• Journal of Animal Science and Technology
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• v.45 no.2
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• pp.327-334
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• 2003

This study was carried out to make early prediction of carcass yield grade. Sixty six Hanwoo steers were measured for back fat thickness, longissimus muscle area and body weight at 18, 21 and 24 months of age by ultrasound. Carcass evaluation was done after ultrasound measurement at 24 month of age. Ultrasonic yield grade at 18, 21 and 24 month of age were predicted by regression and decision tree methods. Classifying by carcass yield grade, ultrasonic back fat thickness at 18, 21 and 24 months of age was significantly different in each carcass yield grade (p<0.05). The prediction accuracy of carcass yield grade by regression method was 78.8% at 18 months, 86.4% at 21 months and 90.9% at 24 months of age. By using the decision tree method for carcass yield grade, 78.8%, 89.4% and 89.4% of prediction accuracy were obtained at 18, 21 and 24 months of age, respectively.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1011-1027
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• 2010

In hydrologic modeling, prediction uncertainty generally stems from various uncertainty sources associated with model structure, data, and parameters, etc. This study aims to assess the parameter uncertainty effect on hydrologic prediction results. For this objective, a distributed rainfall-sediment yield-runoff model, which consists of rainfall-runoff module for simulation of surface and subsurface flows and sediment yield module based on unit stream power theory, was applied to the mesoscale mountainous area (Cheoncheon catchment; 289.9 $km^2$). For parameter uncertainty evaluation, the model was calibrated by a multi-objective optimization algorithm (MOSCEM) with two different objective functions (RMSE and HMLE) and Pareto optimal solutions of each case were then estimated. In Case I, the rainfall-runoff module was calibrated to investigate the effect of parameter uncertainty on hydrograph reproduction whereas in Case II, sediment yield module was calibrated to show the propagation of parameter uncertainty into sedigraph estimation. Additionally, in Case III, all parameters of both modules were simultaneously calibrated in order to take account of prediction uncertainty in rainfall-sediment yield-runoff modeling. The results showed that hydrograph prediction uncertainty of Case I was observed over the low-flow periods while the sedigraph of high-flow periods was sensitive to uncertainty of the sediment yield module parameters in Case II. In Case III, prediction uncertainty ranges of both hydrograph and sedigraph were larger than the other cases. Furthermore, prediction uncertainty in terms of spatial distribution of erosion and deposition drastically varied with the applied model parameters for all cases.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.157-162
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• 2016

In this paper, a yield prediction model based on relevance vector machine (RVM) and a granular computing model (quotient space theory) is presented. With a granular computing model, massive and complex meteorological data can be analyzed at different layers of different grain sizes, and new meteorological feature data sets can be formed in this way. In order to forecast the crop yield, a grey model is introduced to label the training sample data sets, which also can be used for computing the tendency yield. An RVM algorithm is introduced as the classification model for meteorological data mining. Experiments on data sets from the real world using this model show an advantage in terms of yield prediction compared with other models.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.19
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• pp.100-131
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• 1975

To obtain basic information on the improvement of naked barley production. and to clarify the relation-ships between yield or yield components and some meteorogical factors for yield prediction were the objectives of this study. The basic data used in this study were obtained from the experiments carried out for 16 years from 1958 to 1974 at the Chon-nam Provincial Office of Rural development. The simple correlation coefficients and multiple regression coefficients among the yield or yield components and meteorogical factors were calculated for the study. Days to emergence ranged from 8 to 26 days were reduced under conditions of mean minimum air temperature were high. The early emergence contributed to increasing plant height and number of tillers as well as to earlier maximum tillering and heading date. The plant height before wintering showed positive correlations with the hours of sunshine. On the other hand, plant height measured on march 1st and March 20th showed positive correlation with the amount of precipitation and negative correlation with the hours of sunshine during the wintering or regrowth stage. Kernel weights were affected by the hours of sunshine and rainfall after heading, and kernel weights were less variable when the hours of sunshine were relatively long and rainfalls in May were around 80 to 10mm. It seemed that grain yields were mostly affected by the climatic condition in March. showing the negative correlation between yield and mean air temperature, minimum air temperature during the period. In the other hand, the yield was shown to have positive correlation with hours of sunshine. Some yield prediction equations were obtained from the data of mean air temperature, mean minimum temperature and accumulated air temperature in March. Yield prediction was also possible by using multiple regression equations, which were derived from yield data and the number of spikes and plant height as observed at May 20th.

