• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.507-509
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• 2022

농업은 우리의 삶에서 빼놓을 수 없는 중요한 분야이며, 농업은 토지를 이용하여 다양한 작물들을 길러 음식을 만드는 기본이라고 말할 수 있다. 이렇게 중요한 농업 분야를 ICT 분야에서 가장 이슈가 되는 기술인 인공지능 기술과 결합하여 스마트팜과 같은 농업의 디지털화를 구축할 수 있다. 이와 같은 스마트팜 구축을 위해서는 기본적으로 다양한 작물의 빅데이터를 제공하고, 이 데이터를 바탕으로 인공지능을 수행하여 다양한 결과를 제공할 수 있다. 하지만 인공지능 연구를 수행하기 위한 시스템 및 플랫폼의 부재라는 문제점이 존재한다. 이러한 문제점을 해결하기 위해 농업 빅데이터 관리 및 인공지능 연구 플랫폼 개발을 위한 과제를 통해 농업 빅데이터를 관리하고 인공지능을 연구자들이 손쉽게 수행할 수 있는 플랫폼을 개발하여 농업 분야의 작물 생산성 향상에 기여하고자 한다.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.149-158
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• 2017

Big data have been presenting us with exciting opportunities and challenges in economic development. For instance, in the agriculture sector, mixing up of various agricultural data (e.g., weather data, soil data, etc.), and subsequently analyzing these data deliver valuable and helpful information to farmers and agribusinesses. However, massive data in agriculture are generated in every minute through multiple kinds of devices and services such as sensors and agricultural web markets. It leads to the challenges of big data problem including data collection, data storage, and data analysis. Although some systems have been proposed to address this problem, they are still restricted either in the type of data, the type of storage, or the size of data they can handle. In this paper, we propose a novel design of a platform for collecting and analyzing agricultural big data. The proposed platform supports (1) multiple methods of collecting data from various data sources using Flume and MapReduce; (2) multiple choices of data storage including HDFS, HBase, and Hive; and (3) big data analysis modules with Spark and Hadoop.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.8
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• pp.235-242
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• 2020

In order to promote digital transformation of our agricultural and rural communities in the wake of the fourth industrial revolution and prepare for the upcoming artificial intelligence era, it is necessary to establish a system and system that can collect, analyze and utilize necessary quality data. To this end, we will investigate and analyze problems and issues felt by various stakeholders such as farmers and agricultural officials, and present strategic measures to revitalize big data, which must be decided in order to promote digital transformation of our agricultural and rural communities, such as expanding big data platforms for joint utilization, establishing sustainable big data governance, and revitalizing the foundation for big data utilization based on demand.

• The Journal of Bigdata
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• v.8 no.1
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• pp.1-10
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• 2023

This paper presents the design process of an agricultural data sharing platform intended to address major challenges faced by the domestic agricultural industry. The platform was designed with a user interface that prioritizes user requirements for ease of use and offers various analysis techniques to provide growth prediction for field environment, growth, management, and control data. Additionally, the platform supports File to DB and DB to DB linkage methods to ensure seamless linkage between the platform and farmhouses. The UI design process utilized HTML/CSS-based languages, JavaScript, and React to provide a comprehensive user experience from platform login to data upload, analysis, and detailed inquiry visualization. The study is expected to contribute to the development of Korean smart farm models and provide reliable data sets to agricultural industry sites and researchers.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1593-1601
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• 2017

In a big data platform, association rule mining applications could bring some benefits. For instance, in a agricultural big data platform, the association rule mining application could recommend specific products for farmers to grow, which could increase income. The key process of the association rule mining is the frequent itemsets mining, which finds sets of products accompanying together frequently. Former researches about this issue, e.g. Apriori, are not satisfying enough because huge possible sets can cause memory to be overloaded. In order to deal with it, SON algorithm has been proposed, which divides the considered set into many smaller ones and handles them sequently. But in a single machine, SON algorithm cause heavy time consuming. In this paper, we present a method to find association rules in our Hadoop based big data platform, by parallelling SON algorithm. The entire process of association rule mining including pre-processing, SON algorithm based frequent itemset mining, and association rule finding is implemented on Hadoop based big data platform. Through the experiment with real dataset, it is conformed that the proposed method outperforms a brute force method.

