• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.3
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• pp.237-244
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• 2022

Onions are a significant crop in Korea, and cultivation is increasing every year along with high demand. Onions are planted in the fall and mainly harvested in June, the rainy season, therefore, physiological changes in onion bulbs during long-term storage might have happened. Onions are stored in cold room and at adequate relative humidity to avoid quality loss. In this study, bio-yield stress and weight loss were measured as the quality parameters of net packaged onions during 10 weeks of storage, and the storage environmental conditions are monitored using sensor networks systems. Quality estimation of net packaged onion during storage was performed using the storage environmental condition data through machine learning approaches. Among the suggested estimation models, support vector regression method showed the best accuracy for the quality estimation of net packaged onions.

• Journal of Mushroom
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• v.4 no.3
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• pp.101-105
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• 2006

This was performed to improve the working conditions of oyster mushroom cultivation on bed using rice straw of which binding and cutting procedure need a lot of time. There was a little difference in physiochemical components between rice straw and cotton waste C/N ration of cotten waste was, however, similar to rice straw as 84-85. If is feasible to use as substrate material. The average yield of a net was 1450g in six frustration harvest, when the size of an onion net was $35{\times}30cm$, mixture ratio of rice straw to cotton waste was 40:60(V/V). Cultivation method using an onion net saved 3 days for preparation, 4 hours for inoculation compared to normal bed cultivation. This method was analysed to give 5% economical benefit.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1061-1070
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• 2021

In this manuscript, we tried to improve the performance of the FC-DenseNet by applying an attention gate for the classification of cropping areas. The attention gate module could facilitate the learning of a deep learning model and improve the performance of the model by injecting of spatial/spectral weights to each feature map. Crop classification was performed in the onion and garlic regions using a proposed deep learning model in which an attention gate was added to the skip connection part of FC-DenseNet. Training data was produced using various PlanetScope satellite imagery, and preprocessing was applied to minimize the problem of imbalanced training dataset. As a result of the crop classification, it was verified that the proposed deep learning model can more effectively classify the onion and garlic regions than existing FC-DenseNet algorithm.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.26 no.2
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• pp.61-69
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• 2020

As post-harvest processes of onions are carried by a 20 kg-net package which results in high-cost and low-efficiency, especially, the insufficient drying and physical damage of onions after harvesting leads to a huge second loss in storage, we had developed a low-cost, high-efficiency post-harvest bulk handling machinery system by collecting onions on a farm using ton-bags, drying with forced air circulation, and sorting/packaging. The post-harvest bulk handling machinery system consisted of 6 devices, and this study designed an automatic feed hopper with a feeding rate control device, an inclined belt conveyor with a two-step chute, and an automatic pallet unloading device for feeding onions into the sorting/packing line. This study also analyzed the performance and control of the total system. The device had 1-ton handling capacity, but the operational condition was set to increase the capacity. The three-step filling method of pallet by the velocity control of the inclined belt conveyor was applied in the post-harvest bulk handling machinery system for the prevention of physical damage. If one worker was set to operate the total system, the time required to complete one palletized load was approximately 5 minutes and 5 seconds. The calculated daily handling capacity was approximately 94 tons, when the daily actual working time was 8 hours. When the developed system was applied to the managerial size of 2,000 ton, the processing cost per ton of the system was decreased by 19.5%, compared with the existing 20 kg-net package-based handling. The developed post-harvest bulk handling machinery system would be a good substitute for the rapid decline and aging of rural labor.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.233-238
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• 2020

Process-based model (PBM), based on the interactions between endogenous physiological processes and many environmental factors, can be a powerful tool for estimating crop growth and productivity. Carbon acquisition and biomass accumulation are the main components in PBM, so it has become important to understand and integrate gas exchange process in crop model. This study aimed to assess the applicability of FvCB model (a leaf model of C3 photosynthesis proposed by Farquhar, von C aemmerer, and Berry (1980)) in onion (Allium cepa L.). For parameterization, two early-maturing onion cultivars, 'Singsingball' and 'Thunderball', grown in a temperature gradient plastic film house, were used in measuring leaf net CO2 assimilation rate (A), and then, parameter estimation was carried out for four parameters including Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), TPU (rate of triose phosphate utilization), and Rd (Dark respiration rate). The gas-exchange model calibrated in this research is expected to be able to explain the photosynthetic responses of onion under various environmental conditions (R2=0.95***).

