• Environmental and Resource Economics Review
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• v.9 no.5
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• pp.853-876
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• 2000

The purpose of this paper is to come up with the measures for sustainable development of the agricultural sector in store for the strengthened U.N. Framework Convention on Climate Change. We analyze the spillover effects of Methane and Nitrous Oxides taxes (carbon tax) on the Korean agricultural sector. Unlike the other sectors, the agricultural sector has a unique characteristic generating greenhouse gas in the process of production itself even without consuming much fossil fuel. In order to estimate the impacts of those taxes, non-linear optimization method has been used with various assumed scenarios. The production effect, income and' price effect, and greenhouse gas emission reduction effect in the agricultural sector have been estimated through this method. The empirical results show that the paddy sector has a bigger tax effect than the livestock sector. In the paddy sector, the carbon tax has more impacts in the suburban areas than in the rural areas, while the swine farming section in the livestock sector has a conspicuous income effect in the midst of low greenhouse gas emission effect. These results allude us to apply graded tax rates to the crop, the livestock, and the region of different kind. Even if the agricultural sector has a less tax effect when compared with other industrial sectors, an environmental tax might be an effective measure to prevent global warming.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.6
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• pp.743-750
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• 2016

Recently, usage of UAV (Unmanned Aerial Vehicle) has increased in agricultural part. This study was conducted to classify onion and garlic using supervised classification of a fixed-wing UAV (Model : Ebee) images for evaluation of possibility about estimation of onion and garlic cultivation area using UAV images. Aerial images were obtained 11~12 times from study sites in Changryeng-gun and Hapcheon-gun during farming season from 2015 to 2016. The result for accuracy in onion and garlic image classification by R-G-B and R-G-NIR images showed highest Kappa coefficients for the maximum likelihood method. The result for accuracy in onion and garlic classification showed high Kappa coefficients of 0.75~0.97 from DOY 105 to DOY 141, implying that UAV images could be used to estimate onion and garlic cultivation area.

• The Journal of Fisheries Business Administration
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• v.13 no.1
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• pp.1-46
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• 1982

Setting a Problem : Developing industrialization and urbanization has been accompanying the outflow of farming-and fisheries village's population, and brings upon lowering of number of fishermen and their productive efficiency as a result. The point of issue of such an outflow of fishermen which is a cause of the shortage of fisheries man-power should be considered through the analysis of the present state of fishery, and then 'How should the normative fisheries man-power policy be performed?' has to be thought after the investigation upon self-existable fishermen's successor-cultivation(SFSC) project which has been driven forward by government. Method of Study : In order to seek concretely a rational way to achieve SFSC undertaking successfully, the teleological method is adopted fundamentally. But the analysis on the fisheries present state centering on fisheries population and the fishery-orientedness of fishermen who are selected randomly in five islands of Jeonranamdo-Province and Kyeongsangnamdo-Province has been tried with the object of groping for a successful and purposive way of successor-cultivation. And the controversial point is brought out through the analysis of the first year's result which SFSC project has been undertaken in 1981. The above-mentioned affair is especially studied on the basic attitude which 'fishery' is a 'profession'.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.39-45
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• 2011

There have been many studies on red tide because of increasing of damage to sea farming by a red tide blooms of harmful algae. The studies of red tide have mostly focused chemical properties and investigation of biological cause. If we can predict the occurrence of red tide, we will be able to minimize the damage of red tide. However, internal study of prediction of red tide blooms is only classification method that is still insufficient for red tide blooms forecast. In this paper, we proposed the red tide blooms prediction method using neural network and SVM.

• Journal of Korean Society of Rural Planning
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• v.20 no.4
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• pp.45-54
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• 2014

The main purpose of this study is to provide the back-from-city farmers with the information about the melon cultivation technology by surveying 268 farm houses in the major melon producing districts such as Seongju and Chilgok. For the purpose, this study classifies the essential technologies that the melon experts think as most important into 6 categories: size of plastic film house, covering film, varieties of oriental melon, lagging cover, ventilation method and ways to reduce repeated-cultivation damage. The result of the study shows that the back-from-city farmers should consider the following items when they choose to cultivate oriental melons. For the size of plastic film house, the ventilation method and the covering film of plastic film house, it is better to choose the latest technology. Even though it may require larger initial investment, the latest technology can increase the production and lower the cost. In case of variety, it is better to choose popular or the most widely grown ones rather than the new ones. The lagging cover should be selected in consideration of climate conditions such as average temperature and humidity, transplant time and harvest time of the farming region.

