• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.424-433
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• 2021

The aim of this study is to explores the possibility of applying new aquaponics using guppies, a tropical fish breeding as companion fish at home, different from the aquaponics system using fish species such as loach, carp, and catfish for commercial purposes. To facilitate the application of Aquaponics at home, a system was established by connecting a water tank, water plants, hydroponic pots, plant growth LEDs, and Arduino sensors using Internet of Things(IoT) technology. As a hydroponic crops, lettuce that can be easily obtained and consumed at home was selected. In order to confirm the applicability of aquaponics using tropical fish, the growth rates of hydroponic crops in the same environment were compared as a control. The growth rate of aquaponics crops using tropical fish was about 77.4% of that of hydroponic crops. This will produce the same effect as hydroponic cultivation if conditions correspond with enough fish quantity to feed plant and appropriate pH control for growth are met. It can be seen that, and in the future, it can be used to develop an Aquaphonics standard system applicable at home.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1209-1211
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• 2012

최근 들어 모바일 단말기의 보급이 확대되면서 스마트 워크, NFC, USN등 사회 전반적으로 많은 분야에서 활용도가 높아지고 있다. 이에 본 논문에서는 모바일 단말기를 활용하여 농가에서 시설 원예작물의 생장 및 생산량을 예측하고 데이터를 관리하기 위한 연구를 진행하여 농가에서의 모바일 단말기 활용을 돕고 시설 원예작물의 재배에 도움이 되고자 한다.

• Smart Media Journal
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• v.12 no.10
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• pp.102-109
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• 2023

This study aimed to investigate the growth parameters and antioxidant activity of Cnidium officinale under controlled temperature and carbon dioxide levels during the cultivation period. The plants were cultivated for four months, each group being set at the average temperature of the cultivation area +1.8℃/445ppm(SSP1), +3.6℃/872ppm(SSP3), and +4.4℃/1,142ppm(SSP5), respectively. During the cultivation period, the growth, Top/Root ratio, and leaf weight ratio(LWR) of C. officinale slightly decreased in SSP3 and SSP5 compared to SSP1, while the root weight ratio(RWR) increased. The antioxidant activity and related phenolic compound content in the aerial parts of C. officinale increased proportionally with temperature and CO₂ concentration. However, an adverse effect was observed in the high-concentration SSP5 group. Conversely, in the roots, the SSP5 group exhibited the highest antioxidant activity. This study suggests that it can be utilized as fundamental data necessary for understanding the correlation between environmental conditions and the growth as well as physiological activities of medicinal plants.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.47-55
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• 2000

In oreder to predict the productions of major crops such as rice, barely, soybean and potato in Kyongsang Puk Do as early as possible, an attempt has been made to develop some prediction model of crop yields, using the data from the Statistical Yearbooks of Agriculture, Forestry and Fisheries from 1966 through 1999. Among the various models considered, $y=\exp({\beta}_{0}+{\beta}_{1}t+{\epsilon})$ was best fit to the planted area of the crops and $y=\exp({\beta}_{0}+{\beta}_{1}t^{1/2}+{\beta}_{2}t+{\sum}^{p}_{i=1}{\beta}_{i}+_2x_i+{\epsilon})$ to the yields. The $R^{2}$ values for the planted areas were $0.9180{\sim}0.9505$, implying good prediction, while that for rice was 0.7234 and those for barley, soybean and potato were $0.8855{\sim}0.9098$, Predictions have also been made for the planted areas upto the year 2005 and yield for the year 2000.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.265-266
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• 2021

In this paper, we show the case of establishing a outdoor production system using the Internet of things and define the environmental variables in the outdoor production system. By measuring soil temperature, water content, electrical conductivity and acidity through sensors, LoRa communication module transmits the information to the outdoor production system. The outdoor production system controls the amount of fertilizer and the volume of water based on this sensor data. We have developed a system that manages a wide range of crops using LoRa technology, which is a suitable communication method for cultivating crops, and manages production volume and sales performance.

• Journal of Plant Biotechnology
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• v.47 no.4
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• pp.261-272
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• 2020

Approvals for cultivation and import of genetically modified (GM) crops have dramatically increased around the world. Comparative compositional studies are an important aspect of safety assessments of products from GM crops and are based on substantial equivalence. Compositional analyses focus on determining similarities and differences between the compositions of the GM crops and their conventional counterparts, and thereby assessing the compositional equivalence of GM crops and their conventional comparators. The analytes, such as major constituents, key nutrients, and antinutrients, are generally determined on a crop-specific basis according to the OECD consensus document. The use of standard methods throughout the processes, such as selection of comparators, field trials, analytical methods, and statistical data analysis, is crucial. In this study, we showed the general framework of compositional studies. Literature for compositional studies of GM crops conducted abroad and in Korea was reviewed to obtain information about analytes, conventional counterparts, cultivation year, location, and statistical methods. The studies conducted abroad assessed for commercial release of GM crops such as soybean, maize, and cotton, while domestic studies were mainly performed for research in rice. In addition, we suggested a guidance for conventional comparators and field trials applicable to the domestic situation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.283-284
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• 2021

The domestic and overseas IoT (Internet of Things)-based automation industry is developing remarkably, and the development of this automation technology is further accelerated by the development of sensor technology. In recent years, the smart farm industry for the purpose of growing crops based on various sensor technologies is rapidly developing. In the case of smart farms, real-time monitoring and mobile services are provided by measuring representative environmental data such as temperature, humidity, and CO2 required for crop cultivation. Most of these environmental monitoring and control operations use the RS-485-based Modbus (RTU) communication method. In this paper, we intend to test the performance of sensor data and actuator information required for smart farm construction by building a platform for controlling sensor data and actuators based on LabView using MQTT (Message Queuing Telemetry Transport), an IoT standard protocol.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.34-45
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• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.

• The Korean Journal of Pesticide Science
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• v.13 no.1
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• pp.28-38
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• 2009

An extrapolation of residue data of seven commonly used pesticides namely bifenthrin, chlorothalonil, cypermethrin, diazinon, fenvalerate, phenthoate and procymidone on a total of 22 minor crops has been carried out in an experimental field trial. The pesticides were applied to 11 leafy-, 5 root- and 6 stem-crops grown in the experimental green-house and the crops and plants were randomly collected at 1, 3, 5, 7 days after application. The average recoveries of applied pesticides were ranged from 72.0 to 117.0% in leafy crops, from 81.3 to 105.0% in stem crops and from 70.1 to 108.1% in the root-crops. Limits of detection (LODs) were 0.005-0.1 mg/kg in the leafy crops and 0.001-0.005 mg/kg in both the stem & root crops. Based on the results of residual dissipation pattern and their morphology, all crops were classified into high and low residual groups. The results showed that it might be possible to extrapolate residual data of stem-crops to root-crops within the same group. Crops that have currently no registered pesticide for use, would be possible to use the pesticides which are already been registered for the similar crops.