• Smart Media Journal
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• v.12 no.1
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• pp.47-53
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• 2023

Recently, research on a smart farm that creates new values by combining information and communication technology(ICT) with agriculture has been actively done. In order for domestic smart farm technology to have productivity at the same level of advanced agricultural countries, automated decision-making using machine learning is necessary. However, current smart greenhouse data collection technologies in our country are not enough to perform big data analysis or machine learning. In this paper, we design and implement a smart greenhouse data preprocessing system for autonomous machine learning. The proposed system applies target data to various preprocessing techniques. And the proposed system evaluate the performance of each preprocessing technique and store optimal preprocessing technique for each data. Stored optimal preprocessing techniques are used to perform preprocessing on newly collected data

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.61-67
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• 2024

In order to monitor the growth environment of new varieties of crops, it is necessary to build the agricultural production infrastructure and strengthen the agricultural resource management system using popular smart sensor tag technology. In addition, the infrastructure for improving high-quality new varieties of crops using IoT technology and the monitoring system must be strengthened. In other words, widespread smart sensor (RFID UHF Sensor Tag) technology for environmental monitoring required for improving new crop varieties is desperately needed in the smart farm environment. Therefore, in this paper, we implemented an integrated sensor that can implement smart tag functions based on heterogeneous integrated sensors. In addition, we developed a technology that can manage crops in real time through the implemented smart integrated tag and smartphone linkage. For this purpose, an integrated antenna capable of RFID and Bluetooth communication was constructed. In addition, a communication method that allows information to be collected directly from the smartphone through the Bluetooth function was used.

• Smart Media Journal
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• v.13 no.1
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• pp.18-23
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• 2024

Power efficiency management in smart farms is important due to its link to climate change. As climate change negatively impacts agriculture, future agriculture is expected to utilize smart farms to minimize climate impacts, but smart farms' power consumption may exacerbate the climate crisis due to the current electricity production system. Therefore, it is essential to efficiently manage and optimize the power usage of smart farms. In this study, we propose a system that monitors the power usage of smart farm equipment in real time and predicts the power usage one hour later using GRU. CT sensors are installed to collect power usage data, which are analyzed to detect and prevent abnormal patterns, and combined with IoT technology to efficiently manage and monitor the overall power usage. This helps to optimize power usage, improve energy efficiency, and reduce carbon emissions. The system is expected to improve not only the energy management of smart farms, but also the overall efficiency of energy use.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.145-152
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• 2020

In this paper, we propose the Crop Classification Management System (CCMS) that detects changes in growth environment to improve crop production rate. The CCMS consists of two modules. First, the Crop Classification Module (CCM) classifies crops through CNN. Second, the Farm Anomaly Detection Module (FADM) detects abnormal crops by comparing accumulated data of farms. The CCM recognizes crops currently grown on farms and sends them to the FADM, and the FADM picks up the weather data from the past to the present day of the farm growing the crops and applies them to the Nelson rules. The FADM uses the Nelson rules to find out weather data that has occurred and adjust farm conditions through IoT devices. The performance analysis of CCMS showed that the CCM had a crop classification accuracy of about 90%, and the FADM improved the estimated yield by up to about 30%. In other words, managing farms through the CCMS can help increase the yield of smart farms.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.27-33
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• 2018

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.

• Journal of Korean Society of Rural Planning
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• v.28 no.1
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• pp.17-26
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• 2022

It is necessary to prepare a stable production base in advance for a change in the global grain market, and it is required to prepare comprehensive countermeasures such as securing technical skills and cultivation technology. Therefore, Korea, which relies on imports of major grains other than rice, could be exposed to a food crisis at any time unless the self-sufficiency rate of grains is improved. In order to respond to this new food crisis, it is necessary to find ways to efficiently utilize rice fields to increase the domestic grain self-sufficiency rate. From this point of view, interest and demand for the generalization of farmland that can be used as paddy fields and returned to paddy fields are increasing, and related research is also being continuously performed. In order to select a multipurpose farmland project site, this study extracted farmland containing 10% or more purchased and stockpiled farmland through spatial analysis (buffer, dissolve, intersect, etc.), and finally presented areas subject to multipurpose farmland projects. The target site for the multipurpose farmland project was finally selected by integrating data onto a point-by-point basis so that the current status of farmland purchased and stockpiled, Farm Manager Registration Records, and the Korean Soil Information System data (drainage classes, surface soil texture, field-suitability classification, etc.) can be used in combination. There are 175 areas where the multipurpose farmland is possible. Incheon 2, Gyeongbuk 40, Gangwon 2, Chungbuk 7, Chungnam 48, Jeonbuk 34, Jeonnam 19, Gyeongbuk 15, Gyeongnam 8. Chungcheongnam-do has the most target site for the multipurpose farmland project, and Gangwon-do is the least. It is expected to contribute to new commercialization and business expansion by deriving business areas by identifying the scale of the farmland multipurpose farmland project using Farm Manger Registration Records and spatial data.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.107-112
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• 2018

