• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.79-89
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• 2022

Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, various process-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending on the model parameters and structures that causes the different model output for the same modeling condition. In this study, the three land surface models (Noah Land Surface Model, Soil Water Atmosphere Plant, Community Land Model) were used to compare the model performance (soil moisture prediction) and develop the multi-model simulation. At first, the genetic algorithm was used to estimate the optimal soil parameters for each model, and the parameters were used to predict soil moisture in the study area. Then, we used the multi-model approach based on Bayesian model averaging (BMA). The results derived from this approach showed a better match to the measurements than the results from the original single land surface model. In addition, identifying the strengths and weaknesses of the single model and utilizing multi-model methods can help to increase the accuracy of soil moisture prediction.

• Journal of Crop Science and Biotechnology
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• v.11 no.2
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• pp.141-150
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• 2008

The response of grain yield(GY) and milled-rice protein content(PC) to crop growth status and nitrogen(N) rates at panicle initiation stage(PIS) is critical information for prescribing topdress N rate at PIS(Npi) for target GY and PC. Three split-split-plot experiments including various N treatments and rice cultivars were conducted in Experimental Farm, Seoul National University, Korea in 2003-2005. Shoot N density(SND, g N in shoot $m^{-2}$) and canopy reflectance were measured before N application at PIS, and GY, PC, and SND were measured at harvest. Data from the first two years(2003-2004) were used for calibrating the predictive models for GY, PC, and SND accumulated from PIS to harvest using SND at PIS and Npi by multiple stepwise regression. After that the calibrated models were used for calculating N requirement at PIS for each of nine plots based on the target PC of 6.8% and the values of SND at PIS that was estimated by canopy reflectance method in the 2005 experiment. The result showed that SND at PIS in combination with Npi were successful to predict GY, PC, and SND from PIS to harvest in the calibration dataset with the coefficients of determination ($R^2$) of 0.87, 0.73, and 0.82 and the relative errors in prediction(REP, %) of 5.5, 4.3, and 21.1%, respectively. In general, the calibrated model equations showed a little lower performance in calculating GY, PC, and SND in the validation dataset(data from 2005) but REP ranging from 3.3% for PC and 13.9% for SND accumulated from PIS to harvest was acceptable. Nitrogen rate prescription treatment(PRT) for the target PC of 6.8% reduced the coefficient of variation in PC from 4.6% in the fixed rate treatment(FRT, 3.6g N $m^{-2}$) to 2.4% in PRT and the average PC of PRT was 6.78%, being very close to the target PC of 6.8%. In addition, PRT increased GY by 42.1 $gm^{-2}$ while Npi increased by 0.63 $gm^{-2}$ compared to the FRT, resulting in high agronomic N-use efficiency of 68.8 kg grain from additional kg N. The high agronomic N-use efficiency might have resulted from the higher response of grain yield to the applied N in the prescribed N rate treatment because N rate was prescribed based on the crop growth and N status of each plot.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.242-249
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• 2006

Potential impacts of the future climate change on crop production can be inferred by crop simulations at a landscape scale, if the climate data may be provided at appropriate spatial scales. Northern Gyunggi Province is one of the few prospective regions in South Korea for growing quality soybeans. Any geographical shift of production areas under the changing climate may influence the current land planning policy in this region. A soybean growth simulation was performed at 342 land units in northern Gyunggi province to test the potential geographical shift of the current production areas for quality soybeans in the near future (form 2011 to 2100). The land units for soybean cultivation were selected by the land use, the soil characteristics, and the minimum arable land area. Daily maximum and minimum temperature, precipitation, the number of rain days and solar radiation were extracted for each land unit from the future digital climate models (DCM, 2011-2040, 2041-2070, 2071-2100). Daily weather data for 30 years were randomly generated for each land unit for each normal year by using a well-known statistical method. They were used to run CROPGRO-Soybean model to simulate the growth, phonology, and yields of 3 cultivars representing different maturity groups grown at 342 land units. According to the model calculations, the warming trend in this region will accelerate the flowering and physiological maturity of all cultivars, resulting in a 7 to 9 days reduction in overall growing season and a 1 to 15% reduction in grain yield of early to medium maturity cultivars. There was a slight increase in grain yield of the late maturing cultivar under the projected climate by 2070, but a decreasing tend was dominant by the year 2100.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.509-518
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• 2018