• Korean Journal of Medicinal Crop Science
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• v.26 no.4
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• pp.296-301
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• 2018

Background: The objective of this study was to investigate the effect of packaging material on the growth of rootstock of Liriope platyphylla. Methods and Results: This study examined the effects of two types of packaging material, LDPE (low density polyethylene) and functional film on the growth of the tubers of L. platyphylla, at $5^{\circ}C$. During the 16-weeks of storage period, the ratio of loss and decay of the tubers was examined at intervals of 4, 8, and 16 weeks to detect the quality of the plant. After 16 weeks of storage, the treated tubers were own. Subsequently, plant height and the number of leaves were recorded. The results revealed that functional film at $5^{\circ}C$ was the ideal material for the storage of L. platyphylla tubers. The rate of loss was the highest (57.42%) with a onion net and the lowest (22.12%) with a functional film. Similarly, the rate of tuber decay was highest (8.20%) using onion net and the least (4.60%) when the functional film was used. Conclusions: Thus, the use of the functional film proved to be the most effective in the storage of L. platyphylla tubers when compared with the LDPE.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.823-833
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• 2020

In order to stably produce crops, there is an increasing demand for effective crop monitoring techniques in domestic agricultural areas. In this manuscript, a cultivation area extraction method by using deep learning model is developed, and then, applied to satellite imagery. Training dataset for crop cultivation areas were generated using RapidEye satellite images that include blue, green, red, red-edge, and NIR bands useful for vegetation and environmental analysis, and using this, we tried to estimate the crop cultivation area of onion and garlic by deep learning model. In order to training the model, atmospheric-corrected RapidEye satellite images were used, and then, a deep learning model using FC-DenseNet, which is one of the representative deep learning models for semantic segmentation, was created. The final crop cultivation area was determined as object-based data through combination with cadastral maps. As a result of the experiment, it was confirmed that the FC-DenseNet model learned using atmospheric-corrected training data can effectively detect crop cultivation areas.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1007-1014
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• 2022

Compact Advanced Satellite 500 (CAS500) can be used for various purposes, including vegetation, forestry, and agriculture fields. It is expected that it will be possible to acquire satellite images of various areas quickly. In order to use satellite images acquired through CAS500 in the agricultural field, it is necessary to develop a satellite image-based extraction technique for crop-cultivated areas.In particular, as research in the field of deep learning has become active in recent years, research on developing a deep learning model for extracting crop cultivation areas and generating training data is necessary. This manuscript classified the onion and garlic cultivation areas in Hapcheon-gun using PlanetScope satellite images and farm maps. In particular, for effective model learning, the model performance was analyzed according to the proportion of crop-cultivated areas. For the deep learning model used in the experiment, Fully Convolutional Densely Connected Convolutional Network (FC-DenseNet) was reconstructed to fit the purpose of crop cultivation area classification and utilized. As a result of the experiment, the ratio of crop cultivation areas in the training data affected the performance of the deep learning model.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.379-385
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• 2018

Purpose: Postharvest handling of onions (harvesting, cleaning, grading, cooling, storing, and transport) should be performed continually to reduce costs and improve quality. The purpose of this study is to a) determine the design parameters and operating conditions of anion auto-dumping that constitutes a key component of the postharvest bulk handling machinery system, and b) to perform a performance test with the auto-dump prototype system. Methods: Kinematic analyses and computer simulations of the auto-dump mechanism were applied to analyze the operating conditions and design parameters. Results: The optimum working condition for the auto-dump was determined from kinetic analyses. In addition, the interaction between the velocity of the hydraulic cylinder and the angular velocity of the auto-dump were analyzed in order to control the bulk handling machinery system. The acting forces and optimum operating conditions of the hydraulic cylinder were determined by analyzing the forces related to the mass of inertia of the auto-dump assembly during rotation. The method of controlling the feeding rate of onions in terms of the uniformity of the stacking pattern and the control of the entire system was better than the two-stage method of controlling the rotational speed of the auto-dump. Based on the performance test with the prototype for the auto-dump, the stacking pattern and rigidity of the system were analyzed. Conclusions: These results would be of great importance in the postharvest bulk handling machinery system for onions.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.199-210
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• 2022

This study aimed to develop a cropping system to use limited crop-land with optimum efficiency, while considering management from farmers. To establish the cropping system involving a two-year rotation of three crops, three types of cropping system were evaluated in Suwon (Seogcheon series) and Anseong (Geumcheon series) in the middle plain area using six crops from 2018 to 2019: maize-perilla-onion, potato-sesame-garlic, and maize-sesame-onion. The crop productivity and income of the cropping systems involving food-, oilseed-, and horticultural crops were analyzed, and the optimal cropping system was reviewed. The total yield of each crop was as follows: maize 1,281 kg, potato 4,837 kg, perilla 125 kg, sesame 120 kg, onion 6,503 kg, and garlic 1,027 kg per 10a. However, in terms of gross profit, the potato was more than 3.8 times more profitable than corn, sesame was 1.8 times more profitable than perilla, and garlic was more than 2.8 times more profitable than onions. As a result, in terms of net income, the potato-sesame-garlic cropping system produced the highest income per unit area. Sesame seedlings were planted after the potato harvest, thereby solving the problem of competition between the first and last crops. Overall, this study confirmed that the potato-sesame-garlic cropping system, a two-year rotation of three crops, contributed to the improvement of upland crop productivity and farmers' income and was an overall effective cropping system.