• Journal of Drive and Control
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• v.21 no.2
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• pp.36-43
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• 2024

The yield is basic and necessary information in precision agriculture that reduces input resources and enhances productivity. Yield information is important because it can be used to set up farming plans and evaluate farming results. Yield monitoring systems are commercialized in the United States and Japan but not in Korea. Therefore, such a system must be developed. This study was conducted to develop a yield monitoring system that improved performance by correcting a previously developed flow sensor using a grain tank-weighing system. An impact-plated type flow sensor was installed in a grain tank where grains are placed, and grain tank-weighing sensors were installed under the grain tank to estimate the weight of the grain inside the tank. The grain flow rate and grain weight prediction models showed high correlations, with coefficient of determinations (R2) of 0.9979 and 0.9991, respectively. A main controller of the yield monitoring system that calculated the real-time yield using a sensor output value was also developed and installed in a combine harvester. Field tests of the combine harvester yield monitoring system were conducted in a rice paddy field. The developed yield monitoring system showed high accuracy with an error of 0.13%. Therefore, the newly developed yield monitoring system can be used to predict grain weight with high accuracy.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.109-112
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• 2001

농작물의 생육 및 작황은 내적으로는 품종 자체의 고유 특성과 외적으로는 재배기술, 토양환경, 기상환경 등에 크게 영향을 받는다. 이중 온도, 일조시수 등의 기상조건은 생육과 수량 형성에 직접적인 영향을 미치게 되며 작물의 고유특성인 출수기, 수량구성요소 등도 기상환경에 따라 변이를 나타낸다.(중략)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.113-116
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• 2001

90년대 이후 다양한 첨단전산기술들이 작물모형분야에도 이용되기 시작하였는데 농업생태계 평가를 위한 국지단위 모의모형의 활용, 인공위성 등을 이용한 원격계측정보의 활용, GIS기술 등은 농업분야에서 널리 활용되고 있는 첨단 신기술의 대표적인 사례가 되고 있다. 이러한 신기술들은 농업기상정보를 생산하는 데에 이용될 뿐만 아니라 가장 중요한 환경요소로서 농업기상정보를 필요로 한다(Munakata, 1976).(중략)

• Journal of Environmental Science International
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• v.18 no.1
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• pp.1-7
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• 2009

In this study a sediment yield is compared by IUSG, IUSG with Kalman filter, tank model and tank model with Kalman filter separately. The IUSG is the distribution of sediment from an instantaneous burst of rainfall producing one unit of runoff. The IUSG, defined as a product of the sediment concentration distribution (SCD) and the instantaneous unit hydrograph (IUH), is known to depend on the characteristics of the effective rainfall. In the IUSG with Kalman filter, the state vector of the watershed sediment yield system is constituted by the IUSG. The initial values of the state vector are assumed as the average of the IUSG values and the initial sediment yield estimated from the average IUSG. A tank model consisting of three tanks was developed for prediction of sediment yield. The sediment yield of each tank was computed by multiplying the total sediment yield by the sediment yield coefficients; the yield was obtained by the product of the runoff of each tank and the sediment concentration in the tank. A tank model with Kalman filter is developed for prediction of sediment yield. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.4
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• pp.591-595
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• 2002

Three hundred thirty five progeny testing steers of Korean beef cattle were evaluated ultrasonically for back fat thickness (BFT), longissimus muscle area (LMA) and intramuscular fat (IF) before slaughter. Class measurements associated with the Korean yield grade and quality grade were also obtained. Residual standard deviation between ultrasonic estimates and carcass measurements of BFT, LMA were 1.49 mm and $0.96cm^2$. The linear correlation coefficients (p<0.01) between ultrasonic estimates and carcass measurements of BFT, LMA and IF were 0.75, 0.57 and 0.67, respectively. Results for improving predictions of yield grade by four methods-the Korean yield grade index equation, fat depth alone, regression and decision tree methods were 75.4%, 79.6%, 64.3% and 81.4%, respectively. We conclude that the decision tree method can easily predict yield grade and is also useful for increasing prediction accuracy rate.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.3
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• pp.164-168
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• 2002

Yield prediction model of rice based on the boundary line analysis of the relationships between rice yield and meteorological conditions during rice growing period was reported in the previous report (Kim et al, 2001). Using the 15-year data of the 20 locations used for the model formulation and of the 12 locations not used, the model was tested for its predictability of location to location, year to year, and variety to variety variation of rice yield. The model predicted reliably the mean yield differences among locations, the yearly yield variation in each location, and the yield variation by variety. However, the model showed relatively lower predictability for the years of cool weather injury especially in mountainous locations. In conclusion, the model using boundary line analysis could be used to predict the yield responses to meteorological conditions during rice growth period and the locational, yearly, and varietal variations of rice yield. And the predictability of the present yield prediction model might be improved by including the boundary line analysis for the other factors such as soil characteristics, fertilization levels, etc.