• Korean Journal of Medicinal Crop Science
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• v.2 no.2
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• pp.105-109
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• 1994

This experiment was conducted to know the labor saving effect and reducing production cost by agricultural mechanization in the cultivation of Bupleurum falcatum. Labor reducing effects of the drilling seeder by hand and the machine attached to two wheel tiller were 97%, but emergency rate was highest in the former. Dry root yield per plant was increased by low amount of seed sowing but that yield per unit area was increased at much seeding amount in the seeder attached to the tiller. The drilling seeder by hand was showed highest standing ratio of seedling and produced yield to 84.1kg of root yield per 10a. Labor saving effect was the best at the multipurposes mechanized harvester and labor saving and famer's income ratio were increased to 69% and 50% respectively. Labor time and cost were reduced to 74% and 69% respectively by mechanization of sowing and harvest cultivation practice on Bupleurum falcatum.

• Korean Journal of Weed Science
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• v.32 no.3
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• pp.188-194
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• 2012

Field experiments were conducted to investigate the competition relationships of main paddy weeds with transplanted rice grown in paddy conditions. Data were used to predict crop yield as a function of weed density using a rectangular hyperbola model and determine weed economic threshold (ET) levels. The rectangular hyperbola (equation 2) was fitted to rice yield to estimate weed-free rice yield ($Y_o$) and weed competitivity (${\beta}$). Its competitivity for M. vaginalis was 0.0007445, 0.0005713, 0.000988 and 0.0008846 in Daejeon, Suwon, Iksan and Naju, respectively. The competitivity at harvest represented by parameter ${\beta}$ ranged from 0.001611 in Naju to 0.002437 in Iksan for S. trifolia. The ET levels of main paddy weeds in machine transplanted rice cultivation were well estimated based on the herbicides applied and its application cost. Therefore, our results can be used to support decision-making on herbicide application for weed management in transplanted rice cultivation.

• Journal of Biosystems Engineering
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• v.28 no.6
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• pp.517-524
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• 2003

There have been worldwide research and development efforts to automate various processes of bio-production and those efforts will be expanded with priority given to tasks which require high intensive labor or produce high value-added product and tasks under hostile environment. In the field of bio-production capabilities of the versatility and robustness of automated system have been major bottlenecks along with economical efficiency. This paper introduces a new concept of automation based on tole-operation, which can provide solutions to overcome inherent difficulties in automating bio-production processes. Operator(farmer), computer, and automatic machinery share their roles utilizing their maximum merits to accomplish given tasks successfully. Among processes of greenhouse watermelon cultivation tasks such as pruning, watering, pesticide application, and harvest with loading were chosen based on the required labor intensiveness and functional similarities to realize the proposed concept. The developed system was composed of 5 major hardware modules such as wireless remote monitoring and task control module, wireless remote image acquisition and data transmission module, gantry system equipped with 4 d.o.f. Cartesian type robotic manipulator, exchangeable modular type end-effectors, and guided watermelon loading and storage module. The system was operated through the graphic user interface using touch screen monitor and wireless data communication among operator, computer, and machine. The proposed system showed practical and feasible way of automation in the field of volatile bio-production process.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.6
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• pp.473-477
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• 2001

Chinese milkvetch (Astragalus sinicus L.) has been traditionally used as a green manure supplying mineral N and organic matters to soil. In rice-Chinese milkvetch interrelay cropping system, three rice cultivating methods (no-till direct seeding, no-till transplanting, and tillage transplanting) were evaluated for stand reestablishment without reseeding Chinese milkvetch with two cropping times (May 25 and June 4) for two years. Chinese milkvetch incorporated was decomposed rapidly in the first week. Decomposition was fast in topsoil than in subsoil. Natural milkvetch reestablishment (NMR), following harvest of no-till-direct-sown rice was good enough to cover the paddy field in both cropping times, but rice yield of this method was lower than that of transplanted rice on paddy field without milkvetch cultivation. Even though good NMR was secured in no-till rice transplanting, the shoot of milkvetch should be removed before machine-transplanting of rice seedlings. NMR was better in rice cropping at the mid-ripening stage of milkvetch (June 4) than at the late-bloom stage (May 25). Rice yield was higher in tillage transplanting at the mid-ripening stage of milkvetch (June 4) than in the other rice cropping method.

