• Agribusiness and Information Management
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• v.8 no.2
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• pp.22-27
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• 2016

• Agricultural and Biosystems Engineering
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• v.4 no.2
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• pp.50-57
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• 2003

This study was conducted to develop software which provides automatic site-specific field data acquisition, data processing, data mapping and management for precision agriculture. The developed software supports acquisition and processing of both digital and analog data streams. The architecture was object-oriented and each component in the architecture was developed as a separate class. In precision agriculture research, the laborious task of manual ground-truth data collection will be avoided using the developed software. In addition, gathering high-density data eliminates the need for interpolation of values for un-sampled areas. This software shows good potential for expansion and compatibility for variable-rate-application (VRA). The FIM (Field Information Management) computer program provides the user with an easy-to-follow process for field information management for precision agriculture.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.269-273
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• 2003

In the rice planted acreage estimation, high precise measurement and laborsaving are required by using satellite data. A method referring accurate agricultural plot vector data was used in this paper which improves the estimation accuracy of rice planted acreage compared with conventional methods. In this method, satellite data are not used for totalization, although they are used to discriminate whether the fields are rice planted or not. This paper described the result of the above method using to ASTER-VNIR data.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Journal of Distribution Science
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• v.14 no.6
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• pp.45-52
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• 2016

Purpose - This study was conducted to present a study on the perception and satisfaction with the brand agricultural products targeted at consumers who use a lot of local products. According to the data of 2011, the total number of the brand agricultural products of Korea is 5,291 with various kinds. Research design, data and methodology - The survey shows that the brand agricultural products are being used by some specific people. However, it can be a useful idea which can help the consumption of brand agricultural products to be expanded if we understand how consumers' recognitions are different between various groups. For an empirical Analysis, the response data of 110 adult patients residing in the metropolitan area were used and conducted with a factor analysis, frequency analysis in order to ensure the validity and conducted a regression analysis and correlation analysis using SPSS statistical program. Results - According to the analysis, it showed consumers with an interest in brand agricultural products are 40-50 age housewives and the middle class of about 5 million won in monthly income more than 3 million won with a college education. As for consumers' purchasing status, all the subjects said that they had experienced buying brand agricultural products and the level of satisfaction for them was very high. Relatively, consumers' satisfaction level with high income and education is high. And recognition of the brand agricultural products was found mainly goes through word of mouth. The age and income are very important factors in customers' repurchase for brand agricultural products. The result of the analysis for the influences on brand agricultural products of customer satisfaction suggests even if the recognitions for safety, quality, and value are vital factors, the recognition of quality doesn't influence on brand agricultural products statistically and significantly. It was analysed if there were any differences between recognitions by group to brand agricultural products, that is to say recognition of safety, quality and value and the result can be summarized as follows. There are all statistical significant differences depending on their age, educational background and income. In the case of 30 or 40 aged, as they got the education level of college and graduate school and earned relatively high income, most customers have positive recognition on the brand agricultural products. This implies the group which can buy and consume the brand agricultural more easily has much more positive recognition. Conclusion - The results of this study shows consumers' brand awareness and satisfaction with brand agricultural products are affected by their age and income level. The purpose of this study is to find the information that can help brand agricultural products markets to be expanded by understanding the factors which encourage consumers to behave repurchase as well as customers' various levels of recognition to the brand agricultural products. The survey says that brand agricultural products are being used by some specific people.

• International Journal of Industrial Entomology and Biomaterials
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• v.18 no.2
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• pp.63-67
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• 2009

To increase utilities as functional materials, 173 strains of silkworm genetic resources in the form of silkworm powder were evaluated for antioxidative capacity, with minilum L-100 device and ARAW-KIT (anti-radical ability of water soluble substance). Silkworm powder was prepared with freezing method from silkworms at 5th instar 3rd day larvae. All strains of silkworm powder were prepared with 80% methanol extraction. The data of pupation rate, longevity of silkmoth with origin and voltinism were used for data base of silkworm genetic resources. The weight of a silkworm larva with freezing method at 5th instar 3rd day was measured. The average of antioxidative capacity of 173 silkworm strains was 429.68 nmol. The analysis of correlation among variables was significant, showing negative correlation of the antioxidative capacity with longevity of silk moth and weight of 5th instar silkworm larva. The strains from the tropic, Europe and some other origins were comparatively high. In conclusion, short longevity and low weight of 5th instar silkworm larvae showed comparatively effective antioxidative capacity.