• Smart Media Journal
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• v.11 no.7
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• pp.94-103
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• 2022

Weeds bring disadvantages to crops since they can damage them, and a clean treatment with less pollution and contamination should be developed. Artificial intelligence gives new hope to agriculture to achieve smart farming. This study delivers an automated weeds growth point detection using deep learning. This study proposes a combination of semantic graphics for generating data annotation and U-Net with pre-trained deep learning as a backbone for locating the growth point of the weeds on the given field scene. The dataset was collected from an actual field. We measured the intersection over union, f1-score, precision, and recall to evaluate our method. Moreover, Mobilenet V2 was chosen as the backbone and compared with Resnet 34. The results showed that the proposed method was accurate enough to detect the growth point and handle the brightness variation. The best performance was achieved by Mobilenet V2 as a backbone with IoU 96.81%, precision 97.77%, recall 98.97%, and f1-score 97.30%.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• The Korean Journal of Food & Health Convergence
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• v.10 no.3
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• pp.19-22
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• 2024

The field of synthetic biology has emerged in response to the ongoing progress in the life sciences. Advances have been made in medicine, farming, eating, making materials, and more. Synthetic biology is the exploration of using living organisms to create new organisms. By manipulating specific genes to express targeted proteins, proteins can be created that are both productive and cost-effective. Solid-phase extraction (SPE) and liquid-liquid extraction (LLE) are employed for protein separation during the production process involving microorganisms. This study centers on Scanning Probe Microscopy (SPM) to showcase its utility in the food industry and food management. SPE is predominantly utilized as a pretreatment method to eliminate impurities from samples. In comparison to LLE, this method presents benefits such as decreased time and labor requirements, streamlined solvent extraction, automation capabilities, and compatibility with various other analytical instruments. Anion exchange chromatography (AEC) utilizes a similar methodology. Pharmaceutical companies utilize these technologies to improve the purity of biopharmaceuticals, thereby guaranteeing their quality. Used in the food and beverage industry to test chemical properties of raw materials and finished products. This exemplifies the potential of these technologies to enhance industrial development and broaden the scope of applications in synthetic biology.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.5
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• pp.1-10
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• 2019

Due to the climate change the drought had been occurring more frequently in recent two decades as compared to the previous years. The change in the pattern and frequency of the rainfall have a direct effect on the farming sector; therefore, the quantitative estimation of water supply is necessary for efficient agricultural water reservoir management. In past researches, there had been several studies conducted in estimation and evaluation of water supply based on the irrigational water requirement. However, some researches had shown significant differences between the theoretical and observed data based on this requirement. Thus, this study aims to propose an approach in estimating reservoir rate based on empirical method that utilized observed reservoir rate data. The result of these two methods in comparison with the previous one is seen to be more fitted for both R2 and RMSE with the observed reservoir rate. Among these procedures, the method that considers the drought year data shows more fitted outcomes. In addition, this new method was verified using 15-year (2002 to 2006) linear regression equation and then compare the preceeding 3-year (1999 to 2001) data to the theoretical method. The result using linear regression equation is also perceived to be more closely fitted to the observed reservoir rate data than the one based on theoretical irrigation water requirement. The new method developed in this research can therefore be used to provide more suitable supply data, and can contribute to effectively managing the reservoir operation in the country.

• Korean Journal of Organic Agriculture
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• v.21 no.4
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• pp.601-615
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• 2013

This study was conducted to compare the community structure and biodiversity of arthropods between conventional and organic red pepper fields. A total of 36 species of 24 families belonging to 10 orders from collected 28,718 arthropods; 6,901 individuals from conventional field and 21,871 individuals from organic field. A number of species comprising arthropod community was same in both fields as 32 species. Species richness of Diptera was the highest in conventional field and that of Hymenoptera and Colembolla was the highest in organic field. Abundance of Frankliniella intonsa was the highest regardless of farming method. Helicoverpa assulta was dominant in conventional field, and Homidia mediaseta, Diptera sp. 4 and Pardosa astrigera were dominant in organic field. Diversity of community on the ground was higher in organic field and statistically different. Similarity of arthropod community showed difference with 34.07% on the ground and 26.95% in the plant above ground. In the ecologically functional guild: species richness of general, pest and parasitoid of natural enemy groups in the plant above ground were statistically different and pest and parasitoid groups were higher in organic field, abundance of predator group of natural enemy on the ground was 2 times higher in organic field and statistically different and diversity of general and parasitoid groups in the plant above ground were statistically different. In the relative occupancy rate, pest group was the highest in conventional field and decomposer group was the highest in organic field. The results of present study is considered to provide useful information of arthropod community for developing efficient insect pest management in organic farming.