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.1
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• pp.22-32
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• 1997

This experiment was conducted to obtain information for the cultivation of Korean valerian(Valeriana lauriei var. dasycarpa Hara) which will be useful for medicinal and aromatic resources. The effect of different temperature conditions, light intensities and soil water conditions on growth, yield and component of essential oil of V. fauriei were measured at the Dankook University, Cheonan, and a study on the shading treatment was at Umsung, Chungchongbukdo, and Jinbu, Kangwondo, in 1995. V. laudei was planted at five different temperature conditions, 10, 15, 20, 25 and 3$0^{\circ}C$, eight light intensity conditions, 1, 000, 2, 500, 5, 000, 20, 000, 30, 000, 40, 000, 50, 000 and 60, 000lux, six soil water contents, 30, 45, 55, 70, 80 and 90％ of the saturated soil, during growth stage. Shading treatment was three conditions, 0, 25 and 50％, during the daytime in field conditions. Photosynthesis had a highly significant relationship with temperature conditions in a quadratic regression model, from which the temperature for the plant growth was estimated to be 17.7$^{\circ}C$. A highly significant quadratic regression was noted between temperature and leaf width or root weight of V. fauriei. It was estimated from the regression equation that the optimum temperature for root growth was 20.3$^{\circ}C$. The content of essential oil and extract rate of root was the highest in the 15~2$0^{\circ}C$. Photosynthesis also was significantly affected by light intensity in a quadratic regression model, from which the optimum light intensity for the growth was estimated to be 40, 000lux. Root yield was more produced in Jinbu than that of in Umsung. The root yield was increased by the shading treatment in Umsung, whereas it was decreased by the shading treatment in Jinbu. The content of essential oil was not affected by the shading treatment of plants during the cultivation, while the compositions of components of essential oil were related to the growing locations. As soil water content was higher, the growth and content of root extract were increased. The optimum soil moisture for the growth of V. fauriei was 80~90％ of the saturated soil. In summary, the results indicated that the growth, yield and component of essential oil in V. fauriei were affected by environmental factors as well as soil moisture.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.333-339
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• 2015

A thermal time-based phenology model of Kimchi cabbage was developed by using the field observed growth and temperature data for the purpose of accurately predicting heading and harvest dates among diverse cropping systems. In this model the lifecycle of Kimchi cabbage was separated into the growth stage and the heading stage, while the growth amount of each stage was calculated by optimal mathematical functions describing the response curves for different temperature regimes. The parameter for individual functions were derived from the 2012-2014 crop status report collected from seven farms with different cropping systems located in major Kimchi cabbage production area of South Korea (i.e., alpine Gangwon Province for the summer cultivation and coastal plains in Jeonnam Province for the autumn cultivation). For the model validation, we used an independent data set consisting of local temperature data restored by a geospatial correction scheme and observed harvest dates from 17 farms. The results showed that the root mean square error averaged across the location and time period (2012-2014) was 5.3 days for the harvest date. This model is expected to enhance the utilization of the Korea Meteorological Administration's daily temperature data in issuing agrometeorological forecasts for developmental stages of Kimchi cabbage grown widely in South Korea.

• Horticultural Science & Technology
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• v.32 no.2
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• pp.165-170
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• 2014

