• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.44-48
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• 2016

Extreme temperature impacts on field crop are of key concern and increasingly assessed, however the studies have seldom taken into account the automatic adaptations such as shifts in planting dates, phenological dynamics and cultivars. In this present study, trial data on rice phenology, agro-meteorological hazards and yields during 1981-2009 at 120 national agro-meteorological experiment stations were used. The detailed data provide us a unique opportunity to quantify extreme temperature impacts on rice yield more precisely and in a setting with automatic adaptations.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2017.11a
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• pp.94-94
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• 2017

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.4
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• pp.108-114
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• 1980

A deterministic conceptual erosion model which simulates detachment, entrainment, transport and deposition of eroded soil particles by rainfall impact and flowing water is presented. Both upland and channel phases of sediment yield are incorporated into the erosion model. The algorithms for the soil erosion and sedimentation processes including land and crop management effects are taken from the literature and then solved using a digital computer. The erosion model is used in conjunction with the modified Kentucky Watershed Model which simulates the hydrologic characteristics from watershed data. The two models are linked together by using the appropriate computer code. Calibrations for both the watershed and erosion model parameters are made by comparing the simulated results with actual field measurements in the Four Mile Creek watershed near Traer, Iowa using 1976 and 1977 water year data. Two water years, 1970 and 1978 are used as test years for model verification. There is good agreement between the mean daily simulated and recorded streamflow and between the simulated and recorded suspended sediment load except few partial differences. The following conclusions were drawn from the results after testing the watershed and erosion model. 1. The watershed and erosion model is a deterministic lumped parameter model, and is capable of simulating the daily mean streamflow and suspended sediment load within a 20 percent error, when the correct watershed and erosion parameters are supplied. 2. It is found that soil erosion is sensitive to errors in simulation of occurrence and intensity of precipitation and of overland flow. Therefore, representative precipitation data and a watershed model which provides an accurate simulation of soil moisture and resulting overland flow are essential for the accurate simulation of soil erosion and subsequent sediment transport prediction. 3. Erroneous prediction of snowmelt in terms of time and magnitute in conjunction with The frozen ground could be the reason for the poor simulation of streamflow as well as sediment yield in the snowmelt period. More elaborate and accurate snowmelt submodels will greatly improve accuracy. 4. Poor simulation results can be attributed to deficiencies in erosion model and to errors in the observed data such as the recorded daily streamflow and the sediment concentration. 5. Crop management and tillage operations are two major factors that have a great effect on soil erosion simulation. The erosion model attempts to evaluate the impact of crop management and tillage effects on sediment production. These effects on sediment yield appear to be somewhat equivalent to the effect of overland flow. 6. Application and testing of the watershed and erosion model on watersheds in a variety of regions with different soils and meteorological characteristics may be recommended to verify its general applicability and to detact the deficiencies of the model. Futhermore, by further modification and expansion with additional data, the watershed and erosion model developed through this study can be used as a planning tool for watershed management and for solving agricultural non-point pollution problems.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.138-147
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• 2018

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.

• Journal of the Korean Applied Science and Technology
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• v.36 no.4
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• pp.1243-1252
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• 2019

The purpose of this study is to investigate the optimum conditions of biomass pretreatment with potassium hydroxide (KOH) for efficient utilization of cellulose, hemicellulose and lignin from Miscanthus. The optimization of variables was performed by response surface methodology (RSM). The variation ranges of the parameters for the RSM were potassium hydroxide 0.2~0.8 M, reaction temperature 110~190℃ and reaction time 10~90 min. The optimum conditions of alkali pretreatment from Miscanthus were determined as follows: concentration of KOH 0.47 M, reaction temperature 134℃ and reaction time 65 min. At the optimum conditions, the yield of cellulose from the solid fraction after pretreatment was predicted to be 95% by model prediction. Finally, 66.1 ± 1.1% of cellulose were obtained by verification experiment under the optimum conditions. The order contents of solid extraction were hemicellulose 26.4 ± 0.4%, lignin 3.7 ± 0.1% and ash 0.5 ± 0.04%. The yield of ethanol concentration of 96% was obtained using separated saccharification and fermentation.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.1
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• pp.87-97
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• 2004

