• 한국농림기상학회지
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• 제20권3호
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• pp.243-251
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• 2018

작물 생산량의 시 공간적 분석은 정책입안자와 이해관계자들에게 중요한 정보를 제공할 수 있으나, 이를 위해서는 공간적 기상자료를 처리하고 이에 맞추어 작물 모형을 구동할 수 있는 작업들이 필요하다. 이에 따라 DSSAT (Decision Support System for Agrotechnology Transfer)을 기반으로 지역내 작물 생산성 자료를 생산할 수 있는 자동화 시스템을 개발하고자 하였다. 이 시스템은 전문적인 컴퓨터 프로그래머가 아니더라도 사용가능한 R과 shell script를 기반으로 개발되었다. 먼저, 격자형 기상자료의 각 격자에 해당하는 정보를 텍스트 형식의 기상 입력자료 형식으로 변환하는 기능을 가지는 모듈을 작성하였다. 다음으로 R 패키지를 사용하여 GIS자료 처리와 병렬 처리기능이 구현된 R script을 작성하였다. 또한, 작물 모델을 자동으로 구동하는 기능을 shell script를 사용하여 구현하였다. 사례 연구로, 미국 Illinois 주에서 콩의 최대 수량을 얻을 수 있는 재배관리 조건의 공간적인 분포를 파악하고자 하였다. 개발된 도구를 통해 AgMERRA 자료로부터 Illinois 주의 1981 - 2005년 까지의 기상입력자료를 생산하였다. 해당 지역에서 1개의 CPU 코어를 사용하여 1년간의 자료를 처리하기 위해 7.38 시간이 걸렸으나, 병렬처리를 통해 16개의 CPU 코어를 사용하였을 때 처리 시간이 크게 줄어, 35분만에 처리가 가능하였다. 이렇게 생산된 기상 입력자료들을 작물 모형 자동 구동 시스템에 활용하여 해당 지역에서의 최대 수량과, 최대 수량을 가지는 성숙군 및 파종일 지도를 작성할 수 있었다. 특히, 본 연구에서 개발된 도구는 DSSAT 모델뿐만 아니라 국내에서 사용되는 다른 작물모델들에게 적용될 수 있어 공간적 작물 생산성 평가에 도움을 줄 수 있을 것으로 보인다.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.308-308
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• 2017

The low and unstable yield of soybean has been a major problem in Japan. Excess soil moisture conditions are one of the major factors to restrict soybean productivity. More than 80 % of soybean crops are cultivated in converted paddy fields which often have poor drainage. In central and eastern regions of Japan, the early vegetative growth of soybean tends to be restricted by the flooding damage because the early growth period is overlapped with the rainy season. Field observation shows that induced excess water stress in early vegetative stage reduces dry matter production by decreasing intercepted radiation by leaf and radiation use efficiency (RUE) (Bajgain et al., 2015). Therefore, it is necessary to evaluate the responses of soybean growth for excess water conditions to assess these effects on soybean productions. In this study, we aim to modify the soybean crop model (Sinclair et al., 2003) by adding the components of the restriction of leaf area development and RUE for adaptable to excess water conditions. This model was consist of five components, phenological model, leaf area development model, dry matter production model, plant nitrogen model and soil water balance model. The model structures and parameters were estimated from the data obtained from the field experiment in Tsukuba. The excess water effects on the leaf area development were modeled with consideration of decrease of blanch emergence and individual leaf expansion as a function of temperature and ground water level from pot experiments. The nitrogen fixation and nitrogen absorption from soil were assumed to be inhibited by excess water stress and the RUE was assumed to be decreasing according to the decline of leaf nitrogen concentration. The results of the modified model were better agreement with the field observations of the induced excess water stress in paddy field. By coupling the crop model and the ground water level model, it may be possible to assess the impact of excess water conditions for soybean production quantitatively.

