• Journal of Digital Convergence
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• v.19 no.2
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• pp.39-50
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• 2021

To investigate these hypotheses, we used data from survey questionnaires on CEO's of 176 persons. First, the results showed that charisma and servant leadership are positively related with goal oriented motivation. transactional leadership did not related with goal oriented motivation. Second, leadership among Excepting for charisma, transactional and servant leadership affect significantly related with activity oriented motivation. Third, we also surprisingly found that charisma, transactional and servant leadership are not related to learning oriented motivation. Fourth, we also showed that goal oriented in motivation of educational participation positively related with agricultural business outcomes. In the addition, the results showed that charisma and servant leadership are positively related with agricultural business outcomes.

• 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.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.

• 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.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.151-167
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• 2020

This study examined the factors affecting the behavioral intention for agricultural drone service. The survey results of 324 agricultural-related workers were analyzed using SPSS v22.0 and PROCESS macro v3.4. The effects of technology acceptance factors by UTAUT on the behavioral intention for agricultural drone service and the mediating effects of perceived benefits were analyzed. The results are as follows: First, the technology acceptance factors had positive (+) effects on perceived benefits and behavioral intention for agricultural drone service. Second, economics mediated between factors excluding performance expectancy and intention, convenience also mediated between factors excluding social influence and intention, and there was no significant mediating effect of practicality benefits. In the future, a further research is required for people trained in agriculture or drone or had a drone license.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.189-199
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• 2020

The purpose of this study was to provide basic data for the development of strategic measures for the application and growth potential of care farming as a healing for office workers. Therefore this study was investigated the relationship between supply conditions and offer programs for the activation of care farming and the intention of participants to use. The analysis results of the survey conducted for general office workers are as follows. Frist, there was a significant positive correlation between all sub-factors and intention to use. Second, the higher the awareness about the convenience of using healing facilities among supply conditions of healing farming and the necessity of medical service, the higher the intention to use. Third, the higher the awareness about the necessity of cultivation of crops, animal mediation, natural activities, and cultural activities among supply conditions of healing farming, the higher the intention to use. Based on the above analysis results, this study discussed the differences from previous studies and also derived insights for establishing a strategic plan to promote care farming.

• Journal of Biosystems Engineering
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• v.5 no.1
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• pp.24-32
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• 1980

테라스 영농지의 기계화영농에 대한 문제점은 여러 학자들에 의해서 의논되어 왔으나 비용에 의한 구체적인 조치는 거의 시도된바 없다. 본 연구에서는 적정한 테라스 영농 시스템을 구명하기 위하여 토양유실비용, 영농기계이용비용 및 테라스 축조비용을 포괄적으로 다루었다. 이를 위하여 테라스 단면의 설계와 그 축조비용의 추정, 토양유실의 예측 및 농업기계의 작업성능과 그 이용비용의 평가가 가능한 디지털 컴퓨터 모형을 개발하였다. 예시의 테라스 예정지에 대하여 반복기법을 이용하여 컴퓨터 모형을 시험한 바 테라스 영농시스템의 적정화에 만족하게 사용될 수 있음이 입증되었다.

• Smart Media Journal
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• v.11 no.4
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• pp.19-29
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• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.

• Proceedings of the Korean Society of Crop Science Conference
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• 2020.11a
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• pp.6-6
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• 2020

• Proceedings of the Korean Society of Crop Science Conference
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• 2020.11a
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• pp.8-8
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• 2020