• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.58-58
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• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.

• Agricultural and Biosystems Engineering
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• v.1 no.1
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• pp.59-62
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• 2000

After ten years of implementing the agricultural automation program in Taiwan, some positive effects and satisfactory results have been recognized by both the agricultural industry and local administrative bureaux. The automation of agriculture is a response to sophisticated demands for production and quality in countries with high labor costs. The development of sensor systems, control systems, precision agriculture systems, and engineering for plant culture systems will determine the degree of automation used for crop production in the 21st century. The engineering system will capitalize upon expertise from physiologists, pathologists, systems analysts, agronomists, horticulturists, computer programmers, economists, crop producers and managers in order to efficiently implement automated crop production.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.s01
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• pp.33-39
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• 1989

Ethylene gas classified as one of five major plant hormones plays an important role in various plant metabolism. The precise analysis of ethylene production of plants or plant parts is a valuable research procedure because knowledge of ethylene production facilitates measures of the physiological activity within the tissue. This paper describes procedures for analyzing ethylene from plant tissues by gas chromatography and discusses problems associated with extracting gas samples either by introducing a vacuum to plant samples or by using a hypodermic syringe. Introduced are a continuous flow system for efficient analysis and an automated system for sampling, analyzing, calculating and recording ethylene production data.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.764-769
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• 2000

A new transplant production system that produces high quality plug seedlings of specific crop has been studied. It is a plant factory designed to produce massive amount of virus free seedlings. The design concept for building this plant factory is to realize maximum energy efficiency and minimum initial investment and running cost. The basic production strategy is the sitespecific management. In this case, the management of the growth of individual plantlet is considered. This requires highly automated and information intensive production system in a closed aseptic environment the sterilized specific crops. One of the key components of this sophisticated system is the irrigation system. The conditions that this irrigation system has to satisfy are: 1. to perform the site specific crop management in irrigation and 2. to meet the no waste standard. The objective of this study is to develop an irrigation scheduling that can implement the no waste standard.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.69-76
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• 2024

This dissertation developed a five-story vertical livestock and fisheries farm (palm tower) model for sustainable food production in cities. It proposes to integrate marine farms, livestock raising, and pesticide-free automated crop farms to efficiently use resources and minimize environmental impact. Based on circular economy principles, the model can recycle the output of each part into resources from the other, increasing the efficiency of the system, utilizing idle space in the city, and promoting job creation and community participation. It can also contribute to reducing the carbon footprint of food production and improving food safety. In addition, the study explores how advanced agricultural technologies can be integrated into urban structures to address global food security challenges. This model presents potential solutions to the food crisis caused by climate change and population growth, and suggests a direction for the development of urban agriculture. Future research should address the technical and policy challenges for practical implementation.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.593-599
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• 2000

Accurate quantification of plant physiological properties is often necessary for optimal control of an automated greenhouse production system. Conventional crop growth monitoring systems are usually burdensome, inaccurate, and harmful to crops. A thermal image analysis system was used to accomplish rapid and accurate measurements of physiological-property changes of water-stressed crops. Thermal images were obtained from several species of plants that were placed in a growth chamber. Analyzing the images provided the pattern of temperature changes in a leaf and the amount of differences in the temperature of stressed plants and non-stressed plants.

• Journal of Biosystems Engineering
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• v.24 no.5
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• pp.439-444
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• 1999

Automated greenhouse production systems often require crop growth monitoring involving accurate quantification of plant physiological properties. Conventional methods are usually burdensome, inaccurate, and harmful to crops. A thermal image analysis system can accomplish rapid and accurate measurements of physiological-property changes of stressed crops. In this research a thermal imaging system was used to measure the leaf-temperature changes of several crops according to water deficit. Thermal images were obtained from lettuce, cucumber, pepper, and chinese cabbage plants. Results showed that there were significant differences in the temperature of stressed plants and non-stressed plants. The temperature differences between these two group of plants were 0.7 to 3$^{\circ}C$ according to species.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.18-18
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• 2022

Barley has been used for the production of malt in the brewing industry. Malting is the process of preparing barley through partial germination. Malt extract is the most important quality parameter for malt quality. The grain and malt quality parameters of ten Korean malting barley varieties were studied. Malts was prepared using Phoeix automated micro malting system(Phoenix Bio, Australia). Quality analysis of Barley and malt was determined according to European brewery convention(EBC, 1998) and American society of brewing chemists(ASBC, 1997) method. And the hordeins of barley and malt were extracted with 50% isopropyl alcohol(IPA, 2-propanol) of 1% dithiothreitol(DTT). The analysis of hordeins was carried out by ultra-performance liquid chromatography(UPLC). The mean values of 1000-grains weight, assortment rate, protein content, starch content, beta-glucan content, husk rate, germination energy, germination capacity and water sensitivity of grain were 45.8g, 86.8%, 11.9%, 58.0%, 3.8%, 14.0%, 96.2%, 97.2%, 10.0%, respectively. The mean values of protein content, friability, diastatic power, extract, soluble protein, Kolbach index, beta-glucan of malt and wort were 11.3%, 87.6%, 201WK(Windish Kolbach), 79.3%, 4.6%, 41%, 85mg/L, respectively. UPLC analysis of grain and malt hordeins revealed that the amount of hordeins significantly degraded during malting. Also, we could successfully be used to compare hordein polypeptide patterns with malt quality.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.79-79
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• 2023

With the development of digital technology, the size of the smart agriculture market at home and abroad is rapidly expanding. It is necessary to establish a foundation for sustainable precision agriculture in order to respond to the aging of rural areas and labor shortages. This study was conducted to establish an automated digital agricultural test bed for soybean production management using data suitable for agricultural environmental conditions in Korea and to demonstrate the field of leading complexes. In order to manage water smartly, we installed a subsurface drip irrigation system in the upland field and an underground water level control system in the paddy field. Based on data collected from sensors, water management was controlled by utilizing an integrated control system. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. The main growth characteristics and yield, such as stem length, number of branches, and number of nodes of the main stem, were investigated during the main growth period. During the operation of the test bed, drought appeared during the early vegetative growth period and maturity period, but in the open field smart agriculture test bed, water was automatically supplied, reducing labor by 53% and increasing yield by 2%. A test bed was installed for each field digital farming element technology, and it is planned to verify it once more this year. In the future, we plan to expand the field digital farming technology developed for leading farmers to the field.