• 한국토양비료학회지
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• 제49권5호
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• pp.420-428
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• 2016

For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery is being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of growth estimating equation for highland Kimchi cabbage using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main districts producing highland Kimchi cabbage. UAV imagery was taken in the Anbandeok ten times from early June to early September. Meanwhile, three plant growth parameters, plant height (P.H.), leaf length (L.L.) and outer leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation during growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 93% of the P.H. and L.L. with a root mean square error (RMSE) of 2.22, 1.90 cm. And $NDVI_{UAV}$ and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to $NDVI_{UAV}$ and other agro-meteorological factors were well reflected in the model.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.181-181
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• 2017

Global warming and climate change have been one of the most important problems last 2 decades. Global warming is known to cause abnormal climate and influence ecology, food production and human health. According to climate change model global warming is causing expansion of drought and increase of evaporation. Therefore, securing water in agriculture has been an important issue for crop cultivation. As potato is susceptible to drought, water shortage generally results in decrease of yield and decrease of biomass. In this research, we investigated characteristics of inorganic nutrient absorption and growth of plants grown under drought condition. Plants were sampled in sites of Cheong-ju and Gangneung, where the severity of drought stress were different. During the growth period in Gangneung, total rainfall in 2016 decreased by 50% compared with those in last 5 years average. Especially, there was almost no rain in tuber enlargement period (from mid-May to mid-June). On the other hand, the total rainfall in of Cheong-ju was is similar to those in last 5 years average. Inorganic components including K, Ca and Mg and plant growth factors such as plant length, stem length, leaf area index and plant biomass were investigated. Tuber yields in both areas were investigated at harvest. Growth period of plants was is longer in Cheong-ju than that in Gangneung. Contents of all inorganic components were higher in plants grown in Cheong-ju than in Gangneung. The results were attributed to higher production of plant biomass in Cheong-ju. Considering the results, severe drought stress conditions in Gangneung accelerated plant aging and resulted in low plant growth. Although total yield was greatly reduced under drought stress the rate of commercial yield was is not significantly different with non-drought conditions.

• 한국토양비료학회지
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• 제50권5호
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• pp.409-421
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• 2017

Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to select optimal vegetation indices and regression model for estimating of rice growth using UAV images. This study was conducted using a fixed-wing UAV (Model : Ebee) with Cannon S110 and Cannon IXUS camera during farming season in 2016 on the experiment field of National Institute of Crop Science. Before heading stage of rice, there were strong relationships between rice growth parameters (plant height, dry weight and LAI (Leaf Area Index)) and NDVI (Normalized Difference Vegetation Index) using natural exponential function ($R{\geq}0.97$). After heading stage, there were strong relationships between rice dry weight and NDVI, gNDVI (green NDVI), RVI (Ratio Vegetation Index), CI-G (Chlorophyll Index-Green) using quadratic function ($R{\leq}-0.98$). There were no apparent relationships between rice growth parameters and vegetation indices using only Red-Green-Blue band images.

• 한국토양비료학회지
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• 제50권6호
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• pp.674-683
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• 2017

The frequency and intensity of soil moisture stress associated with climate change has increasing, and the stability of field crop cultivation has decreasing. This experiment was conducted to investigate the effect of soil moisture management method on growth and yield of corn. Soil moisture was managed at the grade of WSM (wet soil moisture, 34.0~42.9%), OSM (optimum soil moisture, 27.8~34.0%), DSM (dry soil moisture, 20.3~27.8%), and ESM (extreme dry moisture, 16.6~20.3%) during V8 (8th leaf stage)-VT (tasseling stage). After VT, irrigation was limited. The treated amount of irrigation was 54.1, 47.7, 44.0 and 34.5% of total water requirement, respectively. The potential evapotranspiration during the growing period was $3.29mm\;day^{-1}$, and upward movement of soil water was estimated by the AFKAE 0.5 model in the order of ESM, DSM, OSM, and WSM. We could confirm this phenomenon from actual observations. There was no significant difference in leaf characteristics, dry matter, and primary productivity depending on the level of soil moisture, but leaf development was delayed and dry weight decreased in DSM. However, dry weight and individual productivity of DSM increased after irrigation withdrawal compared to that of OSM. In DSM, ear yield and number of kernels per ear decreased, but water use efficiency and harvest index were higher than other treatments. Therefore, it is considered that the soil moisture is concentratedly managed before the V8 period, the V8-VT period is controlled within the range of 100 to 500 kPa (20.3~27.8%), and no additional irrigation is required after the VT.