The objective of this study was to make growth and yield models of sowthistle (Ixeris dentata Nakai) by using an expolinear functional equation in a closed-type plant production system. The growth and yield of hydroponically-grown sowthistle were investigated under four different planting distances ($15{\times}10$, $15{\times}15$, $15{\times}20$, and $15{\times}25$ cm). Shoot dry weights per plant was the highest at $15{\times}25$ cm, but was the lowest at $15{\times}10$ cm. Shoot dry weights per area was the highest at $15{\times}15$ cm, but was the lowest at $15{\times}25$ cm. The optimum planting density and planting distance for yield of sowthistle were 44 plants/$m^2$ and $15{\times}15$ cm, respectively. Shoot dry weights per plant and per area were showed as an expolinear type functional equation. A linear relationship between shoot dry and fresh weights was observed to be linear regardless of the planting distance. Crop growth rate, relative growth rate and lost time in an expolinear functional equation showed quadratic function form. Radiation use efficiency of sowthistle was $4.3-6.1g{\cdot}MJ^{-1}$. The measured and estimated shoot dry weights showed a good agreement using days after transplanting as input data. It is concluded that the expolinear growth model can be a useful tool for quantifying the growth and yield of sowthistle in a closed-type plant production system.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.366-376
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• 2023

This study was conducted to develop a model for predicting the growth of kimchi cabbage using image data and environmental data. Kimchi cabbages of the 'Cheongmyeong Gaual' variety were planted three times on July 11th, July 19th, and July 27th at a test field located at Pyeongchang-gun, Gangwon-do (37°37' N 128°32' E, 510 elevation), and data on growth, images, and environmental conditions were collected until September 12th. To select key factors for the kimchi cabbage growth prediction model, a correlation analysis was conducted using the collected growth data and meteorological data. The correlation coefficient between fresh weight and growth degree days (GDD) and between fresh weight and integrated solar radiation showed a high correlation coefficient of 0.88. Additionally, fresh weight had significant correlations with height and leaf area of kimchi cabbages, with correlation coefficients of 0.78 and 0.79, respectively. Canopy coverage was selected from the image data and GDD was selected from the environmental data based on references from previous researches. A prediction model for kimchi cabbage of biomass, leaf count, and leaf area was developed by combining GDD, canopy coverage and growth data. Single-factor models, including quadratic, sigmoid, and logistic models, were created and the sigmoid prediction model showed the best explanatory power according to the evaluation results. Developing a multi-factor growth prediction model by combining GDD and canopy coverage resulted in improved determination coefficients of 0.9, 0.95, and 0.89 for biomass, leaf count, and leaf area, respectively, compared to single-factor prediction models. To validate the developed model, validation was conducted and the determination coefficient between measured and predicted fresh weight was 0.91, with an RMSE of 134.2 g, indicating high prediction accuracy. In the past, kimchi cabbage growth prediction was often based on meteorological or image data, which resulted in low predictive accuracy due to the inability to reflect on-site conditions or the heading up of kimchi cabbage. Combining these two prediction methods is expected to enhance the accuracy of crop yield predictions by compensating for the weaknesses of each observation method.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.83-95
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• 2020

Being a dense and complex task of precisely detecting the weeds in practical crop field environment, previous approaches lack in terms of speed of processing image frames with accuracy. Although much of the attention has been given to classify the plants diseases but detecting crop weed issue remained in limelight. Previous approaches report to use fast algorithms but inference time is not even closer to real time, making them impractical solutions to be used in uncontrolled conditions. Therefore, we propose a detection model for the complex rice weed detection task. Experimental results show that inference time in our approach is reduced with a significant margin in weed detection task, making it practically deployable application in real conditions. The samples are collected at two different growth stages of rice and annotated manually

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.1
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• pp.8-14
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• 2008

Ammonia ($NH_3$) volatilization from a silty clay loam paddy soil applied with non, straight urea, and coated urea, respectively, under transplanting in Milyang, Korea from 2002 and 2003 was simulated by a Water and Nitrogen Management Model (WNMM). Based on the data from the in-situ measurements, $NH_3$ volatilization during the rice growth was 6.04% and 1.46% of the applied nitrogen (N) from straight urea and coated urea, respectively. The bulk aerodynamic approach in WNMM satisfactorily predicted the difference in $NH_3$ loss during the given rice growing seasons from the two urea fertilizers. $R^2$ for the correlation between the predicted and observed NH3 loss during the calibration year (2002) was 0.53 less than 0.68 of the application year (2003). This difference could be due to the weather condition such as heavy rainfall and temperature during the calibration year.