• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1827-1834
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• 2013

The existing PC-based desktop environment is being changed to a server-based virtual desktop environment due to various advantages such as security, mobility, and upgrade cost reduction. In this paper, a virtual computing lab service system which is applicable to the existing computer lab is designed and implemented using both an open source-based cloud computing platform and hypervisor. In addition, a node power scheduler is proposed in order to reduce power consumption in a server farm. The experimental results show that the power scheduler reduces power consumption considerably over the server farm without the power scheduler.

• Smart Media Journal
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• v.5 no.3
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• pp.17-24
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• 2016

Korean agriculture is currently experiencing difficulties as a cause of rural depopulation, aging of rural population, grain self-sufficiency rate decline, and deepening of climate change. It is necessary to ensure our country's agriculture industrial competitiveness in accordance with opening of FTA imports expanded. To ensure the underdeveloped competitive, Korean government defines the 3rd generation model from 1st generation model to extend the smart farms of Korean types. The agriculture smarting overcomes the growth limitations of agriculture, and efforts to develop 6th + ${\alpha}$ industry. In this paper, We define and verify the IoF(Internet of Farming)-Cloud based substantial services about 2rd generation model, and propose a greenhouse of IoF-Cloud testbed.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.555-566
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• 2012

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.324-327
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• 2013

Recently, due to evolving cloud computing technology, we can easily and transparently utilize both local computing resource and remote computing resource in real life. Especially, enhancing smart device technologies and network infrastructures promote an increase of needs to share files between local smart devices and cloud storages. However, since smart devices have a limited storage space, storing files on cloud storage causes a starvation problem of local storage. It means that users can face a storage-lack problem even a cloud storage service provide a huge file storing space. In this research, we propose a method to manage files between smart devices and cloud storages. Our approach calculate file usage pattern based on recently used date, and then this approach determines local files being migrated. As a result, our approach is sufficient for handling data synchronization between big data storage farm and local thin client which contains limited storage space.

• Journal of Korean Society of Rural Planning
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• v.25 no.2
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• pp.15-21
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• 2019

The agricultural environment is changing and becoming more advanced due to the influence of the 4th Industrial Revolution. From the basic plan of Rural Informatics to the current level of 2nd generation smart farms aimed at improving productivity using Big data, cloud network and more IoT technology. We are continuing to provide support and research and development. However, many problems remain to be solved in order to supply and settle smart farms in Korea. The purpose of this study is to provide a method of collecting and sharing data on farming environment and to help improve the income and productivity of farmers based on collected data. In the case of hog farm, the multiple sensors for environmental data like temperature, humidity and gases and the network environment for connecting the internet were established. The environment sensor was made using the ESP8266 Node MCU board as micro-controller, DHT22 sensor for temperature and humidity, and MQ series sensors for various gases in the hog pens. The network sensor was applied experimentally for one month and the environmental data of the hog farm was stored on a web database. This study is expected to raise the importance of collecting and managing the agricultural and environmental data, for the next generation farmers to understand the smart farm more easily and to try it by themselves.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.743-754
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• 2022

This paper contains the results of the development and performance evaluation of the environmental data monitoring system for the fish farm. For the hardware development, the analogue sensor is used to collect dissolved oxygen, pH, salinity, and temperature of the fish farm water, and the digital sensor is used for collecting ambient temperature, humidity, and location information via a GPS module to be sent to cloud-based Firebase DB. A set of LoRa transmitters and receivers is used as a communication module to upload the collected data. The data stored in Firebase is retrieved as a graph on a web and mobile application to monitor the environmental data changes in real-time. A notification will be delivered if the collected data is outside the determined optimal value. To evaluate the performance of the developed system, a response time from hardware modules to web and mobile applications is ranging from 6.2 to 6.85 seconds, which indicates satisfactory results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.764-771
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• 2018

Domestic and foreign agricultural environments nowadays are undergoing various changes such as aging of agricultural population, increase of earned population, rapid climate change, diversification of agricultural product distribution structure, depletion of water resources and limited cultivation area. In order to respond to various environmental changes in recent agriculture, practical use of Smart Greenhouse to easily record, store and manage crop production information such as crop growing information, growth environment and agriculture work log, Interest is growing. In this paper, we propose a system that collects the situation information necessary for growth such as temperature, humidity, solar radiation, CO2 concentration, and monitor the collected data, which can be measured in the rhizosphere of the crop. We have developed a system that collects data such as temperature, humidity, radiation, and growth environment data, which are measured by data obtained from the rhizosphere measuring section of a growing crop and measured by a sensor, and transmitted to a wireless communication gateway of 400 MHz. We developed the integrated SW that can monitor the rhythm environment data and visualize the data by using cloud based data. We can monitor by graph format and data format for visualization of data. The existing smart farm managed crops and facilities using only the data within the farm, and this study suggested the most efficient growth environment by collecting and analyzing the weather and growth environment of the farms nationwide.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.486-490
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• 2009

In order to investigate the offshore wind resources, the "QuikSCAT Level 3" data by the QuikSCAT satellite was analyzed from Jan 2000 to Dec 2008. QuikSCAT satellite is a specialized device for a microwave scatterometer that measures near-surface wind speed and direction under all weather and cloud conditions. Wind speed measured at 10 m above from the sea surface as extrapolated to the hub height by using the power law model. It has been found that the high wind energy prevailing in the south sea and the east sea of the Korean peninsula. From the limitation of seawater depth for piling the tower and archipelagic environment around the south sea, the west and the south-west sea are favorable to construct the large scale wind farm. Wind map and monthly variation of wind speed are investigate at the positions.