• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.1-8
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• 2022

In this study, a farm map utilization strategy for sustainable agricultural environmental resource management was derived. In addition, it is intended to present an efficient method of providing farm map-related services. As a result of the demand survey, the additional information required for the farm map includes 29% of information on crops grown on farmland, 21% of management-related information such as the owner or business entity, 17% of topographical information including slope, 15% of agricultural water information, 17% of land status information, and the addition of functions. 2% was investigated. As a result of intensive interview survey, it was found that it can be used for information on crops cultivated by agricultural businesses, actual cultivated area by township, arable land consolidation division boundary, and management of agricultural promotion zones. The farm map can be used as basic data to efficiently manage agricultural environmental resources. Since the status of support for individual farms or lots, such as soil improvement agent support and organic fertilizer support, may belong to personal information, it can be processed and provided in units required by administration or policies, such as administrative boundaries, subwatersheds, and watersheds. It can serve as a basis for executing the direct payment currently supported only by individual farms, even in a community unit that manages environmental direct payments.

• Journal of the Korean earth science society
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• v.34 no.7
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• pp.633-644
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• 2013

This study evaluates wind farm efficiency at Haengwon in Jeju Island. The actual energy production at Haengwon wind farm is compared with the estimated energy production based on Korean wind map which is developed at the National Institute of Meteorological Research/KMA. The validation of wind map at Gujwa located near the Haengwon wind farm shows that the wind speed is overestimated. The diurnal variation of wind speed shows a maximum value in the afternoon due to the effect of sea-land breeze. The ratio of the actual energy production at Haengwon wind farm and the estimated energy production based on the Korean wind map is 24.8%, while the distribution of energy frequency is similar each other. The difference of energy production is caused by mechanical error of the turbine and the overestimation of the simulated wind map. This study will contribute to the repowering of turbines for improving the efficiency of wind farm in the future.

• Journal of Korean Society of Rural Planning
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• v.23 no.1
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• pp.21-29
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• 2017

The purpose of this study is to analyze the accuracy of cultivated crop database in agricultural farm business using UAV(Unmanned Aerial Vehicle) and field survey over Daesso-myeon, Umsung-gun, Chungbuk. When comparing with agricultural farm business and cadastral maps, Daeso-myeon crop field shows 29.8%(2,030 parcels out of 6,822 parcels) is either mismatched or missing. It covers almost 19.3%($3.4km^2$ of $17.6km^2$) of total farmland. In order to solve these problems, it is necessary to prepare a multifaceted plan including cadastral map. Comparative analysis of the cultivated crop registered in the agricultural farm business and the field survey agreed only in 3,622 parcels in total 6,822 parcels whereas 3200 parcels disagree. Among these disagreed parcels 2,030(29.8%) have been confirmed as unregistered farm business entity. Accuracy of cultivated crop registered in agricultural farm business agreed in 75.6% cases. Especially the paddy field registration is more accurate that other crops. These discrepancies can lead to false payment in agricultural farm business. For exploration and analysis of regional resources, UAV images can be used together with farm business management database and cadastral map to get a clearer grasp over on-site resources and conditions.

• Journal of the Korean Solar Energy Society
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• v.35 no.2
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• pp.1-10
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• 2015

Promising onshore wind farm sites in Gangwon province of Korea were investigated in this study. Gangwon province was divided into twenty five simulation regions and a commercial program based on Reynolds averaged Navier-Stokes equation was used to find out wind resource maps of the regions. The national wind atlas with a period 2007-2009 developed by Korea institute of energy research was used as climatologies. The wind resource maps were combined to construct a wind resource map of Gangwon province with a horizontal spatial resolution of 100m. In addition to the wind resource, national environmental zoning map, distance from substation, residence and automobile road, Beakdudaegan mountain range, terrain slope, airport and military reservation district were considered to find out promising wind farm sites. A commercial wind farm design program was used to find out developable wind farm capacities in promising wind farm site with and without excluding environmental protection regions. The total wind farm capacities with and without excluding the protection regions were estimated to be 46MW and 598MW, respectively, when a 2MW commercial wind turbine was employed.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.1
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• pp.49-56
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• 2002

The Z-map is a special farm of discrete non-parametric representation in which the height values at grid points on the xy-plane are stored as a 2D array z[ij]. While the z-map is the simplest farm of representing sculptured surfaces and is the most versatile scheme for modeling non-parametric objects, its practical application in industry (eg, tool-path generation) has aroused much controversy over its weaknesses, namely its inaccuracy, singularity (eg, vertical wall), and some excessive storage needs. Much research or the application of the z-map can be found in various articles, however, research on the systematic analysis of sculptured surface shape representation via the z-map model is rather rare. Presented in this paper are the following: shape modeling power of the simple z-map model, exact (within tolerance) z-map representation of sculptured surfaces which have some feature-shapes such as vertical-walls and real sharp-edges by adopting some complementary z-map models, and some application examples.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.254-261
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• 2000

