• Title/Summary/Keyword: smart farming

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Policy Direction for Smart Farming

  • 한국농식품정보과학회
    • Agribusiness and Information Management
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    • v.12 no.2
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    • pp.24-39
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    • 2021
  • As the number of fields adopting ICT (Information and Communication Technology) increased, it was necessary to diagnose the information service status of corporations and present improvement plans in agriculture. Therefore, we designed and conducted various surveys to understand the information service status of domestic agricultural company corporations.The research included ways to utilize information technology, establishing infrastructure related to information technology, current status of information technology application and impact on performance. Specifically, the main purpose of this study was to subdivide related corporations by industry and sales level and provide differentiated management implications for each sector. This is because the type of information service and information technology support that each corporation needs varies greatly depending on the industry and sales level. We provide customized management and policy proposals based on descriptive statistics and regression techniques.

A Study on the Analysis of Agricultural and Livestock Operations Using ICT-Based Equipment

  • Gokmi, Kim
    • International journal of advanced smart convergence
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    • v.9 no.1
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    • pp.215-221
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    • 2020
  • The paradigm of agriculture is also changing to address the problem of food shortages due to the increase of the world population, climate conditions that are increasingly subtropical, and labor shortages in rural areas due to aging population. With the development of Information Communication Technology (ICT), our daily lives are changing rapidly and heralds a major change in agricultural management. In a hyper-connected society, the introduction of high-tech into traditional Agriculture of the past is absolutely necessary. In the development process of Agriculture, the first generation produced by hand, the second generation applied mechanization, and the third generation introduced automation. The fourth generation is the current ICT operation and the fifth generation is artificial intelligence. This paper investigated Smart Farm that increases productivity through convergence of Agriculture and ICT, such as smart greenhouse, smart orchard and smart Livestock. With the development of sustainable food production methods in full swing to meet growing food demand, Smart Farming is emerging as the solution. In overseas cases, the Netherlands Smart Farm, the world's second-largest exporter of agricultural products, was surveyed. Agricultural automation using Smart Farms allows producers to harvest agricultural products in an accurate and predictable manner. It is time for the development of technology in Agriculture, which benchmarked cases of excellence abroad. Because ICT requires an understanding of Internet of Things (IoT), big data and artificial intelligence as predicting the future, we want to address the status of theory and actual Agriculture and propose future development measures. We hope that the study of the paper will solve the growing food problem of the world population and help the high productivity of Agriculture and smart strategies of sustainable Agriculture.

Anomaly Detection System of Smart Farm ICT Device (스마트팜 ICT기기의 이상탐지 시스템)

  • Choi, Hwi-Min;Kim, Joo-Man
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.19 no.2
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    • pp.169-174
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    • 2019
  • This paper propose a system to notify the user that detects failure of malfunction of smart farm ICT devices. As the fourth industrial revolution approaches, agriculture is also fused with ICT technology to improve competitiveness. Smart farming market is rapidly growing every year, but there is still a lack of standardization and certification systems. Especially, smart farm devices that are widely used in Korea are different in product specifications, software and hardware are developed separately, and quality and compatibility are poor. Therefore, a system that can recognize the abnormality of the equipment due to the frequent damage of farmers using low cost smart farm equipment is needed. In this paper, we review smart farm domestic and overseas policy trends and domestic smart agriculture trends, analyze smart farm failure or malfunctions and proactively prevent them, and propose a system to inform users when problems occur.

A Study on Effects of Adopting ICT in Livestock Farm Management on Farm Sales Revenue (정보화기기 활용이 국내 축산농가 총판매금액에 미치는 영향 분석)

  • Hanna Jeong;Jimin Shim;Yerin Lim;Jongwook Lee
    • Journal of Korean Society of Rural Planning
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    • v.30 no.1
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    • pp.81-97
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    • 2024
  • This study examines the effects of adopting Information and Communication Technology (ICT) in livestock farm management on farm sales revenue. Using the 2020 Census of Agriculture, Forestry, and Fisheries, a nationally representative data set constructed by Statistics Korea, this study focuses on a sample of 9,020 livestock farms in South Korea. We employ Propensity Score Matching (PSM) methods to address the potential selection bias between 2,076 farms that used ICT for livestock farm management and 6,944 farms that did not. The findings consistently show that the use of ICT significantly increases farm revenue, taking into account the selection bias. The utilization of ICT in livestock farms leads to a higher increase in sales revenue, particularly for farms with greater sales.

Analysis of Management Performance of Young Farmers in Smart Farm Innovation Valley (스마트팜 혁신밸리 입주 청년농업인의 경영성과 분석)

  • Geun Ho Shimg;Geum Yeong Hwang;So Young Lee;Ji Bum Um
    • Journal of Practical Agriculture & Fisheries Research
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    • v.25 no.4
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    • pp.67-77
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    • 2024
  • This study analyzed the profitability and diagnosed business performance of fruit and vegetable (cherry tomatoes, tomatoes, strawberries, cucumbers) businesses targeting young farmers participating in the youth business incubation center of A Smart Farm Innovation Valley. The purpose of this is to provide basic data for decision-making by prospective young entrepreneurs. As a result of the analysis, Smart Farm Innovation Valley had the advantage of having a fixed rental fee. As a result, it was analyzed that various costs such as depreciation of large farm equipment, depreciation of farming facilities, repair and maintenance costs, land rent, floating capital service cost, fixed capital service cost, and land capital service cost are being reduced. However, excessive input of labor, water, electricity, other materials, and fertilizer costs was being made. Guidance to reduce these costs is expected to make a significant contribution to expanding the influx of young farmers.

