• Title/Summary/Keyword: agricultural classification system

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A Study on Rural Planning Methodology(II) Using Spatial Analysis Method of GIS - (농촌지역 토지이용계획 기법 연구(II) -GIS의 공간분석기법 이용-)

  • 정하우;박병태
    • Journal of Korean Society of Rural Planning
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    • v.1 no.2
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    • pp.43-52
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    • 1995
  • This study is to establish a planning methodology for rural area development with land suitability classification. Land suitability classification was carried out by introducing Geographic Information System. The planning methodology was applied to Sunheung district located in Youngpoong county, Kyongbuk Province, Korea. Land suitability classification by the GIS showed that only 29 % of present agricultural land were higher than class 2 and 71 % were in bad condition for agricultural land. Especially, 22.2 % of agricultural land were under class 5 as the lowest level and 265.2 ha of forest were possible to develop as an agricultural land. It was proved that GIS may be a powerful tool in rural planning process. In addition, it is thought that GIS can be applied to the fields of agricultural land management system in many ways.

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Development of a mid-term preceding observation model for radish (무의 중기 선행관측모형 개발)

  • Cho, Jae-Hwan;Lee, Han-Sung
    • Korean Journal of Agricultural Science
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    • v.38 no.3
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    • pp.571-581
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    • 2011
  • This study develops a mid-term preceding observation model of radish to complement an existing short-term agricultural observation model. The first purpose of the study is to extend a three seasonal classification(spring, summer, fall) of fruit-vegetables to a four seasonal classification that involves the winter additionally. This allows us to verify the reason for demand and supply unbalance and unstable price of radish. The second purpose is to construct a mid-term preceding observation model that would be used to forecast planted areas, output, monthly shipment and price. To achieve these purposes, several multiple regression models are estimated. A system is consisted of a planted areas equation, a yield equation, monthly shipment distribution equation, and monthly price equation. To calculate output an auxiliary equation is involved in the system and the consumer price index etc are considered as exogenous variables.

Discriminant analysis of grain flours for rice paper using fluorescence hyperspectral imaging system and chemometric methods

  • Seo, Youngwook;Lee, Ahyeong;Kim, Bal-Geum;Lim, Jongguk
    • Korean Journal of Agricultural Science
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    • v.47 no.3
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    • pp.633-644
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    • 2020
  • Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1st and 2nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

A Study on the Classification System of the Target Elements for Rural Village Remodelling System -A Study on Deducing Target Elements Based on Empirical Field Survey- (농촌마을 리모델링 대상요소 항목체계 구축에 관한 연구 -현장실증검증을 통한 도출방법을 중심으로-)

  • Kim, Hye-Ran;Lim, Chang-Su;Kim, Eun-Ja;Kim, Sang-Bum;Choi, Jin-Ah
    • Journal of Korean Society of Rural Planning
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    • v.18 no.3
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    • pp.111-122
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    • 2012
  • This study is to evaluate the classification system of rural-villages remodeling components which is provided for improving the quality of life for rural community by improvement of settlement environment. To achieve this, rural-villages remodeling components are classified according to the spatial structure of rural area through analysis of literature, then we have examined the applicability through case studies after modification work which is based on experts's discussion and rearrangement by pilot investigation of researcher. In the classification system of rural-villages remodeling classified productivity area, residential area, community area in first group and this classification is divided into 6 space to production, 4 space to residence, 5 space to community in second group by literature search, pilot investigation of researcher and field survey. The subject elements surveyed a total of 123 through the literature search, additionally, 1 element at a space to production and space to community in field survey for types in zoning cases. As a result, categories and items are decided that it is included 125 target elements.

Standardized Agricultural Land Use Classification Scheme at Various Spatial Resolution of Satellite Images

  • Hong Seong Min;Jung In Kyun;Park Geun Ae;Kim Seong Joon
    • Journal of The Korean Society of Agricultural Engineers
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    • v.46 no.7
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    • pp.15-21
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    • 2004
  • This study is to present a standardized agricultural land use classification scheme at various spatial resolution (from 1 m to 30 m) of satellite images including Landsat TM, KOMPSAT-1 EOC, ASTER VNIR and IKONOS panchromatic (PAN) and multi-spectral (MS) images. The satellite images were interpreted especially for identifying agricultural land use, crop types, agricultural facilities and structures of 18 items. It was found that there is a threshold spatial resolution between 4 m and 6.6 m to identify the full items. Thus it is suggested that IKONOS fusion image (MS enhanced by PAN) is required to produce land use map for agricultural purpose.