• Magazine of the Korean Society of Agricultural Engineers
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• v.59 no.4
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• pp.26-34
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• 2017

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.249-252
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• 2021

한국 농업·농촌의 고령화 현상과 농업인구의 감소가 점차 심화됨에 따라 귀농·귀촌을 통한 인구 유입이 절실한 상황이다. 이를 위해서 귀농·귀촌 희망자가 의사결정 시에 도움을 받을 수 있는 귀농·귀촌 통합플랫폼을 구축 예정이다. 따라서 본 연구를 통해 빅데이터기반 귀농·귀촌 통합플랫폼 사용의도에 영향을 미치는 요인들을 분석하여 실제 플랫폼 구축 시 필요한 시사점을 도출하고자 한다.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.193-198
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• 2021

Recently, interests in the 4th industry have increased the level of demand for pest control by farmers in the field of rice farming, and the interests and use of agricultural pest control drones. Therefore, the diversification of agricultural control drones that spray high-concentration pesticides and the increase of agricultural exterminators due to the acquisition of national drone certifications are rapidly developing the agricultural sector in the drone industry. In addition, as detailed projects, an effective platform is required to construct large-scale big data due to pesticide management, exterminator management, precise spraying, pest control work volume classification, settlement, soil management, prediction and monitoring of damages by pests, etc. and to process the data. However, studies in South Korea and other countries on development of models and programs to integrate and process the big data such as data analysis algorithms, image analysis algorithms, growth management algorithms, AI algorithms, etc. are insufficient. This paper proposed an online drone pest control management information platform to meet the needs of managers and farmers in the agricultural field and to realize precise AI pest control based on the agricultural drone pest control processor using drones and presented foundation for development of a comprehensive management system through empirical experiments.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.157-175
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• 2022

At this moment, a paradigm shift is taking place across all sectors of society for the transition movements to the digital economy. Various movements are taking place in the global agricultural industry to achieve innovative growth using big data which is a key resource of the 4th industrial revolution. Although the government is making various attempts to promote the use of big data, the movement of the agricultural industry as a key player in the use of big data, is still insufficient. Therefore, in this study, effects of performance expectations, effort expectations, social impact, facilitation conditions, based on the Unified Theory of Acceptance and Use of Technology(UTAUT), and innovation tendencies on the acceptance intention of big data were analyzed using the economic and practical benefits that can be obtained from the use of big data for agricultural-related companies as moderating variables. 333 questionnaires collected from agricultural-related companies were used for empirical analysis. The analysis results using SPSS v22.0 and Process macro v3.4 were found to have a significant positive (+) effect on the intention to accept big data by effort expectations, social impact, facilitation conditions, and innovation tendencies. However, it was found that the effect of performance expectations on acceptance intention was insignificant, with social impact having the greatest influence on acceptance intention and innovation tendency the least. Moderating effects of economic benefit and practical benefit between effort expectation and acceptance intention, moderating effect of practical benefit between social impact and acceptance intention, and moderating effect of economic benefit and practical benefit between facilitation condition and acceptance intention were found to be significant. On the other hand, it was found that economic benefits and practical benefits did not moderate the magnitude of the influence of performance expectations and innovation tendency on acceptance intention. These results suggest the following implications. First, in order to promote the use of big data by companies, the government needs to establish a policy to support the use of big data tailored to companies. Significant results can only be achieved when corporate members form a correct understanding and consensus on the use of big data. Second, it is necessary to establish and implement a platform specialized for agricultural data which can support standardized linkage between diverse agricultural big data, and support for a unified path for data access. Building such a platform will be able to advance the industry by forming an independent cooperative relationship between companies. Finally, the limitations of this study and follow-up tasks are presented.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.249-267
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• 2021

The use of big data accumulated along with the progress of digitalization is bringing disruptive innovation to the global agricultural industry. Recently, the government is establishing an agricultural big data platform and a support organization. However, in the domestic agricultural industry, the use of big data is insufficient except for some companies in the field of cultivation and growth. In this context, this study identifies factors affecting the intention to use big data in terms of technology, organization and environment, and also confirm the moderating effect of technical field, focusing on agricultural ventures which should be the main entities in creating innovation by using big data. Research data was obtained from 309 agricultural ventures supported by the A+ Center of FACT(Foundation of AgTech Commercialization and Transfer), and was analyzed using IBM SPSS 22.0. As a result, Among technical factors, relative advantage and compatibility were found to have a significant positive (+) effect. Among organizational factors, it was found that management support had a positive (+) effect and cost had a negative (-) effect. Among environmental factors, policy support were found to have a positive (+) effect. As a result of the verification of the moderating effect of technology field, it was found that firms other than cultivation had a moderating effect that alleviated the relationship between all variables other than relative advantage, compatibility, and competitor pressure and the intention to use big data. These results suggest the following implications. First, it is necessary to select a core business that will provide opportunities to generate new profits and improve operational efficiency to agricultural ventures through the use of big data, and to increase collaboration opportunities through policy. Second, it is necessary to provide a big data analysis solution that can overcome the difficulties of analysis due to the characteristics of the agricultural industry. Third, in small organizations such as agricultural ventures, the will of the top management to reorganize the organizational culture should be preceded by a high level of understanding on the use of big data. Fourth, it is important to discover and promote successful cases that can be benchmarked at the level of SMEs and venture companies. Fifth, it will be more effective to divide the priorities of core business and support business by agricultural venture technology sector. Finally, the limitations of this study and follow-up research tasks are presented.