• 한국작물학회:학술대회논문집
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• 한국작물학회 2000년도 추계 학술대회지
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• pp.123-124
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• 2000

• Water Engineering Research
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• 제4권3호
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• pp.111-126
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• 2003

Meteorological data are often needed to evaluate the long-term effects of proposed hydrologic changes. The evaluation is frequently undertaken using deterministic mathematical models that require daily weather data as input including precipitation amount, maximum and minimum temperature, relative humidity, solar radiation and wind speed. Stochastic generation of the required weather data offers alternative to the use of observed weather records. The precipitation is modeled by a Markov Chain-exponential model. The other variables are generated by multivariate model with means and standard deviations of the variables conditioned on the wet or dry status of the day as determined by the precipitation model. Ultimately, the objective of this paper is to compare Richardson's model and the improved weather generation model in their ability to provide daily weather data for the crop model to study potential impacts of climate change on the irrigation needs and crop yield. However this paper does not refer to the improved weather generation model and the crop model. The new weather generation model improved will be introduced in the Journal of KWRA.

• 대한원격탐사학회지
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• 제40권1호
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2000년도 추계 학술대회지
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• pp.12-27
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• 2000

During the recent decades, he problem of climate variability and change has been in the forefront of scientific problems. The objective of this study was to assess the impact of climate variability on crop growth and yield. The growth duration was the main impact of climate variability on crop yield. Phyllochronterval was shortened in the global worming situations. A simple model to describe developmental traits was provided from heading data of directly seeded rice cultivars and temperature data. Daily mean development rate could be explained by the average temperature during the growth stage. Simple regression equation between daily mean development rate(x) and the average temperature(y) during the growth period as y = ax + b. It can be simply modified as x = 1/a $\ast$ (y-b). The parameters of the model could depict the thermo sensitivity of the cultivars. On the base of this model, the three doubled CO2 GCM scenarios were assessed. The average of these would suggest a decline in rice production of about 11% if we maintained the current cultivars. Future cultivar's developmental traits could be suggested by the two model parameters.

• Agricultural and Biosystems Engineering
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• 제6권1호
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• pp.1-7
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• 2005

Assessing health condition of crop in the field is one of core operation in precision fanning. A sensing system was proposed to remotely detect the crop health condition in terms of SP AD readings directly related to chlorophyll contents of crop using a multispectral camera equipped on ground-based platform. Since the image taken by a camera was sensitive to changes in ambient light intensity, it was needed to convert gray scale image data into reflectance, an index to indicate the reflection characteristics of target crop. A reference reflectance panel consisting of four pieces of sub-panels with different reflectance was developed for a dynamic calibration, by which a calibration equation was updated for every crop image captured by the camera. The system performance was evaluated in a field by investigating the relationship between com canopy reflectance and SP AD values. The validation tests revealed that the com canopy reflectance induced from Green band in the multispectral camera had the most significant correlation with SPAD values $(r^2=0.75)$ and NIR band could be used to filter out unwanted non-crop features such as soil background and empty space in a crop canopy. This research confirmed that it was technically feasible to develop a ground-based remote sensing system for assessing crop health condition.

• 한국농림기상학회지
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• 제20권4호
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• pp.277-283
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• 2018

