• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.5
• /
• pp.563-569
• /
• 2018

This paper is on the implementation of a system for automatically monitoring window farm hydroponics based on Arduino (utilizing Arduino's open source code) emerging as the icon of the Fourth Industrial Revolution. A window farm, which means window-type hydroponics, is offered as an alternative to fulfill the desires of people who want to grow plants aside from the busy daily life in the city. The system proposed in this paper was developed to automatically monitor a window farm hydroponics cultivation environment using the Arduino UNO board, a four-charmel motor shield, temperature and humidity sensors, illumination sensors, and a real-time clock module. Modules for hydroponics have been developed in various forms, but power consumption is high because most of them use general power and motors. Since it is not a system that is monitored automatically, there is a disadvantage in that an administrator always has to manage its operational state. The system is equipped with a water supply that is most suitable for a plant growth environment by utilizing temperature, humidity, and light sensors, which function as Internet of Things sensors. In addition, the real-time clock module can be used to provide a more appropriate water supply. The system was implemented with sketch code in a Linux environment using Raspberry Pi 3 and Arduino UNO.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.16 no.2
• /
• pp.3395-3409
• /
• 1974

The purpose of this study is to test and compare the efficiency of heating-system for materials and construction of the wall, ceiling and window in soil brick house, cement house and boulder house respectively, in order to construct ideal farm houses in rural area. The results obtained were as follows: 1. In heat conservation due to construction of walls the thermal efficiency of cement brick house was equivalent to 66.3% of that of soil brick house, and boulder house 60.3% 2. In the case of ceiling, the thermal efficiency of paper ceiling was amounted to 84.2% of that of the composite ceiling (thickness 6mm veneer+thickness. l0m chaffs), and the common ceiling putting on soil above the ceiling, 76% of the composite while the efficiency of the ceiling putting on chaffs above them was 15.8% higher than that of the paper. 3. In the case of improving the window, the double type was 12% higher than. the efficiency of single type. 4. The warming velocity of conventional house was slower but the velocity of radiation was quicker than that of experimental one. It was thought to be due to unscietific constructions of the room bottom, fire inlet and chimney, 5. The temperature gradient line was not dependad upon the amount of throwing into fuel in the rural farm house. 6. It was concluded that the final thermal efficiency of the conventional farm house was 10.6% lower than that of experimental farm house.

• Journal of People, Plants, and Environment
• /
• v.23 no.6
• /
• pp.655-665
• /
• 2020

Background and objective: This study was conducted to compare the cultivation environment, growth of cut flowers, and management performance of conventional farms and smart farms growing the standard cut chrysanthemum, 'Jinba'. Methods: Conventional and smart farms were selected, and facility information, cultivation environment, cut flower growth, and management performance were investigated. Results: The conventional and smart farms were located in Muan, Jeollanam-do, and conventional farming involved cultivating with soil culture in a plastic greenhouse, while the smart farm was cultivating with hydroponics in a plastic greenhouse. The conventional farm did not have sensors for environmental measurement such as light intensity and temperature and pH and EC sensors for fertigation, and all systems, including roof window, side window, thermal screen, and shading curtain, were operated manually. On the other hand, the smart farm was equipped with sensors for measuring the environment and nutrient solution, and was automatically controlled. The day and night mean temperatures, relative humidity, and solar radiation in the facilities of the conventional and the smart farm were managed similarly. But in the floral differentiation stage, the floral differentiation was delayed, as the night temperature of conventional farm was managed as low as 17.7℃ which was lower than smart farm. Accordingly, the harvest of cut flowers by the conventional farm was delayed to 35 days later than that of the smart farm. Also, soil moisture and EC of the conventional farm were unnecessarily kept higher than those of the smart farm in the early growth stage, and then were maintained relatively low during the period after floral differentiation, when a lot of water and nutrients were required. Therefore, growth of cut flower, cut flower length, number of leaves, flower diameter, and weight were poorer in the conventional farm than in the smart farm. In terms of management performance, yield and sales price were 10% and 38% higher for the smart farm than for the conventional farm, respectively. Also, the net income was 2,298 thousand won more for the smart farm than for the conventional farm. Conclusion: It was suggested that the improved growth of cut flowers and high management performance of the smart farm were due to precise environment management for growth by the automatic control and sensor.

• Journal of IKEEE
• /
• v.22 no.1
• /
• pp.53-60
• /
• 2018

Smart farm dissemination by continuously developing IoT is one of the best solution for decreasing labor in Korea farming area because of ageing. For this reason, the number of Smart farm in Korea is being increased. The Smart farm can control farming environment such as temperature for human. Specially, The important thing is controlling proper temperature for farming. In order to control the temperature, legacy smart farms are usually using pans or air conditioners which can control the temperature. However, those devices result in increasing production cost because the electric power consumption is high. For this reason, we propose a smart farm which can predict the proper temperature after an hour by using Deep learning to minimize the electric power consumption by controlling window instead of pans or air conditioners. We can see the 83% of electric power saving by means of the proposed smart farm.