• Korean Journal of Organic Agriculture
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• v.31 no.4
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• pp.413-426
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• 2023

These experiments were conducted for 3 years from 2021 to 2023 to develop a method that can be safely used to prevent the leakage of Golden apple snails (Pomacea canaliculata) from eco-friendly rice plantations. In the southern part of Jeollanam-do, after planting rice, the young golden apple snails placed in the rice field become adults around mid-July and begin spawning. These individuals can overwinter in drains that do not dry out, but individuals hatched after mid-July will not mature enough to overwinter. The size of golden apple snails overwintered in the drainage canal was more than 2.5cm in shell height. Installing a net at the inlet could block 95% of the inflow of snails, and 99% of outflow was blocked by installing an improved water trap and net at the drain. During the mid-drying period and pre-harvest drying period, a water path was created with a power paddy pottery machine. 59.5% of snails were attracted to the waterway, and it took 130 minutes to build the waterway and collect the snails. Based on these results, seasonal paddy management tips for the safe use of giant snails in rice fields were suggested.

• Korean Journal of Medicinal Crop Science
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• v.4 no.2
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• pp.157-162
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• 1996

Experiments were conducted from 1994 to 1995 to understand the effects of the labor-saving seeding and planting density on growth and root yield of Astragalus membranaceus. The drilling seeder reduced seeding time than the hand seeding; It takes 3. 5hrs/l0a to seed by drilling seeder while 33. 0hrs/l0a by hand seeding, which labor reducing rate was 89. 4 %. Emergence rate in the drilling seeder was increased 17% than in the hand seeding, so the root yield were increased 23% to 136. lkg/l0a in using drilling seeder compared to 110. 3kg in hand seeding. On the effect of planting density on the growth characteristics plant height was long in dense planting and stem diameter was thick in spacious planting. Root diameter and dry root weight root per plant were decreased in dense planting and root yield was highest in optimum planting densities $(6\;row\;(15cm)\;{\times}\;10cm)$ in the harvest of 1 year old plants in Astragalus membranaceus. The gross profit were increased 23% to 1,933 thousand won per l0a in the drilling seeder compared to 1,566 thousand won in the hand seeding, also the managing costs were reduced 18% to 406 thousand won per l0a in the drilling seeder than 494 thousand won per l0a in the hand seeding.

• Journal of Food Hygiene and Safety
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• v.29 no.3
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• pp.181-188
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• 2014

The objective of this study was to analyze the microbiological hazards of Astragalus membranaceus Bunge on the post-harvest processing. Samples from processing equipments (cleaner, water, cart, table, tray and packaging machine), personal hygiene (hand) and harvested crops (before washing, after washing, after sorting, and after drying) were collected from four farms (A, B, C, and D) located in Chungchengbuk-do, Korea. The samples were analyzed for sanitary indication bacteria and pathogenic bacteria. First, total aerobic bacteria and coliform in processing facilities were detected at the levels of 0.93~4.86 and 0.33~2.28 log CFU/$100cm^2$ and/mL respectively. In particular, microbial contamination in hand (5.43~6.11 and 2.52~4.12 log CFU/Hand) showed higher than processing equipments. Among the pathogenic bacteria, Bacillus cereus was detected at the levels of 0.33~2.41 log CFU/$100cm^2$, 1.48~3.27 log CFU/Hand and 0.67~3.65 log CFU/g in equipments, hands, and plants and Staphylococcus aureus were detected in cleaner, table, hand and harvested crops (before washing and after sorting) by qualitative test. Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella spp. were not detected. These results indicated that personal hygiene and processing equipments should be managed to reduce the microbial contamination of A. membranaceus Bunge. Therefore, management system such as good agricultural practices (GAP) criteria is needed for hygienic agricultural products.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.105-111
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• 2021

Rice lodging is an annual occurrence caused by typhoons accompanied by strong winds and strong rainfall, resulting in damage relating to pre-harvest sprouting during the ripening period. Thus, rapid estimations of the area of lodged rice are necessary to enable timely responses to damage. To this end, we obtained images related to rice lodging using a drone in Gimje, Buan, and Gunsan, which were converted to 128 × 128 pixels images. A convolutional neural network (CNN) model, a deep learning model based on these images, was used to predict rice lodging, which was classified into two types (lodging and non-lodging), and the images were divided in a 8:2 ratio into a training set and a validation set. The CNN model was layered and trained using three optimizers (Adam, Rmsprop, and SGD). The area of rice lodging was evaluated for the three fields using the obtained data, with the exception of the training set and validation set. The images were combined to give composites images of the entire fields using Metashape, and these images were divided into 128 × 128 pixels. Lodging in the divided images was predicted using the trained CNN model, and the extent of lodging was calculated by multiplying the ratio of the total number of field images by the number of lodging images by the area of the entire field. The results for the training and validation sets showed that accuracy increased with a progression in learning and eventually reached a level greater than 0.919. The results obtained for each of the three fields showed high accuracy with respect to all optimizers, among which, Adam showed the highest accuracy (normalized root mean square error: 2.73%). On the basis of the findings of this study, it is anticipated that the area of lodged rice can be rapidly predicted using deep learning.