• 농업과학연구
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• 제29권2호
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• pp.109-121
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• 2002

In the near future information technology will make more extensive applications to agricultural marketing system through e-commerce system. This paper is to investigate the current trends of e-commerce in agricultural marketing and suggest strategies for increasing agricultural product sales at the farm level as follows ; 1) price and quality control for attracting customers 2) increasing brand power for marketing promotion, and 3) devising more convenient website for internet users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3194-3216
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• 2018

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via $L_{2,1}$ norm minimization ($2DSFDA-L_{2,1}$) is proposed. $2DSFDA-L_{2,1}$ integrates $L_{2,1}$ norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, $L_{2,1}$ norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed $L_{2,1}$ nonlinear model into a linear regression type. Additionally, $2DSFDA-L_{2,1}$ is extended to a bilateral projection version called $BSFDA-L_{2,1}$. The advantage of $BSFDA-L_{2,1}$ is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed $2DSFDA-L_{2,1}/BSFDA-L_{2,1}$ can obtain competitive performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1414-1430
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• 2022

Due to the Internet of Things popularity, many agricultural data are collected by sensors automatically. The abundance of agricultural data makes precise prediction of rice yield possible. Because the climate factors have an essential effect on the rice yield, we considered the climate factors in the prediction model. Accordingly, this paper proposes a machine learning model for rice yield prediction in Taiwan, including the genetic algorithm and support vector regression model. The dataset of this study includes the meteorological data from the Central Weather Bureau and rice yield of Taiwan from 2003 to 2019. The experimental results show the performance of the proposed model is nearly 30% better than MARS, RF, ANN, and SVR models. The most important climate factors affecting the rice yield are the total sunshine hours, the number of rainfall days, and the temperature.The proposed model also offers three advantages: (a) the proposed model can be used in different geographical regions with high prediction accuracies; (b) the proposed model has a high explanatory ability because it could select the important climate factors which affect rice yield; (c) the proposed model is more suitable for predicting rice yield because it provides higher reliability and stability for predicting. The proposed model can assist the government in making sustainable agricultural policies.

• 한국식물병리학회:학술대회논문집
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• 한국식물병리학회 2003년도 정기총회 및 추계학술발표회
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• pp.108.2-109
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• 2003

The Gyeonggi-do Agricultural Research and Extension Services has developed a web-site (www.epilove.com) in collaboration with EPINET to provide information on agricultural weather and rice disease and insect pest management in Gyeonggi-do. Weather information includes near real-time weather data monitored by automated weather stations (AWS) installed at rice paddy fields of 11 Agricultural Technology Centers (ATC) in Gyeonggi-do, and weekly weather forecast by Korea Meteorological Administration (KMA). Map images of hourly air temperature and rainfall are also generated at 309m x 309m resolution using hourly data obtained from AWS installed at 191 locations by KMA. Based on near real-time weather data from 11 ATC, hourly infection risks of rice blast, sheath blight, and bacterial grain rot for individual districts are estimated by disease forecasting models, BLAST, SHBLIGHT, and GRAINROT. Users can diagnose various diseases and insects of rice and find their information in detail by browsing thumbnail images of them. A database on agrochemicals is linked to the system for disease and insect diagnosis to help users search for appropriate agrochemicals to control diseases and insect pests.

• Journal of the Korean Wood Science and Technology
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• 제51권1호
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• pp.67-67
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• 2023

• Advances in robotics research
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• 제2권3호
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• pp.201-217
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• 2018

Agriculture production is a critical human intensive task, which takes place in all regions of the world. The process to grow and harvest crops is labor intensive in many countries due to the lack of automation and advanced technology. Much of the difficult, dangerous and dirty labor of crop production can be automated with intelligent and robotic platforms. We propose an intelligent, agent-oriented robotic team, which can enable the process of harvesting, gathering and collecting crops and fruits, of many types, from agricultural fields. This paper describes a novel robotic organization enabling humans, robots and agents to work together for automation of gathering and collection functions. The focus of the research is a model, called HARMS, which can enable Humans, software Agents, Robots, Machines and Sensors to work together indistinguishably. With this model, any capability-based human-like organization can be conceived and modeled, such as in manufacturing or agriculture. In this research, we model, design and implement a technology application of knowledge-based robot-to-robot and human-to-robot collaboration for an agricultural gathering and collection function. The gathering and collection functions were chosen as they are some of the most labor intensive and least automated processes in the process acquisition of agricultural products. The use of robotic organizations can reduce human labor and increase efficiency allowing people to focus on higher level tasks and minimizing the backbreaking tasks of agricultural production in the future. In this work, the HARMS model was applied to three different robotic instances and an integrated test was completed with satisfactory results that show the basic promise of this research.

