• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.593-602
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• 2020

Recently, as research on smart farms has been actively conducted, systems for efficiently cultivating crops have been introduced and various energy systems using renewable energy such as solar, geothermal and wind power generation have been proposed to save the energy. In this paper, we propose a new and renewable energy convergence system for crops that provides energy independence and improved crop cultivation environment. First, we present LPWA-based communication node and gateway for ICT-based data collection. Then we propose an integrated monitoring server that collects energy data, crop growth data, and environmental data through a communication node and builds it as big data to perform optimal energy management that reflects the characteristics of the environment for cultivating crops. The proposed system is expected to contribute to the production of low-cost, high-quality crops through the fusion of renewable energy and smart farms.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.215-221
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• 2020

The paradigm of agriculture is also changing to address the problem of food shortages due to the increase of the world population, climate conditions that are increasingly subtropical, and labor shortages in rural areas due to aging population. With the development of Information Communication Technology (ICT), our daily lives are changing rapidly and heralds a major change in agricultural management. In a hyper-connected society, the introduction of high-tech into traditional Agriculture of the past is absolutely necessary. In the development process of Agriculture, the first generation produced by hand, the second generation applied mechanization, and the third generation introduced automation. The fourth generation is the current ICT operation and the fifth generation is artificial intelligence. This paper investigated Smart Farm that increases productivity through convergence of Agriculture and ICT, such as smart greenhouse, smart orchard and smart Livestock. With the development of sustainable food production methods in full swing to meet growing food demand, Smart Farming is emerging as the solution. In overseas cases, the Netherlands Smart Farm, the world's second-largest exporter of agricultural products, was surveyed. Agricultural automation using Smart Farms allows producers to harvest agricultural products in an accurate and predictable manner. It is time for the development of technology in Agriculture, which benchmarked cases of excellence abroad. Because ICT requires an understanding of Internet of Things (IoT), big data and artificial intelligence as predicting the future, we want to address the status of theory and actual Agriculture and propose future development measures. We hope that the study of the paper will solve the growing food problem of the world population and help the high productivity of Agriculture and smart strategies of sustainable Agriculture.

• Agribusiness and Information Management
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• v.10 no.2
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• pp.1-7
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• 2018

In the present day context of changing information needs of the farmers and diversified production systems there is an urgent need to look for the effective extension support system for the small and marginal farmers in the developing countries like India. The rapid developments in the collection and analysis of field data by using the spatial technologies like GPS&GIS were made available for the extension functionaries and clientele for the diversified information needs. This article describes the GIS and GPS based decision support system in precision agriculture for the resource poor farmers. Precision farming techniques are employed to increase yield, reduce production costs, and minimize negative impacts to the environment. The parameters those can affect the crop yields, anomalous factors and variations in management practices can be evaluated through this GPS and GIS based applications. The spatial visualisation capabilities of GIS technology interfaced with a relational database provide an effective method for analysing and displaying the impacts of Extension education and outreach projects for small and marginal farmers in precision agriculture. This approach mainly benefits from the emergence and convergence of several technologies, including the Global Positioning System (GPS), geographic information system (GIS), miniaturised computer components, automatic control, in-field and remote sensing, mobile computing, advanced information processing, and telecommunications. The PPP convergence of person (farmer), project (the operational field) and pixel (the digital images related to the field and the crop grown in the field) will better be addressed by this decision support model. So the convergence and emergence of such information will further pave the way for categorisation and grouping of the production systems for the better extension delivery. In a big country like India where the farmers and holdings are many in number and diversified categorically such grouping is inevitable and also economical. With this premise an attempt has been made to develop a precision farming model suitable for the developing countries like India.

• Journal of Agricultural Extension & Community Development
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• v.26 no.1
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• pp.1-15
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• 2019

The purpose of this study is to analyze the learning tendency of rural residents participating in lifelong education. The analysis data were analyzed using a polynomial model for 3,685 people using raw materials from a lifetime education survey. According to the analysis, participants in rural residents prefer small study groups. They were also approached on a practical level, such as life-help or self-realization. And the participating education programs were highly involved in vocational non-formal education. Based on the workings of personal backgrounds, participation of people with lower income and lower educational backgrounds is more active than in urban areas. In addition, the effect of improving the quality of life through lifelong education was shown in the order of big cities, small cities, rural areas. These results suggest that ways to improve the quality of life through lifelong education are not suitable as a means to maintain rural population.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.243-250
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• 2020

Systematic farming can be planned and managed if long-term agricultural weather information of the plantation is available. Because the greatest risk factor for crop cultivation is the weather. In this study, a method for long-term predicting of agricultural weather using the GloSea5 and machine learning is presented for the cultivation of Chinese cabbage. The GloSea5 is a long-term weather forecast that is available up to 240 days. The deep neural networks and the spatial randomforest were considered as the method of machine learning. The longterm prediction performance of the deep neural networks was slightly better than the spatial randomforest in the sense of root mean squared error and mean absolute error. However, the spatial randomforest has the advantage of predicting temperatures with a global model, which reduces the computation time.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.389-396
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• 2020