기후변화 영향평가를 위해 작물생육모형을 폭넓게 사용하고 있지만 모형을 구동하기 위해서는 품종모수를 결정하는 것이 필수적이다. 그러나 품종모수 결정을 위한 실험은 장시간의 노력이 필요하여 대부분 작황자료 또는 지역적응 시험 자료를 많이 사용하고 있다. 그러나 밭작물의 경우 작황자료 또는 지역적응 시험을 사용하는 경우에는 포장의 관개량과 시기에 대한 자료가 없고 또한 별도의 기상관측 없이 최인접지역의 기상자료를 사용하기 때문에 문제가 발생할 수 있다. CERES-MAIZE를 이용하여 밭작물인 옥수수에 대해서 이 문제점들을 검토하였다. 출사기와 관련된 품종모수는 최대 27km 내에 기상관측 지점이 있는 경우에도 신뢰성있는 품종모수가 얻어졌다. 온도의 경우에는 지형에 따라 유효한 거리가 달라질 수 있지만 본 연구의 대상 지역에서는 품종 모수 추정에 문제가 크지는 않을 것으로 보인다. 또한 온도 이외의 요소인 강수 또는 관개량은 생물계절관련 품종모수와는 큰 영향이 없기 때문에 비교적 정확도가 높은 결과가 나온 것으로 보인다. 그러나 수량과 관련된 품종 모수 요소에서는 그렇지 못하였다. 이는 밭작물의 경우 강수량에 따라 스트레스 정도가 결정되기 때문에 관개 및 강수량 정보가 중요한데, 관개량에 대한 정보를 작황 또는 지적시험 보고서에서는 얻을 수 없기 때문이다. 더구나 강수량의 경우에 온도보다 더 가까운 위치에 기상관측소가 존재해야만 유의미한 정보를 제공할 수 있다. 그러나 시험포장에 따라 기상관측소와의 거리가 충분히 가깝지 않은 경우가 대부분이었다. 따라서, 작황 또는 지역적응 시험자료를 이용하여 옥수수의 품종모수를 결정할 때는 기상관측 지점이 최소한 20km 이내의 인접지역에서 생물계절과 관련된 모수에 대해서만 결정하는 것이 타당할 것으로 생각된다. 그 반면에 수량과 관련된 요소의 결정은 적절하지 않을 것으로 생각된다. 수량과 관련된 요소를 결정하기 위해서는 가급적 직접 기상관측망을 설치하여 해당 포장에서 관개시기와 관개량을 모두 확보한 실험한 결과를 바탕으로 얻는 것이 적절할 것으로 생각된다.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2019년도 춘계학술대회
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• pp.19-19
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• 2019

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.274-276
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• 2003

The traditional crop productivity simulations based on crop models are normally site-specific. To simulate regional crop productivity, the spatial crop model is developed in this study by integrating Geographical Information System (GIS) with Erosion Productivity Impact Calculator (EPIC) model. The integration applied a loose coupling approach. Data are exchanged using ASCII or binary data format between GIS and EPIC model without a common user interface. The spatial EPIC model is conducted to simulate the average corn and wheat productivity of 1980s in North China. The results show that the simulation accuracy of the spatial EPIC model is acceptable. The simulation accuracy can be improved by using the detailed crop management information, such as irrigation, fertilizer and tillage schedule.

• 한국농림기상학회지
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• 제22권1호
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• pp.20-25
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• 2020

최근 고해상도의 격자형 기상자료를 활용한 기후변화 또는 농업기후분석이 시도되고 있다. 모형구동을 위해서는 각 격자별로 재배 정보를 입력해야만 한다. 이러한 입력정보 중 Oryza2000 에서는 육묘기간 온도상승값이 필요하며 이는 지역별로 파종기에 따라 변화될 수밖에 없다. 그러나 격자형 자료를 사용하여 모의할 때는 이것들을 모든 격자에 대해서 변화된 값을 주는 것은 어렵다. 이 문제를 해결하기 위해 철원, 수원, 서산, 광주에 대해서 4 월 중순부터 6 월 중순까지 육묘온도 상승값을 0℃, 2℃, 5℃, 7℃ 및 9℃로 적용하고 가장 변이 발생이 적은 온도를 선택하였다. 0℃와 2℃는 4 월 중순의 낮은 온도가 발생하였을 때 큰 변이를 보여 적절하지 않았으며 7℃이상에서는 변이가 줄어들었으나 모몸살 효과에 따른 출수지연 효과가 지역별로 파종기별로 과대평가되는 경우가 발생할 수도 있다. 따라서 전반적으로는 5℃가 가장 안정적인 출수날짜를 보였으며, 격자형 기상자료를 구동할 때는 이를 활용하는 것이 좋을 것으로 판단된다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2006년도 학술발표회 논문집
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• pp.1586-1590
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• 2006

In order to calculate the actual erosion according to the universal soil loss equation (USLE) and to estimate the impact of land use on soil erosion in Saemangeum, it is important to know the C-factor. Based on the USLE crop-growth stages, the cover-management C-factors were calculated for the main crop and crop rotation systems by National Institute of Agricultural Science and Technology. Combining this result with statistical data about crop cultivation area and crop rotation systems, C-factors of each administrative district in Saemangeum watershed were calculated. The range of C-factors were between 0.28 and 0.35. High C-factor value was obtained with Gimje (C = 0.35) and small C-factor values were found in Wanju (C = 0.28) and Jeongeup (C = 0.29). With this result, calculated annual soil loss was 2,804,483 ton per year. Because of the lack of sufficient statistical data about crop rotation systems, further studies are required on collecting field survey data.