• KOREAN JOURNAL OF CROP SCIENCE
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• v.69 no.1
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• pp.49-60
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• 2024

A region can be divided into cultivation zones based on homogeneity in weather variables that have the greatest influence on crop growth and yield. This study classified the cultivation zone of soybean using weather indices as a prior study to classify the agroclimatic zone of soybean. Meteorological factors affecting soybeans were determined through correlation analysis over a 10 year period (from 2013 to 2022) using data from the Miryang and Suwon regions collected from the soybean yield trial database of the Rural Development Administration, Korea and the meteorological database of the Korea Meteorological Administration. The correlation between growth characteristics and the minimum temperature, daily temperature range, and precipitation were high during the vegetative growth stages. Moreover, the correlation between yield components and the maximum temperature, daily temperature range, and precipitation were high during the reproductive growth stages. As a result of k-means clustering, soybean cultivation zones were divided into three zones. Zone 1 was the central inland region and southern Gyeonggi-do; Zone 2 was the southern part of the west coast, the southern part of the east coast, and the South Sea; and Zone 3 included parts of eastern Gyeonggi-do, Gangwon-do, and areas with high altitudes. Zone 1, which has a wide latitude range, was further subdivided into three cultivation zones. The results of this study may provide useful information for estimating agrometeorological characteristics and predicting the success of soybean cultivation in South Korea.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.355-362
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• 2022

Smart Farm, which combines information and communication technologies with agriculture is moving from simple monitoring of the growth environment toward discovering the optimal environment for crop growth and in the form of self-regulating agriculture. To this end, it is important to collect related data, but it is more important for farmers with cultivation know-how to analyze the collected data from various perspectives and derive useful information for regulating the crop growth environment. In this study, we developed a web service that allows farmers who want to obtain necessary information with data related to crop growth to easily analyze data. Web-based data analysis serivice developed uses R language for data analysis and Express web application framework for Node.js. As a result of applying the developed data analysis service together with the growth environment monitoring system in operation, we could perform data analysis what we want just by uploading a CSV file or by entering raw data directly. We confirmed that a service provider could provid various data analysis services easily and could add a new data analysis service by newly adding R script.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.63-68
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• 2018

Research on smart farms centering on greenhouse cultivation is actively under way due to the decrease in agriculture population and aging, but in the case of vegetables such as vegetables, outdoor cultivation is 70%. Therefore, there is a need to improve productivity and prevent soil contamination by automating, cultivating, and intelligentizing the outdoor cultivation of agriculture crops. 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.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.861-866
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• 2023

Unlike conventional smart farms that require chemical fertilizers and large spaces, aquaponics farming, which utilizes the symbiotic relationship between aquatic organisms and crops to grow crops even in abnormal environments such as environmental pollution and climate change, is being actively researched. Different crops require different environments and nutrients for growth, so it is necessary to configure the ratio of aquatic organisms optimized for crop growth. This study proposes a method to measure the degree of growth based on area and volume using image processing techniques in an aquaponics environment. Tilapia, carp, catfish, and lettuce crops, which are aquatic organisms that produce organic matter through excrement, were tested in an aquaponics environment. Through 2D and 3D image analysis of lettuce and real-time data analysis, the growth degree was evaluated using the area and volume information of lettuce. The results of the experiment proved that it is possible to manage cultivation by utilizing the area and volume information of lettuce. It is expected that it will be possible to provide production prediction services to farmers by utilizing aquatic life and growth information. It will also be a starting point for solving problems in the changing agricultural environment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.497-498
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• 2017

최근 딸기는 고소득 부가가치 작물로 인식되기 시작하면서 수확작업에 노력이 적게 들고, 재배 및 작업 환경이 개선된 딸기 수경재배에 관심이 높아지고 있다. 현재 우리나라에서도 한국형 배양액 자동공급시스템을 개발하여 보급하기 시작하였지만 딸기 수경재배에서는 배양액의 EC와 pH관리 및 배양액의 급액에 따른 적절한 배액량 구명이 가장 시급하게 필요한 사항이다. 본 논문에서는 생육과 밀접한 관계가 있는 딸기 수경재배 생육 정보를 수집하는 장치를 개발하였고, 이 장치를 통해 딸기 수경재배 생육 데이터들을 수집하고 분석하여 딸기 수경재배 농가에 균일화 된 품질생산 및 수확량 증대에 기여할 수 있을 것으로 기대된다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.521-530
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• 2018