In this paper, a cultivation traceability system to implement smart agriculture developed and implemented, and in particular, devised a system that manages the cultivation traceability of grains that are difficult to grow in smart farms. Mobile and web programs based on smart devices are designed, and the collected information is stored in a DB server and can be used as big data. In addition, real-time location information and agricultural activity information can be matched using an electronic map(Vworld) based on GIS/LBS applying GPS of a mobile device. By designing the cultivation traceability information DB required in the field, the farmhouse, farmers, and cultivation information were developed to make it easy for managers to use, and implemented mobile and web programs in the field. The system is expected to raise the quality and safety management capabilities to the next level in response to variables such as labor saving effect and climate change.

• Journal of Korean Society of Forest Science
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• v.108 no.1
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• pp.97-108
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• 2019

In a situation where production of short-term income forestry products has been stagnant or decreased in recent years, the government or related agencies are trying to promote consumption of short-term income forest products. While consumer sentiment studies on short-term income forestry are being conducted as part of efforts to encourage consumption, most of the studies rely solely on a survey-based method. In the information age, consumer sentiment toward consumer goods is reflected mostly on social networking sites due to the spread of the Internet. It is necessary to avoid relying solely on a survey-based method in existing research and directly analyze social networking sites that reflect consumers' wishes. In response, this study identified consumer preferences for major short-term income forest products through social big data analyses and used the results to establish strategies for promoting the sale of short-term income forest products. This paper is different from previous research using only a survey-based method, and it uses SNS to understand consumer preferences. The results of this study are expected to directly help the government or related agencies promote consumption of short-term income forest products and, ultimately, help improve forest-related income and promote healthy forest condition.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.117-124
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• 2021

Livestock behavioral analysis is a factor that has a great influence on livestock health management and agricultural productivity increase. However, most digital devices introduced for behavioral analysis of livestock do not provide raw data and also provide limited analysis results. Such a closed system makes it more difficult to integrate data and build big data, which are essential for the introduction of advanced IT technologies. Therefore, it is necessary to supply farm-scale data collection devices that can be easily used at low cost. This study presents a data collection system for analyzing the behavior of livestock. The system consists of a number of miniature computing units that operate wirelessly, and collects livestock body temperature and acceleration data, location information, and livestock environment data. In addition, this study presents an algorithm for estimating the behavior of livestock based on the collected acceleration data. For the experiment, a system was built in a Korean cattle farm in Icheon, Gyeonggi-do, and data were collected for 20 Korean cattle, and based on this, the empirical and analysis results were presented.

• The Journal of Industrial Distribution & Business
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• v.9 no.3
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• pp.31-43
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• 2018

Purpose - With the technology of the 4th industrial revolution, business models of agricultural sector are changing rapidly toward convergence and high value-added business models due to the 6th industrialization of agricultural. Our goals is to examine the 6th industrialization of agriculture, and then to apply the technology of the 4th industrial revolution to the 6th industrialization of agriculture, suggesting the possibility of future agriculture, and then linking the export competitiveness of agricultural products. Research design, data, and methodology - As the methodology, case studies and empirical analyzes were conducted as well as literature studies. The case analyses included tomatoes, pig breeding farms, and an empirical analysis was conducted using the AHP analysis by experts of the 6th industrialization. In addition, using 124 survey data, this study examined and analyzed the items of the 4th industrial revolution technology for the 6th industrialization of agriculture and the strengthening of export competitiveness. Results - Results showed that the technology of the 4th Industrial Revolution helped "6th industrialization of agriculture" and "the strengthening of export competitiveness" using two successful cases. The AHP analysis was also carried out, and it was found that the improvement of the technology in the 4th industrial revolution could contribute to the future industrialization as well as the 6th industrialization of agriculture. First of all, we looked many conditions were important and urgent. Among the technologies of the 4th industrial revolution, the mobile, big data were important. Moreover, it was recognized that linkage and convergence related efforts would greatly contribute to strengthening export competitiveness of agriculture such as price and quality competitiveness. Conclusions - The 4th industrial revolution such as hyper-connectivity, hyper-intelligence and hyper-predictability contribute greatly to the 6th industrialization of agriculture, and therefore it is essential to improve the competitiveness of the agricultural sector by using the technology of the 4th industrial revolution. In particular, based on analyses of the diamond model, the "demand conditions" was the most important factor for the activation of the 6th Industrialization, and then "related and supporting fields", "factor conditions" and "business context" were followed in order. The results of this study can be useful for policy, practical and academic sectors.