• Journal of the Korean Society of Industry Convergence
• /
• v.25 no.3
• /
• pp.349-356
• /
• 2022

Currently, the agricultural population in Korea indicates a decreasing and aging orientation. As the population of farm labor continues to decline, so farmers are feeling the pressure to be stable crop production. To solve the problem caused by the decreasing of farm labor, it is necessary to change over to "Digital agriculture". Digital agriculture is tools that digitally collect, store, analyze, and share electronic data and/or information in agriculture, and aims to integrate the several digital technologies into crop and livestock management and other processes in agriculture fields. In addition, digital agriculture can offer the opportunity to increase crop production, save costs for farmer. Therefore, in this study, for data-based Digital Agriculture, a greenhouse environment monitoring system for crop growth monitoring based on Node-RED, which even beginners can use easily, was developed, and the implemented system was verified in a hydroponic greenhouse. Several sensors, such as temperature, humidity, atmospheric pressure, CO₂, solar radiation, were used to obtain the environmental data of the greenhouse. And the environmental data were processed and visualized using Node-RED and MariaDB installed in rule.box digital. The environment monitoring system proposed in this study was installed in a hydroponic greenhouse and obtained the environmental data for almost two weeks. As a result, it was confirmed that all environmental data were obtained without data loss from sensors. In addition, the dashboard provides the names of installed sensors, real time environmental data, and changes in the last three days for each environmental data. Therefore, it is considered that farmers will be able to easily monitor the greenhouse environment using the developed system in this study.

• Journal of Agricultural Extension & Community Development
• /
• v.7 no.2
• /
• pp.307-320
• /
• 2000

Agricultural extension services in Korea need to build new extension system to cope with the digital age and especially with shortage of extension educators which has been originated from the changing status from central to local government. The new trends including development of multi-media, wide-spreading of internet, and advent of internet broadcasting are offering the opportunities for new agricultural extension service. Utilization of agricultural internet broadcasting could be a new alternative for enhancing the efficiency of agricultural extension services. Agricultural internet broadcasting could support decision making of farmers by forwarding unique agricultural data which have been collected, compiled and processed by the Rural Development Administration. Farmers will be able to search useful agricultural data through agricultural internet broadcasting for more efficient farm management and marketing farm products. Agricultural internet broadcasting could utilize more prompt and abundant agricultural technology and management data compiled by the Rural Development Administration, and through this process, agricultural internet broadcasting could compensate the shortage of extension manpower. Through feedback process of agricultural internet broadcasting. farmers will be able to communicate more efficiently with extension educators to increase agricultural production and income. Agricultural Internet broadcasting could be an alternative to prevent farmers from being shunned in the information-based society.

• Smart Media Journal
• /
• v.12 no.5
• /
• pp.65-72
• /
• 2023

Smart Farm means creating new value in various fields related to agriculture, including not only agricultural production but also distribution and consumption through the convergence of agriculture and ICT. In Korea, a rental smart farm is created to spread smart agriculture, and a smart farm big data platform is established to promote data collection and utilization. It is pushing for digital transformation of agricultural products distribution from production areas to consumption areas, such as expanding smart APCs, operating online exchanges, and digitizing wholesale market transaction information. As such, although agricultural data is generated according to characteristics from various sources, it is only used as a service using statistics and standardized data. This is because there are limitations due to distributed data collection from agriculture to production, distribution, and consumption, and it is difficult to collect and process various types of data from various sources. Therefore, in this paper, we analyze the current state of domestic agricultural data collection and sharing for digital agriculture and propose a data collection and linkage method for artificial intelligence services. And, using the proposed data, we propose a deep learning-based environmental factor recommendation method.