The paper presents the results from the applications of the Water Erosion Prediction Project (WEPP) model to a single plot, and also a small watershed in the Mid Korean Peninsula which is comprised of hillslopes and channels along the water courses. Field monitoring was carried out to obtain total runoff, peak runoff and sediment yield data from research sites. For the plot of 0.63 ha in size, cultivated with com, the relative error of the simulated total runoff, peak runoff rates, and sediment yields using WEPP ranged from -16.6 to 22％, from -15.6 to 6.0％, and from 23.9 to 356.4％ compared to the observed data, respectively. The relative errors for the upland watershed of 5.1 ha ranged from -0.7 to 11.1 ％ for the total runoff, from -6.6 to 35.0 ％ for the sediment yields. The simulation results seem to justify that WEPP is applicable to the Korean dry croplands if the parameters are correctly defined. The results from WEPP applications showed that the major source areas contributing sediment yield most are downstream parts of the watershed where runoff concentrated. It was suggested that cultural practice be managed in such a way that the soil surface could be fully covered by crop during rainy season to minimize sediment yield. And also, best management practices were recommended based on WEPP simulations.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.47-55
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• 2000

In oreder to predict the productions of major crops such as rice, barely, soybean and potato in Kyongsang Puk Do as early as possible, an attempt has been made to develop some prediction model of crop yields, using the data from the Statistical Yearbooks of Agriculture, Forestry and Fisheries from 1966 through 1999. Among the various models considered, $y=\exp({\beta}_{0}+{\beta}_{1}t+{\epsilon})$ was best fit to the planted area of the crops and $y=\exp({\beta}_{0}+{\beta}_{1}t^{1/2}+{\beta}_{2}t+{\sum}^{p}_{i=1}{\beta}_{i}+_2x_i+{\epsilon})$ to the yields. The $R^{2}$ values for the planted areas were $0.9180{\sim}0.9505$, implying good prediction, while that for rice was 0.7234 and those for barley, soybean and potato were $0.8855{\sim}0.9098$, Predictions have also been made for the planted areas upto the year 2005 and yield for the year 2000.

• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.41-48
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• 2021

As the rural population continues to decline and aging, the improvement of agricultural productivity is becoming more important. Early prediction of crop quality can play an important role in improving agricultural productivity and profitability. Although many researches have been conducted recently to classify diseases and predict crop yield using CNN based deep learning and transfer learning technology, there are few studies which predict postharvest crop quality early in the planting stage. In this study, a early quality prediction model is proposed for sprout ginseng, which is drawing attention as a healthy functional foods. For this end, we took pictures of ginseng seedlings in the planting stage and cultivated them through hydroponic cultivation. After harvest, quality data were labeled by classifying the quality of ginseng sprout. With this data, we build early quality prediction models using several pre-trained CNN models through transfer learning technology. And we compare the prediction performance such as learning period and accuracy between each model. The results show more than 80% prediction accuracy in all proposed models, especially ResNet152V2 based model shows the highest accuracy. Through this study, it is expected that it will be able to contribute to production and profitability by automating the existing seedling screening works, which primarily rely on manpower.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.85-88
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• 2002

This paper presents the result of the Water Erosion Prediction Project(WEPP) watershed scale model's application for prediction of sediment yield from a watershed which is comprised of hillslopes and channels and analyses of the soil loss from hillslopes and channels with crop practice and shape. To evaluate the model's application, the model is applied to a watershed that comprised of six hillslope and one channel, and the result was a good agreement with the observed values. The soil loss from hillslope was increased as the hills lope was under fallow conditions and slope length was longer. The soil loss from the channel was increased at the downstream for the concentration of flow.

• Research in Plant Disease
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• v.17 no.3
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• pp.317-325
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• 2011

Bacterial pustule of soybean (Glycine max) caused by Xanthomonas axonopodis pv. glycines is one of the most prevalent bacterial diseases of soybean in Korea, where it causes considerable yield loss. This study was carried out to develop yield prediction model for bacterial pustule by analyzing correlation between the percentage of diseased leaf area and yield. The severe disease incidence of soybean bacterial pustule caused yield losses by 19.8% in 2006 and 16.8% in 2007, respectively. Severity of bacterial pustule greatly affected on 100 seed weight and yield, but did not on stem length, number of branches per plant, number of pods per plant, number of seeds per plant. On the other hand, correlation coefficients between diseased leaf area and yield were $-0.93^*$('06) and $-0.77^*$('07), respectively. The regression equation obtained by analyzing correlation between the percentage of diseased leaf area and yield loss in 2006 and in 2007 was y = -3.2914x + 348.19($R^2$ = 0.8603) and y = -2.9671x + 302.08($R^2$ = 0.9411), respectively. These results will be helpful in estimating losses on a field-scale and thereby predicting the production of soybean.