• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.802-811
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• 1996

Farmers need alternatives for weed control due to the desire to reduce chemicals used in farming. However, conventional mechanical cultivation cannot selectively remove weeds located in the seedline between crop plants and there are no selective heribicides for some crop/weed situations. Since hand labor is costly , an automated weed control system could be feasible. A robotic weed control system can also reduce or eliminate the need for chemicals. Currently no such system exists for removing weeds located in the seedline between crop plants. The goal of this project is to build a real-time , machine vision weed control system that can detect crop and weed locations. remove weeds and thin crop plants. In order to accomplish this objective , a real-time robotic system was developed to identify and locate outdoor plants using machine vision technology, pattern recognition techniques, knowledge-based decision theory, and robotics. The prototype weed control system is composed f a real-time computer vision system, a uniform illumination device, and a precision chemical application system. The prototype system is mounted on the UC Davis Robotic Cultivator , which finds the center of the seedline of crop plants. Field tests showed that the robotic spraying system correctly targeted simulated weeds (metal coins of 2.54 cm diameter) with an average error of 0.78 cm and the standard deviation of 0.62cm.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.751-762
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• 2017

A crop information system can provide information regarding crop distribution, crop growth conditions, crop yield in various forms such as monitoring, forecasting, estimation or analysis. This paper presents the design and construction of a crop information system based on data collected in Korea, USA, and China. Therein, climate data including temperature, precipitation,solar radiation are used to evaluate the impact on crop growth, NDVI (Normalized Difference Vegetation Index) data is used in crop monitoring, and crop map data is utilized for the management of crop distribution. The system has achieved three prominent results: 1) Providing information with high frequency, 2) Automatically creating the report through the analysis of the data, 3) The users to easily approach the system and retrieve the information.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.87-87
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• 2022

• Proceedings of the Korean Society of Crop Science Conference
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• 2021.04a
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• pp.105-105
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• 2021

• Proceedings of the Korean Society of Crop Science Conference
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• 2021.10a
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• pp.153-153
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• 2021

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.1
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• pp.1-6
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• 2004

N fertilizer required by rice could be reduced greatly in the rice-barley double cropping system than in the rice single cropping system. This study was conducted to investigate how much of the N fertilizer during the early stage of rice in the rice-barley double cropping system, could be saved compared to that in the rice single cropping system. This experiment was carried out at the paddy field of the National Crop Experiment Station in Suwon, Korea during three years from 1999 to 2001. Amounts of soil mineral nitrogen (SMN) and SPAD values of rice leaf during rice growing season in the rice-barley double cropping system were higher than those in the rice single cropping system under the same amount of N application during two years. Yield and N uptakes of rice at harvesting time were also higher in the rice-barley double cropping system than in the rice single cropping system during two years. Yield and N uptake of rice in the rice single cropping system were decreased when basal N fertilizer was omitted, but those reductions were not found by either omitting basal N fertilizer or omitting N fertilizer at tillering stage in the rice-barley double cropping system during 2000 and 2001. But yield and N uptakes of rice were decreased by 70 kg/10a and 2kgN/10a by the omission of both N application at basal and tillering stages in the rice-barley double cropping system in 2002. It was concluded that N fertilizer as much as tillering N fertilizer could be saved in the rice-barley double cropping system.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.315-326
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• 2020

In this study, a real-time crop recognition system was developed for an unmanned farm machine for upland farming. The crop recognition system was developed based on a stereo camera, and an image processing framework was proposed that consists of disparity matching, localization of crop area, and estimation of crop height with coordinate transformations. The performance was evaluated by attaching the crop recognition system to a tractor for five representative crops (cabbage, potato, sesame, radish, and soybean). The test condition was set at 3 levels of distances to the crop (100, 150, and 200 cm) and 5 levels of camera height (42, 44, 46, 48, and 50 cm). The mean relative error (MRE) was used to compare the height between the measured and estimated results. As a result, the MRE of Chinese cabbage was the lowest at 1.70%, and the MRE of soybean was the highest at 4.97%. It is considered that the MRE of the crop which has more similar distribution lower. the results showed that all crop height was estimated with less than 5% MRE. The developed crop recognition system can be applied to various agricultural machinery which enhances the accuracy of crop detection and its performance in various illumination conditions.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.82-82
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• 2022

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.1
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• pp.1-4
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• 2005

This experiment was conducted to investigate the soil nitrogen credit of biological nitrogen fixation (BNF) and the nitrogen balance of soybean in soybean-barley cropping systems. Soybean cultivar, Shinpaldalkong2 and barley cultivar, Olbori, were used in soybean mono-cropping (SM), barley monocropping (BM), and barley­soybean double cropping system. The barley-soybean double cropping system was treated with two different levels of nitrogen fertilizers, 0 nitrogen fertilizer (BS-F0), and standard nitrogen fertilizer (BS-F1). Nitrogen and organic matter concentrations in soil of BS-F1 plot on October, 2001 were increased $4.8\%\;and\;5.9\%$, respectively, compared with those on October, 2000. The ranges of BNF rate in soybean were $69.1\~ 88.2\%$ in two years, and the rate was the highest in BS-F0 plot and the lowest in SM plot. The ranges of nitrogen harvest index (NHI) in all treatments were $83.9\~86.7\%$. The yield was 270 kg/10a in BS­F1 plot and 215 kg/10a in BS-F0 plot. However, the nitrogen balances were +0.6 kg/10a of gain of soil nitrogen in BS-F0 plot and -0.4 kg/10a of loss of soil nitrogen in BS-F1 plot. In comparisons of SM and BS-F1 plots, although the seed yields were similar in two plots, the loss of soil nitrogen was higher in SM than BS-F1 plot. Overall, our results suggest that barley-soybean double cropping system was more effective in respect to seed productivity and soil nitrogen conservation than soybean monocropping system, and the N credit to following crops by soybean cultivation was identified in soybean double cropping system.

• Korean Journal of Organic Agriculture
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• v.20 no.2
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• pp.143-159
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• 2012

Agricultural income is calculated with producer price, output and management cost. This study compared organic farming with conventional one for agricultural income, producer price and output by items. And then it proposed the method of item selection and crop system from a diversification point of view. The coefficient of variation to producer prices in organic farming was 4.7%, and conventional one was 30.3% because organic products have been produced in a system of contract farming with consumers' cooperative. This result means the price of organic products is stabler than that of conventional price. And agricultural income of organic farming has been generally known more than that of conventional one. However, agricultural gross income of conventional farming was more than that of organic one by 20.3% in 2010. It was caused by output reduction of a few items(fer example; onion, large green onion, potato and young pumpkin) due to freak weather conditions and constant producer price for several years in organic farming. In order to increase agricultural income, appropriate crop selection and system should be introduced to organic farming. A principal crop is the rice plant and 2 subordinate crops are dry crops at bare field and greenhouse respectively. Thus 5 crop systems that agricultural gross income are relatively increased larger among 15 crop systems estimated are rice+ginger+cucumber, rice+ginger+tomato, rice+large green onion+cucumber, rice+sweet potato+cucumber and rice+onion+ cucumber.