• Proceedings of the Korean Society of Crop Science Conference
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• 2021.10a
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• pp.181-181
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• 2021

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.3
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• pp.102-106
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• 2010

Recently process industries from oil and gas procedures and mining companies to manufactures of chemicals, foods, and beverages has been exploring the USN (Ubiquitous Sensor Networks) technology to improve safety of production processes. However, to apply the USN technology in the large-scale plant industry, reliability and security issues are not fully addressed yet, and the absence of the industrial sensor networking standard causes a compatibility problem with legacy equipment and systems. Although this situation, process industry such as energy plants are looking for the secure wireless plant solution to provide detailed, accurate safety monitoring from previously hard-reach, unaccordable area. In this paper, SPSF (Smart Plant Safety Framework based on Reliable-Secure USN) is suggested to fulfill the requirements of high-risk industrial environments for highly secure, reliable data collection and plant monitoring that is resistant to interference. The SPSF consists of three main layers: 1) Smart Safety Sensing Layer, 2) Smart Safety Network Layers, 3) Plant Network System Layer.

• 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.

• Journal of the Korean Society of Systems Engineering
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• v.14 no.1
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• pp.28-35
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• 2018

Front End Engineering Design (FEED), currently dominated by a few advanced countries, creates the highest added-value in the in plant construction industry. In the domestic plant engineering industry, it is difficult to acquire its own technology capability and experience due to lack of experience and shortage of experts in advanced design fields such as basic design and FEED. To achieve competitiveness with the advanced countries, it is necessary to establish smart training system for advanced plant design and FEED engineers. This study aims to design an integrated training framework for plant engineering and FEED using system engineering to build a smart plant engineering education system that learns design knowledge based on educational content and experience based on design stage for chemical plant.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.155-155
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• 2023

• Proceedings of the Korean Society of Crop Science Conference
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• 2021.04a
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• pp.113-113
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• 2021

• Proceedings of the Korean Society of Crop Science Conference
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• 2021.04a
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• pp.115-115
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• 2021

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.262-262
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• 2022

This research was supported by a Grant (22193MFD471) from Ministry of Food and Drug Safety in 2022.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.215-220
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• 2018

This work presents a smart water quality sensor for hydroponic plant growing applications. The proposed sensor can effectively measure pH level and electrical conductivity of the water solution. The micro-controller incorporated in the sensor processes the raw sensor data, and converts it into a readable format. In addition, through the mobile interface realized using a WiFi module, the sensor can send data to the web server database that collects and stores the data. The data stored in the web server can be accessed by a personal computer or smart phone. The prototype sensor has been implemented, and the operations have been verified under an actual hydroponic plant growing application.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.04a
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• pp.19-19
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• 2022