Energy Efficiency Classification of Agricultural Tractors in Korea

  • Shin, Chang-Seop;Kim, Kyeong-Uk;Kim, Kwan-Woo
    • Journal of Biosystems Engineering
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    • v.37 no.4
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    • pp.215-224
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    • 2012
  • Purpose: This study was conducted to classify the energy efficiency of 131 tractor models tested during from 2006 to 2010 in Korea. Methods: Four sub-indexes were developed using the fuel consumptions at 60% and 90% of rated speed with partial loads and at pull speeds of 3.0 km/h and 7.5 km/h with maximum drawbar pull. Weighting factors of the sub-indexes were also considered to reflect the characteristics of tractor's actual working hours in Korea. Four sub-indexes were integrated into a classification index. Using the developed classification index, a five-classification system was made on the basis of normal distribution of tractors over the classification range. Percentage of $1^{st}$ grade interval was expected to be close to 15%, $2^{nd}$ grade 20%, $3^{rd}$ grade 30%, $4^{th}$ grade 20%, $5^{th}$ grade 15%. Results: Number of $1^{st}$ grade was 21, $2^{nd}$ grade 23, $3^{rd}$ grade 39, $4^{th}$ grade 33, $5^{th}$ grade 15 among 131 models. Conclusions: Classification index was developed by integrating four sub-indexes. By the classification method using developed index, distribution of classified tractors was acceptable for practical application.

Classification system of fruits by color image processing (칼라 영상처리에 의한 과일분류시스템)

  • 최연호;부기동;구본호
    • Journal of Korea Society of Industrial Information Systems
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    • v.5 no.3
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    • pp.65-70
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    • 2000
  • In general, the quality of agricultural products is determined by direct measurement of a weight or a magnitude, and it is determined by indirect or non-destructive method. In this paper, using color image processing, the algorithm to determine its quality and grading is presented. And the algorithm is applied to real-time citrus classifier. In the system, the size and color of orange are measured by not the sight of human but the digital image processing. The citrus classification system has the real-time maximum classification capacity of six quantify per one second. The system can be applied to controller design for the quality classification of agricultural products.

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A Study on the Classification Schemes of Internet Resources for Agriculture (농학분야 인터넷자원의 분류체계에 관한 연구)

  • 김정현;문지현
    • Journal of Korean Library and Information Science Society
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    • v.33 no.3
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    • pp.393-413
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    • 2002
  • This study is to suggest a classification system to classify the agricultural information resources on the internet. In the first part, I analyzes KDC's class 520(agriculture science). The second part compares the agricultural classes of Yahoo! Korea with those of Empas search engine. The third part compares the classes of AFFIS with Agri_Directory. Based on the comparative analysis, it proposes a classificatory system for the agricultural information resources on the internet.

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Standardizing Agriculture-related Land Cover Classification Scheme using IKONOS Satellite Imagery (IKONOS 영상자료를 이용한 농업지역 토지피복 분류기준 설정)

  • Hong Seong-Min;Jung In-Kyun;Kim Seong-Joon
    • Korean Journal of Remote Sensing
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    • v.20 no.4
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    • pp.253-259
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    • 2004
  • The purpose of this study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including Landsat + ETM, KOMPSAT-1 EOC, ASTER VNIR, and IKONOS panchromatic and multi-spectral images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by National Geographic Information based on aerial photograph and Ministry of Environment based on satellite remote sensing data. As a result, high-resolution agricultural land cover map from IKONOS imageries was made out. The classification result by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.

High-resolution Land Cover Mapping of Rural Area Using IKONOS Imagery (IKONOS 영상을 이용한 고해상도 토지피복도 작성)

  • Hong, Seong Min;Jung, In Kyun;Kim, Seong Joon
    • Proceedings of the Korea Water Resources Association Conference
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    • 2004.05b
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    • pp.1271-1275
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    • 2004
  • The purpose of this study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including Landsat +ETM, KOMPSAT-1 EOC, ASTER VNIR, and IKONOS panchromatic and multi-spectral images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by Ministry of Construction & Transportation based on NGIS (National Geographic Information System) and Ministry of Environment based on satellite remote sensing data. As a result, high-resolution agricultural land cover map from IKONOS imageries was made out. The results by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.

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