR analysis yielded a high determination coefficient of 0.89 value. The regression coefficients yielded by the best PLSR model were further developed for moisture content mapping in green peppers based on the selected wavelengths. Accordingly, the polarization filter helped achieve an uniform illumination and the removal of gloss and artifact glare from the green pepper images. These results demonstrate that the HSI technique with a polarized lighting system combined with chemometrics can be effectively used for high-throughput prediction of moisture content and image-based visualization.

• Smart Media Journal
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• v.11 no.4
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• pp.9-18
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• 2022

Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.

• Korean Journal of Organic Agriculture
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• v.30 no.4
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• pp.451-470
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• 2022

Recently, the environment has been recognized as an important factor in increasing competitiveness in the industry. In agriculture and rural areas, the environment is becoming important in terms of the competitive advantage of agricultural products and continued regional development. This study intended to provide farmers with basic data for the continuous development of local agriculture through exploratory studies of environmental behaviors and regional development. In this study, 107 questionnaires were used for analysis of farmers in Jinan County to analyze the impact of farmers' environmental consciousness on regional development, and the research model was verified using a structural equation model. As a result of the analysis, it was analyzed that among the components of the environmental consciousness of farmers, environmental health has a statistically significant positive effect on environmental behaviors, while environmental interest and soil environment do not have an impact. The environmental behaviors of farmers have not been shown to be statistically significant to regional development. As a result of the analysis of this research, first, it is necessary to foster at the local level by establishing a customized fostering system for each village and region, such as education and technical support to vitalize the participation of young farmers and small and medium-sized farmers through the establishment of an Eco-friendly agricultural organization support system. It is necessary to raise public awareness of the public good function of agriculture and expand opportunities for sharing the value of Eco-friendly agriculture.

• Korean Journal of Organic Agriculture
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• v.30 no.2
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• pp.207-230
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• 2022

A survey on the understanding of environment-friendly certification system was conducted for certified operators. The survey included the necessity of certification system, difficulties in producing certified products, and general evaluation of the current certification system. Certified operators were generally satisfied with the certification system in terms of the certification standards, the public subsidy programs, and the farm management costs. Individuals with parallel production farms were relatively less satisfied than the group with full-time organic farmers. Analysis using the ranking probit model indicated that the longer the certification experience, the more highly aware the need for the certification system was. Our results indicated that policy strategies are needed to enlarge the marketability of environment-friendly agricultural products since economic factors of organic products were the most important factor for maintaining and expanding certification in overseas as well as in Korea. It seems to be necessary to implement economic triggers for certified operators to continue their certification programs by promoting the transition period certification for individual farms in parallel with conventional agriculture. Analysis of the variables correlated with the expansion of environment-friendly agriculture by the logit model implied that certified operators with the younger age and higher annual incomes were more likely to expand environment-friendly agriculture. Therefore, it might also be important to provide financial support and incentives for new entry farmers to participate in environment-friendly agriculture and establish a system to share the know-how of successful certified organic farmers.