• Proceedings of the Korean Society of Crop Science Conference
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• 2020.06a
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• pp.9-10
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• 2020

• 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 Convergence
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• v.15 no.2
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• pp.183-190
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• 2017

Recently the scale and area of greenhouse cultivation have been enlarged in Korea, and its importance in domestic agriculture is being increased. According to these situation, environment control systems are widely used in greenhouses. Even though development of greenhouse facilities and control devices, cultivation skill using them is in lower level more than european countries and Japan. In this study, we propose intelligent information system based on information-communication technology that supports environment control systems. Proposed system is able to support to maintain optimal environment for plant growth using data from environment control system, and also give useful knowledge for cultivation by active way. Furthermore, it estimates future status of plant growth, and suggest best strategy of environment control for current stage.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.293-299
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• 2018

The weather has a lot of influence on the cultivation of crops. Weather information on agricultural crop cultivation areas is indispensable for efficient cultivation and management of agricultural crops. Despite the high demand for agricultural weather, research on this is in short supply. In this research, we deal with the production method of agricultural weather in Jeollanam-do, which is the main production area of onions through GloSea5 and deep learning. A deep neural network model using the sliding window method was used and utilized to train daily weather prediction for predicting the agricultural weather. RMSE and MAE are used for evaluating the accuracy of the model. The accuracy improves as the learning period increases, so we compare the prediction performance according to the learning period and the prediction period. As a result of the analysis, although the learning period and the prediction period are similar, there was a limit to reflect the trend according to the seasonal change. a modified deep layer neural network model was presented, that applying the difference between the predicted value and the observed value to the next day predicted value.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.115-126
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• 2019

This study is a survey of people who are return to farming on the intention of accepting ICT convergence technology in agriculture. The research targets were 218 people based on convenience and judgment sampling methods, and the exploratory factor analysis and multiple return analysis were performed with SPSS 22.0. As a result, the independent variables such as performance expectation, effort expectation, and social influence had a positive effect on the acceptance intention of ICT convergence technology in agriculture. In addition, the moderating effects of innovation resistance on these influence relationships were also verified. The limitations of this study are the lack of verification of perception changes and the inability to control variables. As a result of this research, the results of the UTAUT in other fields were confirmed in this study. It is hoped that this study will further facilitate the research of agriculture in ICT convergence technology.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.95-112
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• 2021

This study analyzed the effects of healing agriculture program types on program satisfaction by using functional image and emotional image of the program as mediators for the participants of healing agriculture program. The sub-variables of the healing agriculture program were classified into horticultural healing, animal healing, food healing, and forest healing. 328 questionnaires collected from the participants of healing agriculture program in the whole country were used for empirical analysis, which used SPSS v22.0 and PROCESS macro v3.4 to analyze the parallel multiple mediation model. First, Among the types of healing agriculture programs, animal healing, food healing, and forest healing had a positive (+) effect on functional image. Second, all types of healing agriculture program had a positive (+) effect on emotional image. Third, both functional and emotional images had a significant positive effect on satisfaction. Fourth, among the types of healing agriculture program, horticultural healing, animal healing, and forest healing had a significant effect on satisfaction, while food healing did not maintain the significant effect on satisfaction. Fifth, functional image mediated between healing agriculture program and satisfaction. Sixth, emotional image mediated between healing agriculture program type and satisfaction. In the next study, it is necessary to study for the adjustment of mediators other than the mediators introduced in this study or the controlled mediated analysis through the conditional process model in which the moderator variable is introduced.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.171-177
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• 2022

In this paper, we propose a service method that can provide insight into multi-source agricultural data, way to cluster environmental factor which supports data analysis according to time flow, and curate crop environmental factors. The proposed curation service consists of four steps: collection, preprocessing, storage, and analysis. First, in the collection step, the service system collects and organizes multi-source agricultural data by using an OpenAPI-based web crawler. Second, in the preprocessing step, the system performs data smoothing to reduce the data measurement errors. Here, we adopt the smoothing method for each type of facility in consideration of the error rate according to facility characteristics such as greenhouses and open fields. Third, in the storage step, an agricultural data integration schema and Hadoop HDFS-based storage structure are proposed for large-scale agricultural data. Finally, in the analysis step, the service system performs DTW-based time series classification in consideration of the characteristics of agricultural digital data. Through the DTW-based classification, the accuracy of prediction results is improved by reflecting the characteristics of time series data without any loss. As a future work, we plan to implement the proposed service method and apply it to the smart farm greenhouse for testing and verification.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.79-83
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• 2024

Digital Twin technology is rapidly transforming various industries by providing comprehensive virtual models that replicate physical objects or processes. In the context of agriculture, digital twin can be a game-changer. This technology can help in creating precise simulations of farming scenarios, thereby enabling farmers to make data-driven decisions and optimize farm operations. The potential benefits include improved crop yields, resource efficiency, and environmental sustainability. However, the implementation of digital twin technology in agriculture poses challenges, such as data management issues and the need for robust IoT infrastructure. Despite these hurdles, the future of digital twin in agriculture looks promising, with ongoing research and developments aimed at overcoming these obstacles.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.224-224
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• 2023

국내·외로 Soil and Water Assessment Tool (SWAT) 모델은 유역 단위에서 유출 및 수질을 예측하는데 활용되고 있다. 하지만 SWAT 모델의 결과물은 데이터 테이블 형식으로만 이루어져 있기 때문에 모델 사용자가 유역 내 하천별 수문 모의 결과물을 직관적으로 확인하기 어렵다는 단점이 있다. 최근 다양한 분야에서 3D 가상환경을 구축하는데 디지털 트윈 기술의 활용성이 증가하고 있다. 디지털 트윈 기술은 현실의 공간을 가상환경으로 구축해 실시간 현실의 상황을 파악하여, 의사결정 지원을 제공한다는 장점이 있다. 이에 본 연구에서는 디지털 트윈 기술과 SWAT 모델을 연계하여, 모델의 결과값을 가상환경 3D 지도인 CESIUM에 실시간으로 표출할 수 있는 디지털 트윈 기반 SWAT 모델 수문 모의 시스템을 개발하였다. 이 시스템은 3D 지형에 SWAT 모델을 통해 모의 된 하천의 수위 및 SS에 대한 표출이 가능할 뿐만 아니라 기후나 유역환경에 따른 유역 내 수문 변화를 시·공간적으로 파악할 수 있는 장점이 있다. 향후 본 연구에서 개발된 디지털 트윈 기반 SWAT 모델 수문 모의 시스템은 홍수 및 가뭄과 같은 재해에 대응할 수 있는 유역 및 하천관리 대책을 효율적으로 수립하는데 활용될 수 있을 것으로 판단된다.