• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.243-251
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• 2018

The gridded simulation of crop growth, which would be useful for shareholders and policy makers, often requires specialized computation tasks for preparation of weather input data and operation of a given crop model. Here we developed an automated system to allow for crop growth simulation over a region using the DSSAT (Decision Support System for Agrotechnology Transfer) model. The system consists of modules implemented using R and shell script languages. One of the modules has a functionality to create weather input files in a plain text format for each cell. Another module written in R script was developed for GIS data processing and parallel computing. The other module that launches the crop model automatically was implemented using the shell script language. As a case study, the automated system was used to determine the maximum soybean yield for a given set of management options in Illinois state in the US. The AgMERRA dataset, which is reanalysis data for agricultural models, was used to prepare weather input files during 1981 - 2005. It took 7.38 hours to create 1,859 weather input files for one year of soybean growth simulation in Illinois using a single CPU core. In contrast, the processing time decreased considerably, e.g., 35 minutes, when 16 CPU cores were used. The automated system created a map of the maturity group and the planting date that resulted in the maximum yield in a raster data format. Our results indicated that the automated system for the DSSAT model would help spatial assessments of crop yield at a regional scale.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1397-1399
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• 2003

When and Where, What kind of agricultural products will be produced and provided for the market? It is a commercial requirement, and also an academic questions to remote sensing technology. Crop physiology analysis and growth monitoring are important elements for precision agriculture management. Remote sensing technology supplies us more selections and available spaces in this dynamic change study by producing images of different spatial, spectral and temporal resolutions. Especially, the hyperspectral remote sensing should do play a key role in crop growth investigation at national, regional and global scales. In the past five years, Chinese academy of sciences and Japan NTT-DATA have made great efforts to establish a prototype information service system to dynamically survey the vegetable planting situation in Nagano area of Japan mainly based on remote sensing data. For such concern, a flexible and light-duty flight system and some practical data processing system and some necessary background information should be rationally made together. In addition, some studies are also important, such as quick pre-processing for hyperspectral data, Multi-temporal vegetation index analysis, hyperspectral image classification in support of GIS data, etc. In this paper, several spectral data analysis models and a designed airborne platform are provided and discussed here.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.222-222
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• 2022

Improving the taste of rice in the breeding process is one of the important goals. However, it takes a lot of time and effort to select lines with good grain quality. MAS related to rice quality can help quickly and accurately select the elite lines in breeding programs. QTL qTV9, derived from Wandoaengmi 6, has been reported as a marker associated with improved glossiness of rice (Park et al., 2019). To confirm the function of QTL qTV9, 186 RILs derived from Hwayeong/Wandoaengmi6 were cultivated on ordinary planting cultivation for five years. The average DTH of Hwayeong and Wandoaengmi 6 was not significant at 99 and 97 days, respectively, but the averages of TV (toyo value) were 72.6 and 86.0, respectively. The DTH and TV of RIL vary from year to year. In 2017-2018, the average DTH was 98 days, which was significantly higher than the other three years. In 2018 and 2021, the average TV was 79.5 and 86.5, respectively, which were significantly higher than in other years. As a result of correlation analysis, DTH in the different years showed highly significant positive correlations (r = 0.71-0.92) from 0.71 to 0.92, whereas TV showed positive but weaker correlations (r = 0.42-0.71). The correlation between DTH and TV in each year was significant but weak (r = 0.25-0.64) and there was no correlation in 2017. The TV (77.6-88.7) of RILs with QTL qTV9 was significantly higher than that of RILs without qTV9 (72.6-84.9) for all five years. As a result of analyzing TV by DTH group, the TV of the lines with qTV9 in DTH groups (93-97) and (98-103) showed a significantly higher trend for all 5 years. And TV was not significant in DTH groups A, B, E, and F. This may have been influenced by factors such as insufficient populations between groups or differences in harvest timing. This study is expected to be used as data for improving the glossiness of cooked rice in breeding programs, and further study of the QTL qTV9 marker is required.

• 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.

• Journal of Bio-Environment Control
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• v.17 no.2
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• pp.83-89
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• 2008

A wind tunnel test was conducted at Protected Horticulture Experiment Station of National Horticultural Research Institute in Busan to find the aerodynamic resistance and quadratic resistance coefficient of chrysanthemum in greenhouse. The internal plants of the CFD model has been designed as a porous media because of the complexity of its physical shapes. Then the aerodynamic resistance value should be input for analyzing CFD model that crop is considered while the value varies by crops. In this study, the aerodynamic resistance value of chrysanthemum canopy was preliminarily found through wind tunnel test. The static pressure at windward increased as wind velocity and planting density increased. The static pressure at leeward decreased as wind velocity increased but was not significantly affected by planting density. The difference of static pressure between windward and leeward increased as wind velocity and planting density increased. The aerodynamic resistance value of chrysanthemum canopy was found to be 0.22 which will be used later as the input data of Fluent CFD model. When the planting distances were $9{\times}9\;cm$, $11{\times}11\;cm$, and $13{\times}13\;cm$, the quadratic resistance coefficients of porous media were found to be 2.22, 1.81, and 1.07, respectively. These values will be used later as the input data of CFX CFD model.