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

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.13 no.1
• /
• pp.49-59
• /
• 1995

This thesis is purposed to economical and rational accomplishment of the agricultural water resources development project as to utilize digital topographic information in basic investigation, preliminary planning and detail design process of the agricultural water resources development. In this study, the digital topographic data is acquired to stereo aerial photography of test field and the digital elevation model(DEM) is generated by interpolation of acquired data. Also, the database of basic investigation which is constituted to graphic and at-tribute data is designed. As the results of this study, the method that is determined to this study makes a contribution to effective accomplishment of the agricultural water resources development project.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.4
• /
• pp.23-32
• /
• 2021

For agricultural water management, it is essential to establish the digital infrastructure data such as agricultural watershed, irrigated area and canal network in rural areas. Approximately 70,000 irrigation facilities in agricultural watershed, including reservoirs, pumping and draining stations, weirs, and tube wells have been installed in South Korea to enable the efficient management of agricultural water. The total length of irrigation and drainage canal network, important components of agricultural water supply, is 184,000 km. Major problem faced by irrigation facilities management is that these facilities are spread over an irrigated area at a low density and are difficult to access. In addition, the management of irrigation facilities suffers from missing or errors of spatial information and acquisition of limited range of data through direct survey. Therefore, it is necessary to establish and redefine accurate identification of irrigated areas and canal network using up-to-date high resolution images. In this study, previous existing data such as RIMS (Rural Infrastructure Management System), smart farm map, and land cover map were used to redefine irrigated area and canal network based on appropriate image data using satellite imagery, aerial imagery, and drone imagery. The results of the building the digital infrastructure in rural areas are expected to be utilized for efficient water allocation and planning, such as identifying areas of water shortage and monitoring spatiotemporal distribution of water supply by irrigated areas and irrigation canal network.

• Korean Journal of Agricultural Science
• /
• v.47 no.4
• /
• pp.1097-1107
• /
• 2020

Assessment of soil carbon stock is essential for climate change mitigation and soil fertility. The digital soil mapping (DSM) is well known as a general technique to estimate the soil carbon stocks and upgrade previous soil maps. The aim of this study is to calculate the soil carbon stock in the top soil layer (0 to 30 cm) in Jeolla Province of South Korea using the DSM technique. To predict spatial carbon stock, we used Cubist, which a data-mining algorithm model base on tree regression. Soil samples (130 in total) were collected from three depths (0 to 10 cm, 10 to 20 cm, 20 to 30 cm) considering spatial distribution in Jeolla Province. These data were randomly divided into two sets for model calibration (70%) and validation (30%). The results showed that clay content, topographic wetness index (TWI), and digital elevation model (DEM) were the most important environmental covariate predictors of soil carbon stock. The predicted average soil carbon density was 3.88 kg·m-2. The R2 value representing the model's performance was 0.6, which was relatively high compared to a previous study. The total soil carbon stocks at a depth of 0 to 30 cm in Jeolla Province were estimated to be about 81 megatons.

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

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.404-414
• /
• 2023

In this study, digital climate maps with high-resolution (1km, daily) for the period of 1981 to 2020 were produced for the use as reference data within the procedures for statistical downscaling of climate change scenarios. Grid data for the six climate variables including maximum temperature, minimum temperature, precipitation, wind speed, relative humidity, solar radiation was created over Korean Peninsula using statistical downscaling model, so-called IGISRM (Improved GIS-based Regression Model), using global reanalysis data and in-situ observation. The digital climate data reflects topographical effects well in terms of representing general behaviors of observation. In terms of Correlation Coefficient, Slope of scatter plot, and Normalized Root Mean Square Error, temperature-related variables showed satisfactory performance while the other variables showed relatively lower reproducibility performance. These digital climate maps based on observation will be used to downscale future climate change scenario data as well as to get the information of gridded agricultural weather data over the whole Korean Peninsula including North Korea.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.66 no.3
• /
• pp.53-62
• /
• 2024

The purpose of this study is to examine the methodology for applying digital twin technology to pumping station, identify the factors to be determined at each stage, and present its applicability. When analyzing the requirements for developing a digital twin for pumping station, they were categorized into service requirements, IoT device requirements, and gateway requirements, with a total of 39 requirements established. In system design, it was structured according to the principles of modularity, abstraction, stepwise decomposition, and information hiding, allowing the implementation of planned items for diagnosis and operational management. There are difficulties in setting communication-related protocols and applying them in the field due to the complexity of overseeing the entire system with data. Therefore, it is necessary to clarify the purpose of the system, and there are challenges in identifying the characteristics of individual facilities, such as pumps in pumping station, and fully incorporating them into the system process. Thus, the framework of the initial design is crucial for implementing a digital twin.