• Journal of Digital Convergence
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• v.18 no.3
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• pp.171-177
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• 2020

Under the influence of the Fourth Industrial Revolution, there are many tries to promote productivity enhancement and competitiveness by adapting smart farm technology that converges ICT technologies in agriculture. This smart farming technology is emerging as a new paradigm for future growth in agriculture. The development of real-time cultivation environment monitoring and automatic control system is needed to implement smart farm. Furthermore, the development of intelligent system that manages cultivation environment using monitoring data of the growth of crops is required. In this paper, a fast and efficient development method for implementing a cloud-based smart farm management system using a highly compatible and scalable web platform is proposed. It was verified that the proposed method using the web platform is effective and stable system implementation through the operation of the actual implementation system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.146-152
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• 2022

Information collected from temperature, humidity, inclination, and pressure sensors using Raspberry Pi or Arduino is used in automatic constant temperature and constant humidity systems. In addition, by using it in the agricultural and livestock industry to remotely control the system with only a smartphone, workers in the agricultural and livestock industry can use it conveniently. In general, temperature and humidity are expressed in a line graph, etc., and the change is monitored in real time. The technology to visually express the temperature has recently been used intuitively by using an infrared device to test the fever of Corona 19. In this paper, the information collected from the Raspberry Pi and the DHT11 sensor is used to predict the temperature change in space through intuitive visualization and to make a immediate response. To this end, an algorithm was created to effectively visualize temperature and humidity, and data representation is possible even if some sensors are defective.

• Convergence Security Journal
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• v.22 no.5
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• pp.69-80
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• 2022

Now that climate change and food resource security are becoming issues around the world, smart farms are emerging as an alternative to solve them. In addition, changes in the production environment in the primary industry are a major concern for people engaged in all primary industries (agriculture, livestock, fishery), and the resulting food shortage problem is an important problem that we all need to solve. In order to solve this problem, in the primary industry, efforts are made to solve the food shortage problem through productivity improvement by introducing smart farms using the 4th industrial revolution such as ICT and BT and IoT big data and artificial intelligence technologies. This is done through the public and private sectors.This paper intends to consider the minimum requirements for the smart farm data collection system for the development and utilization of smart farms, the establishment of a sustainable agricultural management system, the sequential system construction method, and the purposeful, efficient and usable data collection system. In particular, we analyze and improve the problems of the data collection system for building a Korean smart farm standard model, which is facing limitations, based on in-depth investigations in the field of livestock and livestock (pig farming) and analysis of various cases, to establish an efficient and usable big data collection system. The goal is to propose a method for collecting big data.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.487-489
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• 2022

농축산업계는 현재 고효율의 자동화 시스템을 구축하기 위해 많은 연구들이 진행되고 있다. 가축들에 대한 실시간 건강 관리 시스템 구현을 위해 딥러닝, 인공지능 기술들을 활용한 스마트 축사개발에 박차를 가하고 있다. 가축들의 건강은 수익과 직결되는 것은 물론, 사람의 건강까지 위협할 수 있기 때문에 철저한 관리가 필요한 실정이나 여러 기술적 어려움에 부딪히고 있다. 본 연구소는 이를 해결하기 위해 다양한 변수들을 설정, 수집하여 가축들의 건강을 관측하는 기술을 개발하고 있다. 이 논문에서는 악조건에서 수집된 데이터로 우리 내 돼지를 Tracking 한 실험 결과를 소개하고자 한다.

• Journal of the Korean Society for Marine Environment & Energy
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• v.17 no.4
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• pp.283-296
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• 2014

The distribution of heavy metals in the stormwater runoffs from industrial sites around Shihwa Lake that implements the total pollutant load management system (TPLMS) was studied to characterize the temporal changes of metal concentrations and to assess the ecological risk in dissolved and particulate phases of the selected metals. The dissolved Co and Ni concentration demonstrated first flush and tended to decrease with increasing of the duration of rainfall. The intensity of precipitation was found to be the main controlling factor of particulate metals in the stromwater runoffs. The particulate concentration of Pb accounted for 97.2% so the particulate phase was its main form. Other metals followed the sequence: Pb>Cu>Cd>Co>Zn>Ni. The particulate-dissolved partitioning coefficient ($K_d$) indicated that the $K_d$ of Pb were bigger than that of other metals because the metal Pb in the stormwater runoffs is quickly removed into the particulate phase. In a single day rainfall event, total runoff fluxes for total metals as the sum of dissolved and particulate forms through only two sewer outlets were 2.21 kg for Co, 30.5 kg for Ni, 278.3 kg for Cu, 398.3 kg for Zn, 0.39 kg for Cd and 40.0 kg for Pb, respectively. Given the annual rainfall, the number of rain days and the basin area for total pollutant load management system (TPLMS) of Shihwa area, enormous amount of non-point metal pollutants were entered into Lake with any treatment. The dissolved metals (e.g., Ni, Cu and Zn) in the stormwater runoffs exceeded the acute water quality criteria. Additionally, all metals were significantly enriched in the particulate phase and exceeded the PEL criteria of sediment quality guidelines (SQGs). These results indicated that the heavy metals in the stormwater runoffs may pose a very high ecological risk to the coastal environments and ecosystem.