Smart Plant Disease Management Using Agrometeorological Big Data (농업기상 빅데이터를 활용한 스마트 식물병 관리)

  • Kim, Kwang-Hyung;Lee, Junhyuk
    • Research in Plant Disease
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    • v.26 no.3
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    • pp.121-133
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    • 2020
  • Climate change, increased extreme weather and climate events, and rapidly changing socio-economic environment threaten agriculture and thus food security of our society. Therefore, it is urgent to shift from conventional farming to smart agriculture using big data and artificial intelligence to secure sustainable growth. In order to efficiently manage plant diseases through smart agriculture, agricultural big data that can be utilized with various advanced technologies must be secured first. In this review, we will first learn about agrometeorological big data consisted of meteorological, environmental, and agricultural data that the plant pathology communities can contribute for smart plant disease management. We will then present each sequential components of the smart plant disease management, which are prediction, monitoring and diagnosis, control, prevention and risk management of plant diseases. This review will give us an appraisal of where we are at the moment, what has been prepared so far, what is lacking, and how to move forward for the preparation of smart plant disease management.

Size Estimation for Shrimp Using Deep Learning Method

  • Heng Zhou;Sung-Hoon Kim;Sang-Cheol Kim;Cheol-Won Kim;Seung-Won Kang
    • Smart Media Journal
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    • v.12 no.3
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    • pp.112-119
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    • 2023
  • Shrimp farming has been becoming a new source of income for fishermen in South Korea. It is often necessary for fishers to measure the size of the shrimp for the purpose to understand the growth rate of the shrimp and to determine the amount of food put into the breeding pond. Traditional methods rely on humans, which has huge time and labor costs. This paper proposes a deep learning-based method for calculating the size of shrimps automatically. Firstly, we use fine-tuning techniques to update the Mask RCNN model with our farm data, enabling it to segment shrimps and generate shrimp masks. We then use skeletonizing method and maximum inscribed circle to calculate the length and width of shrimp, respectively. Our method is simple yet effective, and most importantly, it requires a small hardware resource and is easy to deploy to shrimp farms.

A TabNet - Based System for Water Quality Prediction in Aquaculture

  • Nguyen, Trong–Nghia;Kim, Soo Hyung;Do, Nhu-Tai;Hong, Thai-Thi Ngoc;Yang, Hyung Jeong;Lee, Guee Sang
    • Smart Media Journal
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    • v.11 no.2
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    • pp.39-52
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    • 2022
  • In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

Sensor technology for environmental monitoring of shrimp farming (새우양식 환경 모니터링을 위한 센서기술 동향 분석)

  • Hur, Shin;Park, Jung Ho;Choi, Sang Kyu;Lee, Chang Won;Kim, Ju Wan
    • Journal of Sensor Science and Technology
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    • v.30 no.3
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    • pp.154-164
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    • 2021
  • In this study, the IoT sensor technology required for improving the survival rate and high-density productivity of individual shrimp in smart shrimp farming (which involves the usage of recirculating aquaculture systems and biofloc technology) was analyzed. The principles and performances of domestic and overseas water quality monitoring IoT sensors were compared. Furthermore, the drawbacks of existing aquaculture monitoring technologies and the countermeasures for future aquaculture monitoring technologies were examined. In particular, for farming white-legged shrimp, an IoT sensor was employed to collect measurement indicators for managing the water quality environment in real-time, and the IoT sensor-based real-time monitoring technology was then analyzed for implementing the optimal farming environment. The results obtained from this study can potentially contribute to the realization of an autonomous farming platform that can improve the survival rate and productivity of shrimp, achieve feed reduction, improve the water quality environment, and save energy.

A Study on the Necessity and Construction Plan of the Internet of Things Platform for Smart Agriculture (스마트 농업 확산을 위한 IoT기반 개방형 플랫폼의 필요성 및 구축 방안 연구)

  • Lee, Joonyoung;Kim, ShinHo;Lee, SaeBom;Choi, HyeonJin;Jung, JaiJin
    • Journal of Korea Multimedia Society
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    • v.17 no.11
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    • pp.1313-1324
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    • 2014
  • Korea has high quality level of ICT Technologies, however it still have a long way to go before invigoration of ICT in agriculture industry. The government of Korea supply to agriculture ICT systems, however these are the enclosed type and insufficient the level of connectivity, compatibility, and integrity between ICT systems. Farmers can not share crop information and one system can not use with others in combination. Recently, IoT(Internet of Things) become popular to emphasize the vision of a global internet and ICT industry. The IoT is a critical technology that leads future internet generation. We believe that IoT will change status of agriculture industry and appearance of various agriculture business model. Using IoT technology is provided a platform of opportunities to optimize work processes and efficient use of energy, time and labour in farm. It can automatically control temperature, humidity, sunshine system and so on for optimal growth conditions at greenhouse and plant factory. Growth setting can even be controlled and monitored crop condition and disease by a smartphone app or PC. It is possible to improve quality of farming and farm product. We suggest that construction of IoT platform through open API in agriculture industry.