This paper proposes "The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity" that collects weather information based on location supporting precision agriculture, predicts current crop condition by using the collected information and real time crop data, and notifies a farmer of the result. The system works as follows. The ICM(Information Collection Module) collects weather information based on location supporting precision agriculture. The DRCM(Deep learning based Risk Calculation Module) predicts whether the C, H, N and moisture content of soil are appropriate to grow specific crops according to current weather. The RNM(Risk Notification Module) notifies a farmer of the prediction result based on the DRCM. The proposed system improves the stability because it reduces the accuracy reduction rate as the amount of data increases and is apply the unsupervised learning to the analysis stage compared to the existing system. As a result, the simulation result shows that the ADS improved the success rate of data analysis by about 6%. And the ADS predicts the current crop growth condition accurately, prevents in advance the crop diseases in various environments, and provides the optimized condition for growing crops.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.536-537
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• 2019

작물의 생육에 따라 적기에 필요한 양만큼의 양분을 공급해 최고의 생산성을 올릴 수 있는 수경재배는 정보통신기술(ICT)을 융·복합한 스마트 농업 형태로 전환되고 있으나, 기술 발전에도 불구하고 여전히 환경 및 경제성 문제 등 많은 개선점을 가지고 있다. 본 논문에서는 딸기 수경재배지의 환경 데이터 및 생육 데이터를 수집하고, 터치스크린과 스마트폰을 통하여 배양액의 배액량, pH, EC, 온도, 습도를 실시간 및 정한 기간에 따라 모니터링이 가능한 수경재배 소규모 온실을 위한 SBC기반의 모니터링 시스템을 제안한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.908-909
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• 2014

식물공장 시스템은 농민들의 감소와 고령화로 인해 인력 부족으로 작물재배를 하는데 있어 어려운 상황을 해결하고 재배환경을 인공적으로 조절하여 계절에 관계없이 농산물을 생산하는 시스템을 말한다. 또한, 도시 근교 또는 도심 속에서 농산물을 생산할 수 있게 되어 도시 소비자에게 도달하는 거리가 짧아 유통 기간과 비용을 절약할 수도 있는 장점도 있다. 하지만 식물공장은 실내 농업이기 때문에 환경관리가 잘 이루어져야 하는 애로사항이 있다. 본 논문에서는 식물공장 내부의 환경 데이터를 통하여 식물공장의 내부를 효율적으로 관리를 할 수 있도록 필요한 파라메타에 대하여 TCP/IP 기반의 소켓 프로그램을 통하여 실시간으로 DB를 구성하여 구성된 데이터를 스마트폰과 연동하도록 구현하여 사용자가 식물공장 환경을 실시간으로 원격 모니터링 할 수 있는 시스템을 제시하고자 한다.

• Journal of Mushroom
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• v.15 no.3
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• pp.118-123
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• 2017

This study was carried out to develop the technology to manage the growth of mushrooms, which were cultivated based on long-term information obtained from quantified data. In this study, hardware that monitored and controlled the growth environment of the mushroom cultivation house was developed. An algorithm was also developed to grow mushrooms automatically. Environmental management for the growth of mushrooms was carried out using cultivation sites, computers, and smart phones. To manage the environment of the mushroom cultivation house, the environmental management data from farmers cultivating the highest quality mushrooms in Korea were collected and a growth management database was created. On the basis of the database value, the management environment for the test cultivar (hukthali) was controlled at $0.5^{\circ}C$ with 3-7% relative humidity and 10% carbon dioxide concentration. As a result, it was possible to produce mushrooms that were almost similar to those cultivated in farms with the best available technology.