Smart livestock has been proposed as a solution to increase farmers' income and new recruitment of livestock farmers. In this paper, a standardization Plan of breeding management device and collected information for smart livestock cattle was proposed. l Sophisticatedly, basic information will be established for all six types of livestock breeding management device: military automatic feeder, calf automatic feeder, smart milk cooler, feed bin to be able to measure feed residue, smart scale, and biometric information collection device. The standardization, common use, and stabilization of major livestock management device and collected information were suggested to solve the problems caused by in existing breeding management device.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.834-835
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• 2023

'비프팜(VIP-farm)'은 'video processing farm'의 약자로 스마트팜으로써 컨테이너에 부착한 센서와 카메라로 조도·온도·습도 등의 내부 정보를 자동으로 취득 및 분석하여 성장 환경을 원격 제어한다. 뿐만 아니라, 기존 스마트팜의 과육 정보량 부족을 보완하기 위해 영상 처리를 이용하여 과일의 개수와 숙성도를 평가하고 적정 수확 시기를 안내하며, 사용자 간 정보공유 및 소통이 가능하도록 하는 기능을 가진다.

• The Journal of Korea Robotics Society
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• v.16 no.1
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• pp.49-55
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• 2021

Recently, a smart farm technology is drawing attention as an alternative to the decline of farm labor population problems due to the aging society. Especially, there is an increasing demand for automatic harvesting system that can be commercialized in the market. Pre-harvest crop detection is the most important issue for the harvesting robot system in a real-world environment. In this paper, we proposed a real-time tomato instance tracking algorithm by using deep learning and probability models. In general, It is hard to keep track of the same tomato instance between successive frames, because the tomato growing environment is disturbed by the change of lighting condition and a background clutter without a stochastic approach. Therefore, this work suggests that individual tomato object detection for each frame is conducted by YOLOv3 model, and the continuous instance tracking between frames is performed by Kalman filter and probability model. We have verified the performance of the proposed method, an experiment was shown a good result in real-world test data.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.147-157
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• 2022

This study tried to propose a smart farm technology strategy suitable for the domestic situation, focusing on the differentiation suitable for the domestic situation of ICT technology. In the case of advanced countries in the overseas agricultural industry, it was confirmed that they focused on the development of a specific stage that reflected the geographical characteristics of each country, the characteristics of the agricultural industry, and the characteristics of the people's demand. Confirmed that no enemy development is being performed. Therefore, in response to problems such as a rapid decrease in the domestic rural population, aging population, loss of agricultural price competitiveness, increase in fallow land, and decrease in use rate of arable land, this study aims to develop smart farm ICT technology in the future to create quality agricultural products and have price competitiveness. It was suggested that the smart farm should be promoted by paying attention to the excellent performance, ease of use due to the aging of the labor force, and economic feasibility suitable for a small business scale. First, in terms of economic feasibility, the ICT technology is configured by selecting only the functions necessary for the small farm household (primary) business environment, and the smooth communication system with these is applied to the ICT technology to gradually update the functions required by the actual farmhouse. suggested that it may contribute to the reduction. Second, in terms of performance, it is suggested that the operation accuracy can be increased if attention is paid to improving the communication function of ICT, such as adjusting the difficulty of big data suitable for the aging population in Korea, using a language suitable for them, and setting an algorithm that reflects their prediction tendencies. Third, the level of ease of use. Smart farms based on ICT technology for the development of the Industry6.0 (1.0(Agriculture, Forestry) + 2.0(Agricultural and Water & Water Processing) + 3.0 (Service, Rural Experience, SCM)) perform operations according to specific commands, finally suggested that ease of use can be promoted by presetting and standardizing devices based on big data configuration customized for each regional environment.