• 한국토양비료학회지
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• 제50권3호
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• pp.162-178
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• 2017

Remote sensing can be used to provide information about the monitoring of crop growth condition. Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to assess weather UAV aerial images are suitable for the monitoring of highland Kimchi cabbage. This study was conducted using a fixed-wing UAV (Model : Ebee) with Cannon S110, IXUS/ELPH camera during farming season from 2015 to 2016 in the main production area of highland Kimchi cabbage, Anbandegi, Maebongsan, and Gwinemi. The Normalized Difference Vegetation Index (NDVI) by using UAV images was stable and suitable for monitoring of Kimchi cabbage situation. There were strong relationships between UAV NDVI and the growth parameters (the plant height and leaf width) ($R^2{\geq}0.94$). The tendency of UAV NDVI according to Kimchi cabbage growth was similar in the same area for two years (2015~2016). It means that if UAV image may be collected several years, UAV images could be used for estimation of the stage of growth and situation of Kimchi cabbage cultivation.

• 한국농림기상학회지
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• 제22권3호
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• pp.176-182
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• 2020

작황조사시험은 우리나라주요 재배지점에 대해 해당 지역에서 주로 재배되는 품종들을 선정하여 장기적으로 벼의 생육과 수량을 관찰하는 연구이다. 이 연구의 목적은 기상 및 병해충의 변화에 따라 재배방법을 능동적으로 수정하여 대응하기 위한 것이다. 장기적인 자료 축적으로 이를 다양한 분야에서도 활용할 수 있도록 이 시험의 이루어지는 장소와 재배관리, 각 장소에 대응하는 기상관측소, 관찰되는 항목들에 대해 소개하였다. 각 관찰항목은 표준적인 절차에 따라 조사되어 일정한 품질이 유지되고 있으며 이 정보들은 현재 문헌으로 모두 공개되어 있으며 전산자료의 형식으로 획득할 수 있다.

• 한국작물학회지
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• 제27권1호
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• pp.84-86
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• 1982

담배의 품종과 재배형별 주당 건물중의 경시적 변화를 보다 더 정밀하게 표현할 수 있는 생장곡선방정식을 수식화하기 위하여 3가지의 생장모형을 만들어서 그 타당성을 분석한 결과는 다음과 같다. 1. 담배의 건물중에 가장 적합한 생장곡선은 C형이며 이 생장곡선은Y = A + (1-$\sqrt{4AK+1}$)/2이다. 2. 이 곡선은 이식후 35-55일의 편차가 Logistic curve보다 더 작으며 정밀하다.

• Journal of Biosystems Engineering
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• 제36권3호
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• pp.180-186
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• 2011

The objective of this research was to establish the estimation method of growth information on potato using Multi-Spectral Image Sensor (MSIS) and Global Positioning System (GPS). And growth estimation map for determining a prescription map over the entire field was generated. To determine the growth model, 10 ground-truth points of areas of $4m^2$ each were selected and investigated. The growth information included stem number, crop height and SPAD value. In addition, images information involving the ground-truth points were also taken by an unmanned helicopter, and reflectance value of Green, Red, and NIR bands were calculated with image processing. Then, growth status of potato was modeled by multi-regression analysis using these reflectance value of Green, Red, and NIR. As a result, potato growth information could be detected by analyzing Green, Red, and NIR images. Stem number, crop height and SPAD value could be estimated with $R^2$ values of 0.600, 0.657 and 0.747 respectively. The generated GIS map would describe variability of the potato growth in a whole field.