Soil chemical properties, relief of field surface, SPAD values and grain yield were investigated in a 0.5ha paddy field in 1999 to obtain basic field information for precision agriculture. Descriptive statistics of field information showed that the coefficient of variation ranged from 1.63% to 38.7%. Field information showed a high spatial dependence for within paddy field. The ranges of spatial dependence were from 15m to 60m, respectively. Kriged maps enable the visualization and comparison the spatial variability of field information. The causes of spatial variability of the field information could be explained rationally by a field management map. Grain yield was negatively correlated with pH, relief values, whereas, was positively correlated with total C, total N, C/N ratio, mineralizable N, available P and exchangeable K, Ca at the significant level of 1 %.

• Journal of the Korean Solar Energy Society
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• v.36 no.4
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• pp.21-29
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• 2016

Commercial wind farm design tools and the national wind map are used to determine the implementation potential of offshore wind power in Korea in this study. For this, the territorial waters of Korea were divided into nine analysis regions and a commercial CFD code was used to obtain wind resource maps at 100m A.S.L. which is the hub height of a 5MW wind turbine used in this study. With the wind resource obtained, factors including water depth, distance from substations, minimum and maximum capacity of a wind farm, distance between turbines and wind farms were considered to determine wind power potential. Also, the conservation areas, military zones, ports, fishing grounds, etc. were considered and excluded. As the result, a total capacity of 6,720 MW was found to be the implementation potential and this corresponds to $3.38MW/km^2$ in API. Also if the distance from the substation is not considered, the potential increased to be 10,040 MW. This offshore wind farm potential is considered enough to satisfy the target of wind farm capacities in the 7th national plan for electricity demand and supply.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.534-535
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• 2009

The objective of this research aims preliminary assessments of the proposed plans of offshore wind farm development based on the recently established national offshore wind map and suitability assessment system of offshore wind farm. Incheon Mueodo, Busan Dadaepo-Gadukdo, Sinangun Haeodo have been assessed considering geographic constraints such as water depth, offshore distance, national park, grid connection, and meteorological constraint such as wind power density and wind direction. According to the assessment, Mueodo plan has a weak point in grid connection and several geographic limitations are involved in Haeodo plan while Dadaepo-Gadukdo seems the most possible plan among the review cases. Because of limited assessment in this research, more detail and further consideration are necessary to make a decision of a feasibility project at proposed sites.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.6
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• pp.15-22
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• 2023

In this study, We conduct a comparative study of deep learning-based classification of agricultural field attributes using Tagged Image File (TIF) and Enhanced Compression Wavelet (ECW) images. The goal is to interpret and classify the attributes of agricultural fields by analyzing the differences between these two image formats. "FarmMap," initiated by the Ministry of Agriculture, Food and Rural Affairs in 2014, serves as the first digital map of agricultural land in South Korea. It comprises attributes such as paddy, field, orchard, agricultural facility and ginseng cultivation areas. For the purpose of comparing deep learning-based agricultural attribute classification, we consider the location and class information of objects, as well as the attribute information of FarmMap. We utilize the ResNet-50 instance segmentation model, which is suitable for this task, to conduct simulated experiments. The comparison of agricultural attribute classification between the two images is measured in terms of accuracy. The experimental results indicate that the accuracy of TIF images is 90.44%, while that of ECW images is 91.72%. The ECW image model demonstrates approximately 1.28% higher accuracy. However, statistical validation, specifically Wilcoxon rank-sum tests, did not reveal a significant difference in accuracy between the two images.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.77-82
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• 2023

The Ministry of Agriculture, Food and Rural Affairs established the FarmMap, an digital map of agricultural land. In this study, using deep learning, we suggest the application of farm map reading to farmland such as paddy fields, fields, ginseng, fruit trees, facilities, and uncultivated land. The farm map is used as spatial information for planting status and drone operation by digitizing agricultural land in the real world using aerial and satellite images. A reading manual has been prepared and updated every year by demarcating the boundaries of agricultural land and reading the attributes. Human reading of agricultural land differs depending on reading ability and experience, and reading errors are difficult to verify in reality because of budget limitations. The farmmap has location information and class information of the corresponding object in the image of 5 types of farmland properties, so the suitable AI technique was tested with ResNet50, an instance segmentation model. The results of attribute reading of agricultural land using deep learning and attribute reading by humans were compared. If technology is developed by focusing on attribute reading that shows different results in the future, it is expected that it will play a big role in reducing attribute errors and improving the accuracy of digital map of agricultural land.