• Journal of Biosystems Engineering
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• 제29권2호
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• pp.187-194
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• 2004

The study focused on setting priority among the future core technologies that were derived by Delphi in agricultural management ＆ information using analytic hierarchy process(AHP). Items for setting priority were decided as ‘technology’, ‘market’, and ‘public concerns’by council. The future core technologies were divided into four parts by importance and R＆D level. Technologies which consisted of upper two parts having higher importance were considered in the study. Questionnaires were given to 9 specialists in biosystems engineering. As the results, ‘market’ was decided as a most important item. The most important technologies were ‘Developing technology and system of post harvest processing for higher quality rice’ in group I, and ‘Developing sensor technology to evaluate quality and safety for agricultural products’ in group II.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2003년도 춘계 학술발표논문집
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• pp.87-87
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• 2003

The Gyeonggi-do Agricultural Research and Extension Services has developed a web-site (http://www.epilove.com) in collaboration with EPINET to provide information on agricultural weather and rice disease and insect pest management in Gyeonggi-do. Weather information includes near real-time weather data monitored by automated weather stations (AWS) installed at rice paddy fields of 11 Agricultural Technology Centers (ATC) in Gyeonggi-do, and weekly weather forecast by Korea Meteorological Administration (KMA).(omitted)

• BMB Reports
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• 제43권7호
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• pp.491-498
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• 2010

Chemerin is a novel adipokine which is abundant in adipose tissue to promote adipocyte differentiation and with significant relativity to BMI and insulin sensitivity. We report here the molecular characterization of porcine chemerin and its receptors ChemR23 and GPR1, as well as their transcriptional regulation during lipogenesis. Chemerin was mainly expressed in liver, intestine, kidney and adipose tissue, consistent with the expression pattern of GPR1, but not ChemR23, which was predominantly present in spleen and temperately in adipose tissue. We further investigated the lipogenesis-related transcriptional activation of $PPAR{\gamma}$ and KLF15 on chemerin and its receptors. The data showed that KLF15, but not $PPAR{\gamma}$, can up-regulate the mRNA level of chemerin, ChemR23 and GPR1, which was consistent with the results of luciferase assay that confirmed the effect of KLF15 on ChemR23 promoter. Taken together, our data provide basic molecular information for the further investigation on the function of chemerin in lipogenesis.

• Asian-Australasian Journal of Animal Sciences
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• 제25권2호
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• pp.177-182
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• 2012

To investigate the genetic diversity of seven Chinese indigenous meat goat breeds (Tibet goat, Guizhou white goat, Shannan white goat, Yichang white goat, Matou goat, Changjiangsanjiaozhou white goat and Anhui white goat), explain their genetic relationship and assess their integrity and degree of admixture, 302 individuals from these breeds and 42 Boer goats introduced from Africa as reference samples were genotyped for 11 microsatellite markers. Results indicated that the genetic diversity of Chinese indigenous meat goats was rich. The mean heterozygosity and the mean allelic richness (AR) for the 8 goat breeds varied from 0.697 to 0.738 and 6.21 to 7.35, respectively. Structure analysis showed that Tibet goat breed was genetically distinct and was the first to separate and the other Chinese goats were then divided into two sub-clusters: Shannan white goat and Yichang white goat in one cluster; and Guizhou white goat, Matou goat, Changjiangsanjiaozhou white goat and Anhui white goat in the other cluster. This grouping pattern was further supported by clustering analysis and Principal component analysis. These results may provide a scientific basis for the characteristization, conservation and utilization of Chinese meat goats.