• Korean Journal of Agricultural Science
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• v.35 no.2
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• pp.247-262
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• 2008

The purpose of this study is to evaluate farmer's general contents on the current direct payment schemes and to derive some improvement measures, based on the survey data for the three types of direct payment schemes currently executed in Korea; Rice Farmer's Income Support, Early Retirement Aged Farmer's Support and Less Favored Area Direct Payment. In recent years we have introduced diverse direct payment schemes that are expected to have immediate effects in a short term period without enough preparation of the policy, which raises some contradiction between the agricultural policy and the original purpose of the direct payments. The result shows some important revisions should be made related the direct payment schemes, such as farmer's income stability through the improvement of unit payment and the payment length jointed with criteria of the payment, in addition to keeping up multifunctionality of agriculture and enhancing the effect of structural adjustment in agriculture.

• Journal of Climate Change Research
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• v.5 no.4
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• pp.323-329
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• 2014

Since the terrain of Korea is complex, micro- as well as meso-climate variability is extreme by locations in Korea. In particular, air temperature of agricultural fields is influenced by topographic features of the surroundings making accurate interpolation of regional meteorological data from point-measured data. This study was carried out to compare spatial interpolation methods to estimate air temperature in agricultural fields surrounded by rugged terrains in South Korea. Four spatial interpolation methods including Inverse Distance Weighting (IDW), Spline, Ordinary Kriging (with the temperature lapse rate) and Cokriging were tested to estimate monthly air temperature of unobserved stations. Monthly measured data sets (minimum and maximum air temperature) from 588 automatic weather system(AWS) locations in South Korea were used to generate the gridded air temperature surface. As the result, temperature lapse rate improved accuracy of all of interpolation methods, especially, spline showed the lowest RMSE of spatial interpolation methods in both maximum and minimum air temperature estimation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3275-3298
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• 2022

Cloud-based architectures for precision agriculture are domain-specific controlled and require remote access to process and analyze the collected data over third-party cloud computing platforms. Due to the dynamic changes in agricultural parameters and restrictions in terms of accessing cloud platforms, developing a locally controlled and real-time configured architecture is crucial for efficient water irrigation and farmers management in agricultural fields. Thus, we present a new implementation of an independent sensor-enabled architecture using variety of wireless-based sensors to capture soil moisture level, amount of supplied water, and compute the reference evapotranspiration (ETo). Both parameters of soil moisture content and ETo values was then used to manage the amount of irrigated water in a small-scale agriculture field for 356 days. We collected around 34,200 experimental data samples to evaluate the performance of the architecture under different agriculture parameters and conditions, which have significant influence on realizing real-time monitoring of agricultural fields. In a proof of concept, we provide empirical results that show that our architecture performs favorably against the cloud-based architecture, as evaluated on collected experimental data through different statistical performance models. Experimental results demonstrate that the architecture has potential practical application in a many of farming activities, including water irrigation management and agricultural condition control.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.1
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• pp.64-79
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• 2016

The purpose of this study is to find the relationship between precipitation deficit, SPI(standardized precipitation index)-12 month, agricultural reservoir water storage deficit and agricultural drought-related big data, and to evaluate the usefulness of agricultural risk management through big data. For the long term drought (from January 2014 to September 2015), each data was collected and analysed with monthly and Provincial base. The minimum SPI-12 and maximum reservoir water storage deficit compared to normal year were occurred at the same time of July 2014, and August and September 2015. The maximum frequency of big data was occurred at June and July of 2014, and March and June to September of 2015. The maximum big data was occurred 1 month advanced in 2014 and 2 months advanced in 2015 than the maximum reservoir water storage deficit. The occurrence of big data was sensitive to spring drought from March, late Jangma of June, dry Jangma of July and the rainfall deficit of September 2015. The big data was closely related with the meteorological drought and agricultural drought. Because the big data is the in situ feeling drought, it is proved as a useful indicator for agricultural risk management.