• Journal of Korean Society of Rural Planning
• /
• v.25 no.1
• /
• pp.21-38
• /
• 2019

Korean agricultural industry has weakened as demand for domestic agricultural products has declined due to accelerating market liberalization, aging and shrinking of rural population, and stagnating rural households' incomes. On the other hand, as the forth industrial revolution unfolds in earnest, tremendous changes are expected, and those changes won't be confined to certain industries but would shaken the world we know of entirely. Smart farm, which is one example of the fourth industrial revolution, is increasingly being recognized as a new growth engine for the future as smart farm and the science and technology behind it, not the size of arable land, will determine competitiveness of the agricultural industry and drive agricultural productivity and managerial efficiency. In consideration that John W. Kingdon's Multiple Streams Framework has recently been presented as an important theoretical model in the policy field, this study analyzed problem stream, policy stream, and political stream in the process of forming the smart farm policy, and looked into what role the government played as policy entrepreneur in policy window. The smart farm policy was put on policy agenda by the government and was approved when the government announced the Smart Farm Plan together with relevant ministries at the 5th Economy-Related Ministers' Meeting held in April 2018. This suggests that change of the government is the most critical factor in political stream, and explicitly indicates the importance of politics in formation of an agricultural policy. In addition, actual outcome of the policy and how policy alternatives that will enhance people's understanding will support it seem to be the key to success. It also shows that it is important that policy alternatives be determined based on sufficient discussion amongst stakeholders.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.28 no.3
• /
• pp.309-315
• /
• 1995

This paper describes a monitoring system for fish farm formation effect based on personal computer by using an image processing technique. This method is based on image processing technique incorporating concept of window and threshold processing to track the target object and to distinguish it from background. The image processing program runs in the veal time so that all program modules are able to process multi-task. The effectiveness is evaluated through the comparative study on the motion of lantern net for the scallop culturing by wave action in an experimental wave tank.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.27 no.5
• /
• pp.358-363
• /
• 2015

As a preparation process for successful establishment of demonstration offshore wind farm, analyses have been made for working time at the construction site where working time is defined as the time available for marine operation to take place under given weather conditions. Data used are hourly wave and wind data from met mast, HeMOSU-1, and 3 hour numerical model data from Korea Meteorological Administration (KMA). Seasonal results show the minimum working time during winter and moderate during autumn and spring. The most working time was seen during summer on average. Monthly analyses show the most working time in May, June, and August which was higher than the working time in July and September. Working time reaches at steady state and no significant change was seen above wave height of 1.5 m and wind speed of 8 m/s.

• Journal of Korean Society for Quality Management
• /
• v.49 no.4
• /
• pp.569-579
• /
• 2021

Purpose: The purpose of this study is the time series analysis for predicting the yield of crops applicable to each farm using environmental variables measured by smart farms cultivating tomato. In addition, it is intended to confirm the influence of environmental variables using a deep learning model that can be explained to some extent. Methods: A time series analysis was performed to predict production using environmental variables measured at 75 smart farms cultivating tomato in two periods. An LSTM-based encoder-decoder model was used for cases of several farms with similar length. In particular, Dual Attention Mechanism was applied to use environmental variables as exogenous variables and to confirm their influence. Results: As a result of the analysis, Dual Attention LSTM with a window size of 12 weeks showed the best predictive power. It was verified that the environmental variables has a similar effect on prediction through wieghtss extracted from the prediction model, and it was also verified that the previous time point has a greater effect than the time point close to the prediction point. Conclusion: It is expected that it will be possible to attempt various crops as a model that can be explained by supplementing the shortcomings of general deep learning model.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.8A
• /
• pp.1115-1123
• /
• 2000

Most of the existing web caches are used in huge organizations. But many internet users belong to small organizations such as a venture company or a PC room. Users are in general in multiple window environments, and use several programs concurrently with rapid preference change within a relatively short period of time. We develop a network-path based algorithm. It organizes a cache according to the network paths of the requested URLs and builds a network cache farm where caches are logically connected with each other and each cache has its own preference over certain network paths. The algorithm has been implemented and tested in a real site. The performance results show that the new algorithm outperforms the existing algorithms in the hit ratio and response time dramatically with low cost.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1154-1154
• /
• 2001

Near infra-red (NIR) spectroscopy has been used for the non-invasive assessment of intact fruit for eating quality attributes such as total soluble solids (TSS) content. However, little information is available in the literature with respect to the robustness of such calibration models validated against independent populations (however, see Peiris et al. 1998 and Guthrie et al. 1998). Many studies report ‘prediction’ statistics in which the calibration and prediction sets are subsets of the same population (e. g. a three year calibration validated against a set from the same population, Peiris et al. 1998; calibration and validation subsets of the same initial population, Guthrie and Walsh 1997 and McGlone and Kawano 1998). In this study, a calibration was developed across 84 melon fruit (R$^2$= 0.86$^{\circ}$Brix, SECV = 0.38$^{\circ}$Brix), which predicted well on fruit excluded from the calibration set but taken from the same population (n = 24, SEP = 0.38$^{\circ}$Brix with 0.1$^{\circ}$Brix bias), relative to an independent group (same variety and farm but different harvest date) (n = 24, SEP= 0.66$^{\circ}$ Brix with 0.1$^{\circ}$Brix bias). Prediction on a different variety, different growing district and time was worse (n = 24, SEP = 1.2$^{\circ}$Brix with 0.9$^{\circ}$Brix bias). Using an ‘in-line’ unit based on a silicon diode array spectrometer, as described in Walsh et al. (2000), we collected spectra from fruit populations covering different varieties, growing districts and time. The calibration procedure was optimized in terms of spectral window, derivative function and scatter correction. Performance of a calibration across new populations of fruit (different varieties, growing districts and harvest date) is reported. Various calibration sample selection techniques (primarily based on Mahalanobis distances), were trialled to structure the calibration population to improve robustness of prediction on independent sets. Optimization of calibration population structure (using the ISI protocols of neighbourhood and global distances) resulted in the elimination of over 50% of the initial data set. The use of the ISI Local Calibration routine was also investigated.