• 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.4
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• pp.246-251
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• 2017

Crop models have been used to predict a heading date for efficient management of fertilizer application. Recently, the ORYZA (v3) model was developed to improve the ORYZA2000 model, which has been used for simulation of rice growth in Korea. Still, little effort has been made to assess applicability of the ORYZA (v3) model to rice farms in Korea. The objective of this study was to evaluate reliability of heading dates predicted using the the ORYZA (v3) model, which would indicate applicability of the model to a decision support system for fertilizer application. Field experiments were conducted from 2015-2016 at the Rural Development Administration (RDA) to obtain rice phenology data. Shindongjin cultivar which is mid-late maturity type was grown under a conventional fertilizer management, e.g., application of fertilizer at the rate of 11 Kg N/10a. Another set of heading dates was obtained from annual reports at experiment farms operated by the National Institute of Crop Science and Agricultural Technology Centers in each province. The input files for the ORYZA (v3) model were prepared using weather and soil data collected from the Korean Meteorology Administration (KMA) and the Korean Soil Information System, respectively. Input parameters for crop management, e.g., transplanting date and planting density, were set to represent management used for the field experiment. The ORYZA (v3) model predicted heading date within 1 day for two seasons. The crop model also had a relatively small error in prediction of heading date for three ecotypes of rice cultivars at experiment farms where weather input data were obtained from a near-by weather station. Those results suggested that the ORYZA (v3) model would be useful for development of a decision support system for fertilizer application when reliable input data for weather variables become available.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.1
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• pp.42-49
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• 2008

GDD models of corn were developed in bare soil, while sweet and super sweet corns are grown under black polyethylene (P. E.) film mulch in Korea. To develop a suitable GDD model under black P. E. film mulch, a super sweet com hybrid "Cambella-90" was planted from 1 April to 30 June in 2003 at the 10-day intervals under black P. E. film mulch and in bare soil. In bare soil the best GDD model was $GDD\;=\;{\sum}[H"+L')/2\;-\;10^{\circ}C]$, where H" was daily maximum temperature but is was substituted for $30^{\circ}C$ - (daily maximum temperature - $30^{\circ}C$) when higher than $30^{\circ}C$ and L' was daily minimum temperature, but it was substituted for $10^{\circ}C$ when lower than $10^{\circ}C$. The same GDD model could be adapted for com grown under black P. E. film mulch, but base temperature was substituted for $9^{\circ}C$. To determine planting date for the scheduled harvests, accumulated GDDs were calculated using 30-year average temperature data during the growing season. Under black P. E. film mulch planting dates were determined by subtracting GDD of the hybrid, $970^{\circ}C$, from accumulated GDD of scheduled harvest dates.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.361-371
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• 2015

To understand the impact of 2015 spring drought on crop production of DPRK (Democratic People's Republic of Korea), we analyzed satellite and weather data to produce 2015 spring outlook of rice paddy field and rice growth in relation to weather anomaly. We defined anomaly of 2015 for weather and NDVI in comparison to past 5 year-average data. Weather anomaly layers for rainfall and mean temperature were calculated based on 27 weather station data. Rainfall in late April, early May, and late May in 2015 was much lower than those in average years. NDVI values as an indicator of rice growth in early June of 2015 was much lower than in 2014 and the average years. RapidEye and Radarsat-2 images were used to monitor status of rice paddy irrigation and transplanting. Due to rainfall shortage from late April to May, rice paddy irrigation was not favorable and rice planting was not progressed in large portion of paddy fields until early June near Pyongyang. Satellite images taken in late June showed rice paddy fields which were not irrigated until early June were flooded, assuming that rice was transplanted after rainfall in June. Weather and NDVI anomaly data in regular basis and timely acquired satellite data can be useful for grasping the crop and land status of DPRK, which is in high demand.

• Journal of Agricultural Extension & Community Development
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• v.25 no.1
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• pp.1-13
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• 2018

This study aims to identify factors affecting the acceptance intention of cultivating a new Italian ryegrass(IRG) variety using partial least square structural equation modeling(PLS-SEM) and find priority to maximize the acceptance intention of new IRG variety using importance-performance matrix analysis(IPMA). The data were collected on a seven-point Likert-type from 188 farm households located in Korea central region for two months. As a major result of PLS-SEM, expected effect significantly affected acceptance intention. The IPMA also showed expected effect should be considered as the most important factor to improve the acceptance intention. This study suggested the new technology distributors should scientifically prove and actively promote the effects such as increase in farm income, productivity improvement, labor saving and management efficiency caused by planting new IRG variety.